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Hwang EJ, Park JE, Song KD, Yang DH, Kim KW, Lee JG, Yoon JH, Han K, Kim DH, Kim H, Park CM. 2023 Survey on User Experience of Artificial Intelligence Software in Radiology by the Korean Society of Radiology. Korean J Radiol 2024; 25:613-622. [PMID: 38942455 PMCID: PMC11214921 DOI: 10.3348/kjr.2023.1246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 04/24/2024] [Accepted: 04/26/2024] [Indexed: 06/30/2024] Open
Abstract
OBJECTIVE In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR). MATERIALS AND METHODS An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs. RESULTS Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs. CONCLUSION The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.
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Affiliation(s)
- Eui Jin Hwang
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Kyoung Doo Song
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Dong Hyun Yang
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Kyung Won Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - June-Goo Lee
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jung Hyun Yoon
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Kyunghwa Han
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dong Hyun Kim
- Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hwiyoung Kim
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Chang Min Park
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
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Cè M, Ibba S, Cellina M, Tancredi C, Fantesini A, Fazzini D, Fortunati A, Perazzo C, Presta R, Montanari R, Forzenigo L, Carrafiello G, Papa S, Alì M. Radiologists' perceptions on AI integration: An in-depth survey study. Eur J Radiol 2024; 177:111590. [PMID: 38959557 DOI: 10.1016/j.ejrad.2024.111590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/05/2024]
Abstract
PURPOSE To assess the perceptions and attitudes of radiologists toward the adoption of artificial intelligence (AI) in clinical practice. METHODS A survey was conducted among members of the SIRM Lombardy. Radiologists' attitudes were assessed comprehensively, covering satisfaction with AI-based tools, propensity for innovation, and optimism for the future. The questionnaire consisted of two sections: the first gathered demographic and professional information using categorical responses, while the second evaluated radiologists' attitudes toward AI through Likert-type responses ranging from 1 to 5 (with 1 representing extremely negative attitudes, 3 indicating a neutral stance, and 5 reflecting extremely positive attitudes). Questionnaire refinement involved an iterative process with expert panels and a pilot phase to enhance consistency and eliminate redundancy. Exploratory data analysis employed descriptive statistics and visual assessment of Likert plots, supported by non-parametric tests for subgroup comparisons for a thorough analysis of specific emerging patterns. RESULTS The survey yielded 232 valid responses. The findings reveal a generally optimistic outlook on AI adoption, especially among young radiologist (<30) and seasoned professionals (>60, p<0.01). However, while 36.2 % (84 out 232) of subjects reported daily use of AI-based tools, only a third considered their contribution decisive (30 %, 25 out of 84). AI literacy varied, with a notable proportion feeling inadequately informed (36 %, 84 out of 232), particularly among younger radiologists (46 %, p < 0.01). Positive attitudes towards the potential of AI to improve detection, characterization of anomalies and reduce workload (positive answers > 80 %) and were consistent across subgroups. Radiologists' opinions were more skeptical about the role of AI in enhancing decision-making processes, including the choice of further investigation, and in personalized medicine in general. Overall, respondents recognized AI's significant impact on the radiology profession, viewing it as an opportunity (61 %, 141 out of 232) rather than a threat (18 %, 42 out of 232), with a majority expressing belief in AI's relevance to future radiologists' career choices (60 %, 139 out of 232). However, there were some concerns, particularly among breast radiologists (20 of 232 responders), regarding the potential impact of AI on the profession. Eighty-four percent of the respondents consider the final assessment by the radiologist still to be essential. CONCLUSION Our results indicate an overall positive attitude towards the adoption of AI in radiology, though this is moderated by concerns regarding training and practical efficacy. Addressing AI literacy gaps, especially among younger radiologists, is essential. Furthermore, proactively adapting to technological advancements is crucial to fully leverage AI's potential benefits. Despite the generally positive outlook among radiologists, there remains significant work to be done to enhance the integration and widespread use of AI tools in clinical practice.
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Affiliation(s)
- Maurizio Cè
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy
| | - Simona Ibba
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Michaela Cellina
- Radiology Department, ASST Fatebenefratelli Sacco, Piazza Principessa Clotilde 3, 20121 Milan, Italy.
| | - Chiara Tancredi
- University Suor Orsola Benincasa, corso Vittorio Emanuele 292, 80135 Naples, Italy.
| | | | - Deborah Fazzini
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Alice Fortunati
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy.
| | - Chiara Perazzo
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy.
| | - Roberta Presta
- University Suor Orsola Benincasa, corso Vittorio Emanuele 292, 80135 Naples, Italy.
| | - Roberto Montanari
- University Suor Orsola Benincasa, corso Vittorio Emanuele 292, 80135 Naples, Italy; RE:LAB s.r.l., Via Tamburini, 5, 42122 Reggio Emilia, Italy.
| | - Laura Forzenigo
- Radiology Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School of Radiodiagnostic, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy; Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milan, Italy
| | - Sergio Papa
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy.
| | - Marco Alì
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, 20147 Milan, Italy; Bracco Imaging SpA, Via Caduti di Marcinelle, 20134 Milan, Italy.
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Khattap MG, Abd Elaziz M, Hassan HGEMA, Elgarayhi A, Sallah M. AI-based model for automatic identification of multiple sclerosis based on enhanced sea-horse optimizer and MRI scans. Sci Rep 2024; 14:12104. [PMID: 38802440 DOI: 10.1038/s41598-024-61876-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 05/10/2024] [Indexed: 05/29/2024] Open
Abstract
This study aims to develop an AI-enhanced methodology for the expedited and accurate diagnosis of Multiple Sclerosis (MS), a chronic disease affecting the central nervous system leading to progressive impairment. Traditional diagnostic methods are slow and require substantial expertise, underscoring the need for innovative solutions. Our approach involves two phases: initially, extracting features from brain MRI images using first-order histograms, the gray level co-occurrence matrix, and local binary patterns. A unique feature selection technique combining the Sine Cosine Algorithm with the Sea-horse Optimizer is then employed to identify the most significant features. Utilizing the eHealth lab dataset, which includes images from 38 MS patients (mean age 34.1 ± 10.5 years; 17 males, 21 females) and matched healthy controls, our model achieved a remarkable 97.97% detection accuracy using the k-nearest neighbors classifier. Further validation on a larger dataset containing 262 MS cases (199 females, 63 males; mean age 31.26 ± 10.34 years) and 163 healthy individuals (109 females, 54 males; mean age 32.35 ± 10.30 years) demonstrated a 92.94% accuracy for FLAIR images and 91.25% for T2-weighted images with the Random Forest classifier, outperforming existing MS detection methods. These results highlight the potential of the proposed technique as a clinical decision-making tool for the early identification and management of MS.
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Affiliation(s)
- Mohamed G Khattap
- Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt.
- Technology of Radiology and Medical Imaging Program, Faculty of Applied Health Sciences Technology, Galala University, Suez, 435611, Egypt.
| | - Mohamed Abd Elaziz
- Faculty of Computer Science and Engineering, Galala University, Suez, 435611, Egypt
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon
| | - Hend Galal Eldeen Mohamed Ali Hassan
- Technology of Radiology and Medical Imaging Program, Faculty of Applied Health Sciences Technology, Galala University, Suez, 435611, Egypt
- Diagnostic and Interventional Radiology Department, Faculty of Medicine, Ain Shams University, Cairo, 11591, Egypt
| | - Ahmed Elgarayhi
- Applied Mathematical Physics Research Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
| | - Mohammed Sallah
- Department of Physics, College of Sciences, University of Bisha, P.O. Box 344, Bisha, 61922, Saudi Arabia
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Lam BD, Dodge LE, Zerbey S, Robertson W, Rosovsky RP, Lake L, Datta S, Elavakanar P, Adamski A, Reyes N, Abe K, Vlachos IS, Zwicker JI, Patell R. The potential use of artificial intelligence for venous thromboembolism prophylaxis and management: clinician and healthcare informatician perspectives. Sci Rep 2024; 14:12010. [PMID: 38796561 PMCID: PMC11127994 DOI: 10.1038/s41598-024-62535-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 05/17/2024] [Indexed: 05/28/2024] Open
Abstract
Venous thromboembolism (VTE) is the leading cause of preventable death in hospitalized patients. Artificial intelligence (AI) and machine learning (ML) can support guidelines recommending an individualized approach to risk assessment and prophylaxis. We conducted electronic surveys asking clinician and healthcare informaticians about their perspectives on AI/ML for VTE prevention and management. Of 101 respondents to the informatician survey, most were 40 years or older, male, clinicians and data scientists, and had performed research on AI/ML. Of the 607 US-based respondents to the clinician survey, most were 40 years or younger, female, physicians, and had never used AI to inform clinical practice. Most informaticians agreed that AI/ML can be used to manage VTE (56.0%). Over one-third were concerned that clinicians would not use the technology (38.9%), but the majority of clinicians believed that AI/ML probably or definitely can help with VTE prevention (70.1%). The most common concern in both groups was a perceived lack of transparency (informaticians 54.4%; clinicians 25.4%). These two surveys revealed that key stakeholders are interested in AI/ML for VTE prevention and management, and identified potential barriers to address prior to implementation.
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Affiliation(s)
- Barbara D Lam
- Division of Hematology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, 02215, USA
- Division of Clinical Informatics, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, USA
| | - Laura E Dodge
- Department of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sabrina Zerbey
- Division of Hematology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, 02215, USA
| | - William Robertson
- Weber State University, Ogden, UT, USA
- National Blood Clot Alliance, Philadelphia, PA, USA
| | - Rachel P Rosovsky
- Division of Hematology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Siddhant Datta
- Division of Hospital Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Pavania Elavakanar
- Division of Hematology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, 02215, USA
| | - Alys Adamski
- Division of Blood Disorders, National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Nimia Reyes
- Division of Blood Disorders, National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Karon Abe
- Division of Blood Disorders, National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Ioannis S Vlachos
- Department of Pathology, Cancer Research Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Jeffrey I Zwicker
- Division of Hematology, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rushad Patell
- Division of Hematology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, 02215, USA.
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Hoppe BF, Rueckel J, Dikhtyar Y, Heimer M, Fink N, Sabel BO, Ricke J, Rudolph J, Cyran CC. Implementing Artificial Intelligence for Emergency Radiology Impacts Physicians' Knowledge and Perception: A Prospective Pre- and Post-Analysis. Invest Radiol 2024; 59:404-412. [PMID: 37843828 DOI: 10.1097/rli.0000000000001034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
PURPOSE The aim of this study was to evaluate the impact of implementing an artificial intelligence (AI) solution for emergency radiology into clinical routine on physicians' perception and knowledge. MATERIALS AND METHODS A prospective interventional survey was performed pre-implementation and 3 months post-implementation of an AI algorithm for fracture detection on radiographs in late 2022. Radiologists and traumatologists were asked about their knowledge and perception of AI on a 7-point Likert scale (-3, "strongly disagree"; +3, "strongly agree"). Self-generated identification codes allowed matching the same individuals pre-intervention and post-intervention, and using Wilcoxon signed rank test for paired data. RESULTS A total of 47/71 matched participants completed both surveys (66% follow-up rate) and were eligible for analysis (34 radiologists [72%], 13 traumatologists [28%], 15 women [32%]; mean age, 34.8 ± 7.8 years). Postintervention, there was an increase that AI "reduced missed findings" (1.28 [pre] vs 1.94 [post], P = 0.003) and made readers "safer" (1.21 vs 1.64, P = 0.048), but not "faster" (0.98 vs 1.21, P = 0.261). There was a rising disagreement that AI could "replace the radiological report" (-2.04 vs -2.34, P = 0.038), as well as an increase in self-reported knowledge about "clinical AI," its "chances," and its "risks" (0.40 vs 1.00, 1.21 vs 1.70, and 0.96 vs 1.34; all P 's ≤ 0.028). Radiologists used AI results more frequently than traumatologists ( P < 0.001) and rated benefits higher (all P 's ≤ 0.038), whereas senior physicians were less likely to use AI or endorse its benefits (negative correlation with age, -0.35 to 0.30; all P 's ≤ 0.046). CONCLUSIONS Implementing AI for emergency radiology into clinical routine has an educative aspect and underlines the concept of AI as a "second reader," to support and not replace physicians.
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Affiliation(s)
- Boj Friedrich Hoppe
- From the Department of Radiology, University Hospital, LMU Munich, Munich, Germany (B.F.J., J.Rueckel, Y.D., M.H., N.F., B.O.S., J.Ricke, J.Rudolph, C.C.C.); and Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany (J.R.)
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Estrada Alamo CE, Diatta F, Monsell SE, Lane-Fall MB. Artificial Intelligence in Anesthetic Care: A Survey of Physician Anesthesiologists. Anesth Analg 2024; 138:938-950. [PMID: 38055624 DOI: 10.1213/ane.0000000000006752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
BACKGROUND This study explored physician anesthesiologists' knowledge, exposure, and perceptions of artificial intelligence (AI) and their associations with attitudes and expectations regarding its use in clinical practice. The findings highlight the importance of understanding anesthesiologists' perspectives for the successful integration of AI into anesthesiology, as AI has the potential to revolutionize the field. METHODS A cross-sectional survey of 27,056 US physician anesthesiologists was conducted to assess their knowledge, perceptions, and expectations regarding the use of AI in clinical practice. The primary outcome measured was attitude toward the use of AI in clinical practice, with scores of 4 or 5 on a 5-point Likert scale indicating positive attitudes. The anticipated impact of AI on various aspects of professional work was measured using a 3-point Likert scale. Logistic regression was used to explore the relationship between participant responses and attitudes toward the use of AI in clinical practice. RESULTS A 2021 survey of 27,056 US physician anesthesiologists received 1086 responses (4% response rate). Most respondents were male (71%), active clinicians (93%) under 45 (34%). A majority of anesthesiologists (61%) had some knowledge of AI and 48% had a positive attitude toward using AI in clinical practice. While most respondents believed that AI can improve health care efficiency (79%), timeliness (75%), and effectiveness (69%), they are concerned that its integration in anesthesiology could lead to a decreased demand for anesthesiologists (45%) and decreased earnings (45%). Within a decade, respondents expected AI would outperform them in predicting adverse perioperative events (83%), formulating pain management plans (67%), and conducting airway exams (45%). The absence of algorithmic transparency (60%), an ambiguous environment regarding malpractice (47%), and the possibility of medical errors (47%) were cited as significant barriers to the use of AI in clinical practice. Respondents indicated that their motivation to use AI in clinical practice stemmed from its potential to enhance patient outcomes (81%), lower health care expenditures (54%), reduce bias (55%), and boost productivity (53%). Variables associated with positive attitudes toward AI use in clinical practice included male gender (odds ratio [OR], 1.7; P < .001), 20+ years of experience (OR, 1.8; P < .01), higher AI knowledge (OR, 2.3; P = .01), and greater AI openness (OR, 10.6; P < .01). Anxiety about future earnings was associated with negative attitudes toward AI use in clinical practice (OR, 0.54; P < .01). CONCLUSIONS Understanding anesthesiologists' perspectives on AI is essential for the effective integration of AI into anesthesiology, as AI has the potential to revolutionize the field.
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Affiliation(s)
- Carlos E Estrada Alamo
- From the Department of Anesthesiology, Virginia Mason Medical Center, Seattle, Washington
| | - Fortunay Diatta
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Yale School of Medicine, New Haven, Connecticut
| | - Sarah E Monsell
- Department of Biostatistics, University of Washington, Hans Rosling Center for Population Health, Seattle, Washington
| | - Meghan B Lane-Fall
- Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, Pennsylvania
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Alwadani FAS, Lone A, Hakami MT, Moria AH, Alamer W, Alghirash RA, Alnawah AK, Hadadi AS. Attitude and Understanding of Artificial Intelligence Among Saudi Medical Students: An Online Cross-Sectional Study. J Multidiscip Healthc 2024; 17:1887-1899. [PMID: 38706506 PMCID: PMC11068042 DOI: 10.2147/jmdh.s455260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 04/02/2024] [Indexed: 05/07/2024] Open
Abstract
Purpose Artificial Intelligence is drastically used nowadays in healthcare, but little is known about the attitude and perception of medical students towards AI in Saudi Arabia. This study aimed to explore undergraduate medical student's views on AI, assessed their understanding of AI, and the level of confidence of using basic AI tools in the future. Methods This cross-sectional study invited 303 medical undergraduate students to complete an anonymous electronic survey, which consists of questions related to attitude, understanding and confidence of using basic AI tools. We examined the statistical association between the categorical variables by using Chi-square test. Results The results of the study indicate that eighty-seven percent of participants believed that AI will play significant role in healthcare. Thirty-eight percent respondents reported that they have an understanding of the basic computational principle of AI. 71.29% respondents agreed that teaching in AI would be favorable for their career. More than half of the participants were confident in using basic AI tools in the future, Male students (p = 0.00), 26-30 years old participants (p = 0.03), intern students (p = 0.00), and Imam Abdulrahman Bin Faisal University medical students (p = 0.04) had positive attitude of artificial intelligence. Male participants (p = 0.02), and intern students (p = 0.00) had the highest proportion of confidence in using basic healthcare AI tool. Nearly 14% students received training on AI. Participants who received training on AI reported better understanding of AI (p = 0.03), develops positive attitude towards teaching in AI (p = 0.05), more confidence in using basic healthcare AI tools (p = 0.05). Conclusion Saudi medical undergraduate students understand the significance of AI and demonstrated a positive attitude towards AI. Medical students training on AI should be expanded and improved to avoid threats for seeking jobs by adapting artificial intelligence.
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Affiliation(s)
| | - Ayoob Lone
- Clinical Neurosciences Department, College of Medicine, King Faisal University, AlHasa, Saudi Arabia
| | | | | | - Walaa Alamer
- College of Medicine, King Faisal University, AlHasa, Saudi Arabia
| | | | | | - Abdulaziz Shary Hadadi
- Clinical Neurosciences Department, College of Medicine, King Faisal University, AlHasa, Saudi Arabia
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Nowroozi A, Salehi MA, Shobeiri P, Agahi S, Momtazmanesh S, Kaviani P, Kalra MK. Artificial intelligence diagnostic accuracy in fracture detection from plain radiographs and comparing it with clinicians: a systematic review and meta-analysis. Clin Radiol 2024:S0009-9260(24)00200-9. [PMID: 38772766 DOI: 10.1016/j.crad.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/09/2024] [Accepted: 04/15/2024] [Indexed: 05/23/2024]
Abstract
PURPOSE Fracture detection is one of the most commonly used and studied aspects of artificial intelligence (AI) in medicine. In this systematic review and meta-analysis, we aimed to summarize available literature and data regarding AI performance in fracture detection on plain radiographs and various factors affecting it. METHODS We systematically reviewed studies evaluating AI algorithms in detecting bone fractures in plain radiographs, combined their performance using meta-analysis (a bivariate regression approach), and compared it with that of clinicians. We also analyzed the factors potentially affecting algorithm performance using meta-regression. RESULTS Our analysis included 100 studies. In 83 studies with confusion matrices, AI algorithms showed a sensitivity of 91.43% and a specificity of 92.12% (Area under the summary receiver operator curve = 0.968). After adjustment and false discovery rate correction, tibia/fibula (excluding ankle) fractures were associated with higher (7.0%, p=0.004) AI sensitivity, while more recent publications (5.5%, p=0.003) and Xception architecture (6.6%, p<0.001) were associated with higher specificity. Clinicians and AI showed similar specificity in fracture identification, although AI leaned to higher sensitivity (7.6%, p=0.07). Radiologists, on the other hand, were more specific than AI overall and in several subgroups, and more sensitive to hip fractures before FDR correction. CONCLUSIONS Currently available AI aids could result in a significant improvement in care where radiologists are not readily available. Moreover, identifying factors affecting algorithm performance could guide AI development teams in their process of optimizing their products.
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Affiliation(s)
- A Nowroozi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - M A Salehi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - P Shobeiri
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - S Agahi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - S Momtazmanesh
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - P Kaviani
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - M K Kalra
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
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Tozsin A, Ucmak H, Soyturk S, Aydin A, Gozen AS, Fahim MA, Güven S, Ahmed K. The Role of Artificial Intelligence in Medical Education: A Systematic Review. Surg Innov 2024:15533506241248239. [PMID: 38632898 DOI: 10.1177/15533506241248239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
BACKGROUND To examine the artificial intelligence (AI) tools currently being studied in modern medical education, and critically evaluate the level of validation and the quality of evidence presented in each individual study. METHODS This review (PROSPERO ID: CRD42023410752) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A database search was conducted using PubMed, Embase, and Cochrane Library. Articles written in the English language between 2000 and March 2023 were reviewed retrospectively using the MeSH Terms "AI" and "medical education" A total of 4642 potentially relevant studies were found. RESULTS After a thorough screening process, 36 studies were included in the final analysis. These studies consisted of 26 quantitative studies and 10 studies investigated the development and validation of AI tools. When examining the results of studies in which Support vector machines (SVMs) were employed, it has demonstrated high accuracy in assessing students' experiences, diagnosing acute abdominal pain, classifying skilled and novice participants, and evaluating surgical training levels. Particularly in the comparison of surgical skill levels, it has achieved an accuracy rate of over 92%. CONCLUSION AI tools demonstrated effectiveness in improving practical skills, diagnosing diseases, and evaluating student performance. However, further research with rigorous validation is required to identify the most effective AI tools for medical education.
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Affiliation(s)
- Atinc Tozsin
- Department of Urology, Trakya University School of Medicine, Edirne, Turkey
| | - Harun Ucmak
- Department of Urology, Meram School of Medicine, Necmettin Erbakan University, Konya, Turkey
| | - Selim Soyturk
- Department of Urology, Meram School of Medicine, Necmettin Erbakan University, Konya, Turkey
| | - Abdullatif Aydin
- MRC Centre for Transplantation, Guy's Hospital, King's College London, London, UK
- Department of Urology, King's College Hospital NHS Foundation Trust, London, UK
| | | | - Maha Al Fahim
- Medical Education Department, Sheikh Khalifa Medical City, Abu Dhabi, UAE
| | - Selcuk Güven
- Department of Urology, Meram School of Medicine, Necmettin Erbakan University, Konya, Turkey
| | - Kamran Ahmed
- MRC Centre for Transplantation, Guy's Hospital, King's College London, London, UK
- Khalifa University, Abu Dhabi, UAE
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10
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Al Mohammad B, Aldaradkeh A, Gharaibeh M, Reed W. Assessing radiologists' and radiographers' perceptions on artificial intelligence integration: opportunities and challenges. Br J Radiol 2024; 97:763-769. [PMID: 38273675 PMCID: PMC11027289 DOI: 10.1093/bjr/tqae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 09/30/2023] [Accepted: 01/21/2024] [Indexed: 01/27/2024] Open
Abstract
OBJECTIVES The objective of this study was to evaluate radiologists' and radiographers' opinions and perspectives on artificial intelligence (AI) and its integration into the radiology department. Additionally, we investigated the most common challenges and barriers that radiologists and radiographers face when learning about AI. METHODS A nationwide, online descriptive cross-sectional survey was distributed to radiologists and radiographers working in hospitals and medical centres from May 29, 2023 to July 30, 2023. The questionnaire examined the participants' opinions, feelings, and predictions regarding AI and its applications in the radiology department. Descriptive statistics were used to report the participants' demographics and responses. Five-points Likert-scale data were reported using divergent stacked bar graphs to highlight any central tendencies. RESULTS Responses were collected from 258 participants, revealing a positive attitude towards implementing AI. Both radiologists and radiographers predicted breast imaging would be the subspecialty most impacted by the AI revolution. MRI, mammography, and CT were identified as the primary modalities with significant importance in the field of AI application. The major barrier encountered by radiologists and radiographers when learning about AI was the lack of mentorship, guidance, and support from experts. CONCLUSION Participants demonstrated a positive attitude towards learning about AI and implementing it in the radiology practice. However, radiologists and radiographers encounter several barriers when learning about AI, such as the absence of experienced professionals support and direction. ADVANCES IN KNOWLEDGE Radiologists and radiographers reported several barriers to AI learning, with the most significant being the lack of mentorship and guidance from experts, followed by the lack of funding and investment in new technologies.
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Affiliation(s)
- Badera Al Mohammad
- Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Afnan Aldaradkeh
- Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Monther Gharaibeh
- Department of Special Surgery, Faculty of Medicine, The Hashemite University, Zarqa 13133, Jordan
| | - Warren Reed
- Discipline of Medical Imaging Science, Faculty of Medicine and Health, University of Sydney 2006, Sydney, NSW, Australia
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11
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Shawn Yuan PH, Yan TD, Sharma S, Chahley E, MacLean LJ, Freitas V, Yong-Hing CJ. Authorship gender among articles about artificial intelligence in breast imaging. Eur J Radiol 2024; 175:111428. [PMID: 38492508 DOI: 10.1016/j.ejrad.2024.111428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 03/04/2024] [Accepted: 03/11/2024] [Indexed: 03/18/2024]
Abstract
RATIONALE AND OBJECTIVES The purpose of this study is to investigate the variance of women authors, specifically first and senior authorship among peer-reviewed artificial intelligence-related articles with a specific focus in breast imaging. MATERIALS AND METHODS A strategic search was conducted in July 2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to capture all existing and publicly available peer-reviewed articles intersecting AI and breast imaging. Primary outcomes were first and senior authors' gender, which were assigned with the aid of an emailed self-declaration survey. Secondary outcomes included country of article, journal impact factor, and year of publication. Comparisons were made using logistic regression models and analysis of variances. RESULTS 115 studies were included in the analysis. Women authors represented 35.7% (41/115) and 37.4% (43/115) of first and senior authors, respectively. Logistic regression modelling showed a significant increase in women senior authors over time but no changes in women first authors. Impact factor was not associated with female authorship and certain countries had women authorship reach over 50%. CONCLUSION This study demonstrates that there is a significant authorship gender gap in artificial intelligence breast imaging research. An increasing temporal trend of senior authors in breast imaging AI-related research is a promising prognosis for more women voices in this field. Further study needs to be done to understand the reasons behind this gap and any potential implications.
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Affiliation(s)
- Po Hsiang Shawn Yuan
- Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Tyler D Yan
- Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sonali Sharma
- Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Erin Chahley
- Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Luke J MacLean
- Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Vivianne Freitas
- Department of Medical Imaging, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Charlotte J Yong-Hing
- Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada; Department of Diagnostic Imaging, BC Cancer, Vancouver, British Columbia, Canada.
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12
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Warren BE, Bilbily A, Gichoya JW, Conway A, Li B, Fawzy A, Barragán C, Jaberi A, Mafeld S. An Introductory Guide to Artificial Intelligence in Interventional Radiology: Part 1 Foundational Knowledge. Can Assoc Radiol J 2024:8465371241236376. [PMID: 38445497 DOI: 10.1177/08465371241236376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024] Open
Abstract
Artificial intelligence (AI) is rapidly evolving and has transformative potential for interventional radiology (IR) clinical practice. However, formal training in AI may be limited for many clinicians and therefore presents a challenge for initial implementation and trust in AI. An understanding of the foundational concepts in AI may help familiarize the interventional radiologist with the field of AI, thus facilitating understanding and participation in the development and deployment of AI. A pragmatic classification system of AI based on the complexity of the model may guide clinicians in the assessment of AI. Finally, the current state of AI in IR and the patterns of implementation are explored (pre-procedural, intra-procedural, and post-procedural).
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Affiliation(s)
- Blair Edward Warren
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada
| | - Alexander Bilbily
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- 16 Bit Inc., Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | | | - Aaron Conway
- Prince Charles Hospital, Queensland University of Technology, Brisbane, QLD, Australia
| | - Ben Li
- Division of Vascular Surgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Aly Fawzy
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Camilo Barragán
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada
| | - Arash Jaberi
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada
| | - Sebastian Mafeld
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada
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13
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Busch F, Hoffmann L, Truhn D, Palaian S, Alomar M, Shpati K, Makowski MR, Bressem KK, Adams LC. International pharmacy students' perceptions towards artificial intelligence in medicine-A multinational, multicentre cross-sectional study. Br J Clin Pharmacol 2024; 90:649-661. [PMID: 37728146 DOI: 10.1111/bcp.15911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 09/06/2023] [Accepted: 09/16/2023] [Indexed: 09/21/2023] Open
Abstract
AIMS To explore international undergraduate pharmacy students' views on integrating artificial intelligence (AI) into pharmacy education and practice. METHODS This cross-sectional institutional review board-approved multinational, multicentre study comprised an anonymous online survey of 14 multiple-choice items to assess pharmacy students' preferences for AI events in the pharmacy curriculum, the current state of AI education, and students' AI knowledge and attitudes towards using AI in the pharmacy profession, supplemented by 8 demographic queries. Subgroup analyses were performed considering sex, study year, tech-savviness, and prior AI knowledge and AI events in the curriculum using the Mann-Whitney U-test. Variances were reported for responses in Likert scale format. RESULTS The survey gathered 387 pharmacy student opinions across 16 faculties and 12 countries. Students showed predominantly positive attitudes towards AI in medicine (58%, n = 225) and expressed a strong desire for more AI education (72%, n = 276). However, they reported limited general knowledge of AI (63%, n = 242) and felt inadequately prepared to use AI in their future careers (51%, n = 197). Male students showed more positive attitudes towards increasing efficiency through AI (P = .011), while tech-savvy and advanced-year students expressed heightened concerns about potential legal and ethical issues related to AI (P < .001/P = .025, respectively). Students who had AI courses as part of their studies reported better AI knowledge (P < .001) and felt more prepared to apply it professionally (P < .001). CONCLUSIONS Our findings underline the generally positive attitude of international pharmacy students towards AI application in medicine and highlight the necessity for a greater emphasis on AI education within pharmacy curricula.
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Affiliation(s)
- Felix Busch
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Department of Anesthesiology, Division of Operative Intensive Care Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Lena Hoffmann
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany
| | - Subish Palaian
- Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates
- Center of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates
| | - Muaed Alomar
- Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates
- Center of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates
| | - Kleva Shpati
- Department of Pharmacy, Albanian University, Tirana, Albania
| | | | - Keno Kyrill Bressem
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
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14
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Lombi L, Rossero E. How artificial intelligence is reshaping the autonomy and boundary work of radiologists. A qualitative study. SOCIOLOGY OF HEALTH & ILLNESS 2024; 46:200-218. [PMID: 37573551 DOI: 10.1111/1467-9566.13702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 07/19/2023] [Indexed: 08/15/2023]
Abstract
The application of artificial intelligence (AI) in medical practice is spreading, especially in technologically dense fields such as radiology, which could consequently undergo profound transformations in the near future. This article aims to qualitatively explore the potential influence of AI technologies on the professional identity of radiologists. Drawing on 12 in-depth interviews with a subgroup of radiologists who participated in a larger study, this article investigated (1) whether radiologists perceived AI as a threat to their decision-making autonomy; and (2) how radiologists perceived the future of their profession compared to other health-care professions. The findings revealed that while AI did not generally affect radiologists' decision-making autonomy, it threatened their professional and epistemic authority. Two discursive strategies were identified to explain these findings. The first strategy emphasised radiologists' specific expertise and knowledge that extends beyond interpreting images, a task performed with high accuracy by AI machines. The second strategy underscored the fostering of radiologists' professional prestige through developing expertise in using AI technologies, a skill that would distinguish them from other clinicians who did not pose this knowledge. This study identifies AI machines as status objects and useful tools in performing boundary work in and around the radiological profession.
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Affiliation(s)
- Linda Lombi
- Department of Sociology, Università Cattolica del Sacro Cuore, Milan, Italy
| | - Eleonora Rossero
- Fundamental Rights Laboratory, Collegio Carlo Alberto, Turin, Italy
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15
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Truong NM, Vo TQ, Tran HTB, Nguyen HT, Pham VNH. Healthcare students' knowledge, attitudes, and perspectives toward artificial intelligence in the southern Vietnam. Heliyon 2023; 9:e22653. [PMID: 38107295 PMCID: PMC10724669 DOI: 10.1016/j.heliyon.2023.e22653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 11/15/2023] [Accepted: 11/16/2023] [Indexed: 12/19/2023] Open
Abstract
The application of new technologies in medical education still lags behind the extraordinary advances of AI. This study examined the understanding, attitudes, and perspectives of Vietnamese medical students toward AI and its consequences, as well as their knowledge of existing AI operations in Vietnam. A cross-sectional online survey was administered to 1142 students enrolled in undergraduate medicine and pharmacy programs. Most of the participants had no understanding of AI in healthcare (1053 or 92.2 %). The majority believed that AI would benefit their careers (890 or 77.9 %) and that such innovation will be used to oversee public health and epidemic prevention on their behalf (882 or 77.2 %). The proportion of students with satisfactory knowledge significantly differed depending on gender (P < 0.001), major (P = 0.003), experience (P < 0.001), and income (P = 0.011). The percentage of respondents with positive attitudes significantly differed by year level (P = 0.008) and income (P = 0.003), and the proportion with favorable perspectives regarding AI varied considerably by age (P = 0.046) and major (P < 0.001). Most of the participants wanted to integrate AI into radiology and digital imaging training (P = 0.283), while the fifth-year students wished to learn about AI in medical genetics and genomics (P < 0.001, 4.0 ± 0.8). The male students had 1.898 times more adequate knowledge of AI than their female counterparts, and those who had attended webinars/lectures/courses on AI in healthcare had 4.864 times more adequate knowledge than those having no such experiences. The majority believed that the barrier to implementing AI in healthcare is the lack of financial resources (83.54 %) and appropriate training (81.00 %). Participants saw AI as a "partner" rather than a "competitor", but the majority of low knowledge was recorded. Future research should take into account the way to integrate AI into medical training programs for healthcare students.
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Affiliation(s)
- Nguyen Minh Truong
- Faculty of Pharmacy, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, 700000, Viet Nam
| | - Trung Quang Vo
- Faculty of Pharmacy, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, 700000, Viet Nam
| | - Hien Thi Bich Tran
- Faculty of Pharmacy, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, 700000, Viet Nam
| | - Hiep Thanh Nguyen
- Faculty of Medicine, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, 700000, Viet Nam
| | - Van Nu Hanh Pham
- Faculty of Pharmaceutical Management and Economic, Hanoi University of Pharmacy, Hanoi, 100000, Viet Nam
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16
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Vo V, Chen G, Aquino YSJ, Carter SM, Do QN, Woode ME. Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis. Soc Sci Med 2023; 338:116357. [PMID: 37949020 DOI: 10.1016/j.socscimed.2023.116357] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 09/04/2023] [Accepted: 10/24/2023] [Indexed: 11/12/2023]
Abstract
INTRODUCTION Despite the proliferation of Artificial Intelligence (AI) technology over the last decade, clinician, patient, and public perceptions of its use in healthcare raise a number of ethical, legal and social questions. We systematically review the literature on attitudes towards the use of AI in healthcare from patients, the general public and health professionals' perspectives to understand these issues from multiple perspectives. METHODOLOGY A search for original research articles using qualitative, quantitative, and mixed methods published between 1 Jan 2001 to 24 Aug 2021 was conducted on six bibliographic databases. Data were extracted and classified into different themes representing views on: (i) knowledge and familiarity of AI, (ii) AI benefits, risks, and challenges, (iii) AI acceptability, (iv) AI development, (v) AI implementation, (vi) AI regulations, and (vii) Human - AI relationship. RESULTS The final search identified 7,490 different records of which 105 publications were selected based on predefined inclusion/exclusion criteria. While the majority of patients, the general public and health professionals generally had a positive attitude towards the use of AI in healthcare, all groups indicated some perceived risks and challenges. Commonly perceived risks included data privacy; reduced professional autonomy; algorithmic bias; healthcare inequities; and greater burnout to acquire AI-related skills. While patients had mixed opinions on whether healthcare workers suffer from job loss due to the use of AI, health professionals strongly indicated that AI would not be able to completely replace them in their professions. Both groups shared similar doubts about AI's ability to deliver empathic care. The need for AI validation, transparency, explainability, and patient and clinical involvement in the development of AI was emphasised. To help successfully implement AI in health care, most participants envisioned that an investment in training and education campaigns was necessary, especially for health professionals. Lack of familiarity, lack of trust, and regulatory uncertainties were identified as factors hindering AI implementation. Regarding AI regulations, key themes included data access and data privacy. While the general public and patients exhibited a willingness to share anonymised data for AI development, there remained concerns about sharing data with insurance or technology companies. One key domain under this theme was the question of who should be held accountable in the case of adverse events arising from using AI. CONCLUSIONS While overall positivity persists in attitudes and preferences toward AI use in healthcare, some prevalent problems require more attention. There is a need to go beyond addressing algorithm-related issues to look at the translation of legislation and guidelines into practice to ensure fairness, accountability, transparency, and ethics in AI.
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Affiliation(s)
- Vinh Vo
- Centre for Health Economics, Monash University, Australia.
| | - Gang Chen
- Centre for Health Economics, Monash University, Australia
| | - Yves Saint James Aquino
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Soceity, University of Wollongong, Australia
| | - Stacy M Carter
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Soceity, University of Wollongong, Australia
| | - Quynh Nga Do
- Department of Economics, Monash University, Australia
| | - Maame Esi Woode
- Centre for Health Economics, Monash University, Australia; Monash Data Futures Research Institute, Australia
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17
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Pirocca U, Vinchon F, Beregi JP, Jacques T, Delabrousse E, Gouy E, Rolland F, Truong LN, Frajerman A, Ohana M, Malakhia A, Hadouiri N. How do medical students, residents and graduated physicians really perceive radiologists? A cross-national study. Eur J Radiol 2023; 168:111115. [PMID: 37832193 DOI: 10.1016/j.ejrad.2023.111115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 09/08/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023]
Abstract
BACKGROUND Radiology has always been an attractive specialty for residents, but its attractiveness has recently decreased in France regarding the median choice rank after at the National Residency Board. AIM To study Radiologists' perceptions and social representations (SRs) among a group of medical students, residents and graduated physicians in France, to better understand the view of Radiologists to debunk stereotypes. METHODS The nationwide web-based survey was based on valid hierarchical evocation methods. We determined the corpus's central core and SRs' principal themes with prototypical and correspondence factor analysis (CFA), respectively. RESULTS Overall, 419 answers were analyzed. Radiologists' SRs were divided into 3 classes: negative stereotypes of Radiologists, negative stereotypes of the Radiologists' daily practice and Radiologists' skills. After multivariate analysis, variables that seemed to have a positive influence on Radiologists' SRs were considering radiology as a potential choice of specialty (p < 0.001) and the existence of practical experience in Radiology (p = 0.008). Women seemed to have a more negative SR of Radiologists than men (p = 0.035). DISCUSSION This was the largest qualitative study on the subject and the only one among medical students, residents and graduated physicians, allowing a global picture. SRs of Radiologists seemed to be negative, potentially caused by poor knowledge of the Radiologists' profession. CONCLUSION SRs of Radiologists among medical students and graduated physicians appears to be negative. Promoting the specialty among medical students and encouraging their immersion in a Radiology department could help to debunk many stereotypes about the daily life and missions of Radiologists.
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Affiliation(s)
- Ugo Pirocca
- Department of Radiology, Centre Hospitalier Universitaire de Besançon, 25030 Besançon, France; Department of Vascular & Interventional Radiology, University Hospital of Dijon, Dijon, France
| | - Florent Vinchon
- Université Paris Cité et Université Gustave Eiffel, LaPEA, F-92100 Boulogne-Billancourt, France
| | - Jean Paul Beregi
- Department of Radiology, Nîmes Carémeau University Hospital, 30900 Nimes, France
| | - Thibaut Jacques
- Service de radiologie et d'imagerie musculo-squelettique, centre de consultation et d'imagerie de l'appareil locomoteur, CHU de Lille, avenue du Professeur Emile-Laine, 59000 Lille, France; Faculté de médecine, université de Lille, Lille, France
| | - Eric Delabrousse
- Department of Radiology, Centre Hospitalier Universitaire de Besançon, 25030 Besançon, France
| | - Evan Gouy
- Genetics Department, University Hospital of Lyon, Bron, France; Genetics and Neurobiology of C. elegans, Mechanisms in Integrated Life Sciences (CNRS UMR 5284 - INSERM U1314 - Claude Bernard University Lyon 1), NeuroMyoGene Institute, Lyon, France
| | - Franck Rolland
- CESP-Inserm, U1018, Research in Ethics and Epistemology (R2E), Paris-Sud University, Paris-Saclay University, Inserm, Paris, France
| | - Linh Nam Truong
- Faculté de Médecine, Université de Bourgogne-Franche-Comté, 7 boulevard Jeanne D'Arc, Dijon, France
| | - Ariel Frajerman
- Université Paris-Saclay, AP-HP, Service Hospitalo-Universitaire de Psychiatrie, Hôpital de Bicêtre, Equipe MOODS, Inserm U1178, CESP (Centre de Recherche en Epidémiologie et Sante des Populations), Le Kremlin-Bicêtre, France
| | - Mickael Ohana
- Radiology Department, Nouvel Hôpital Civil, 1 Place de l'Hôpital, 67000 Strasbourg, France
| | - Alexandre Malakhia
- Department of Radiology, Centre Hospitalier Universitaire de Besançon, 25030 Besançon, France; Department of Vascular & Interventional Radiology, University Hospital of Dijon, Dijon, France
| | - Nawale Hadouiri
- Department of Physical Medicine and Rehabilitation, Center Hospitalier Universitaire de Dijon, 21000 Dijon, France.
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Chen Y, Wu Z, Wang P, Xie L, Yan M, Jiang M, Yang Z, Zheng J, Zhang J, Zhu J. Radiology Residents' Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study. J Med Internet Res 2023; 25:e48249. [PMID: 37856181 PMCID: PMC10623237 DOI: 10.2196/48249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 07/07/2023] [Accepted: 09/01/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is transforming various fields, with health care, especially diagnostic specialties such as radiology, being a key but controversial battleground. However, there is limited research systematically examining the response of "human intelligence" to AI. OBJECTIVE This study aims to comprehend radiologists' perceptions regarding AI, including their views on its potential to replace them, its usefulness, and their willingness to accept it. We examine the influence of various factors, encompassing demographic characteristics, working status, psychosocial aspects, personal experience, and contextual factors. METHODS Between December 1, 2020, and April 30, 2021, a cross-sectional survey was completed by 3666 radiology residents in China. We used multivariable logistic regression models to examine factors and associations, reporting odds ratios (ORs) and 95% CIs. RESULTS In summary, radiology residents generally hold a positive attitude toward AI, with 29.90% (1096/3666) agreeing that AI may reduce the demand for radiologists, 72.80% (2669/3666) believing AI improves disease diagnosis, and 78.18% (2866/3666) feeling that radiologists should embrace AI. Several associated factors, including age, gender, education, region, eye strain, working hours, time spent on medical images, resilience, burnout, AI experience, and perceptions of residency support and stress, significantly influence AI attitudes. For instance, burnout symptoms were associated with greater concerns about AI replacement (OR 1.89; P<.001), less favorable views on AI usefulness (OR 0.77; P=.005), and reduced willingness to use AI (OR 0.71; P<.001). Moreover, after adjusting for all other factors, perceived AI replacement (OR 0.81; P<.001) and AI usefulness (OR 5.97; P<.001) were shown to significantly impact the intention to use AI. CONCLUSIONS This study profiles radiology residents who are accepting of AI. Our comprehensive findings provide insights for a multidimensional approach to help physicians adapt to AI. Targeted policies, such as digital health care initiatives and medical education, can be developed accordingly.
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Affiliation(s)
- Yanhua Chen
- Vanke School of Public Health, Tsinghua University, Beijing, China
- School of Medicine, Tsinghua University, Beijing, China
| | - Ziye Wu
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Peicheng Wang
- Vanke School of Public Health, Tsinghua University, Beijing, China
- School of Medicine, Tsinghua University, Beijing, China
| | - Linbo Xie
- Vanke School of Public Health, Tsinghua University, Beijing, China
- School of Medicine, Tsinghua University, Beijing, China
| | - Mengsha Yan
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Maoqing Jiang
- Department of Radiology, Ningbo No. 2 Hospital, Ningbo, China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jianjun Zheng
- Department of Radiology, Ningbo No. 2 Hospital, Ningbo, China
| | - Jingfeng Zhang
- Department of Radiology, Ningbo No. 2 Hospital, Ningbo, China
| | - Jiming Zhu
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
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19
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Martiniussen MA, Larsen M, Larsen ASF, Hovda T, Koch HW, Bjørnerud A, Hofvind S. Norwegian radiologists' expectations of artificial intelligence in mammographic screening - A cross-sectional survey. Eur J Radiol 2023; 167:111061. [PMID: 37657381 DOI: 10.1016/j.ejrad.2023.111061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/13/2023] [Accepted: 08/22/2023] [Indexed: 09/03/2023]
Abstract
PURPOSE To explore Norwegian breast radiologists' expectations of adding artificial intelligence (AI) in the interpretation procedure of screening mammograms. METHODS All breast radiologists involved in interpretation of screening mammograms in BreastScreen Norway during 2021 and 2022 (n = 98) were invited to take part in this anonymous cross-sectional survey about use of AI in mammographic screening. The questionnaire included background information of the respondents, their expectations, considerations of biases, and ethical and social implications of implementing AI in screen reading. Data was collected digitally and analyzed using descriptive statistics. RESULTS The response rate was 61% (60/98), and 67% (40/60) of the respondents were women. Sixty percent (36/60) reported ≥10 years' experience in screen reading, while 82% (49/60) reported no or limited experience with AI in health care. Eighty-two percent of the respondents were positive to explore AI in the interpretation procedure in mammographic screening. When used as decision support, 68% (41/60) expected AI to increase the radiologists' sensitivity for cancer detection. As potential challenges, 55% (33/60) reported lack of trust in the AI system and 45% (27/60) reported discrepancy between radiologists and AI systems as possible challenges. The risk of automation bias was considered high among 47% (28/60). Reduced time spent reading mammograms was rated as a potential benefit by 70% (42/60). CONCLUSION The radiologists reported positive expectations of AI in the interpretation procedure of screening mammograms. Efforts to minimize the risk of automation bias and increase trust in the AI systems are important before and during future implementation of the tool.
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Affiliation(s)
- Marit A Martiniussen
- Department of Radiology, Østfold Hospital Trust, Kalnes, Norway; University of Oslo, Institute of Clinical Medicine, Oslo, Norway
| | - Marthe Larsen
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway
| | | | - Tone Hovda
- Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway
| | - Henrik W Koch
- Department of Radiology, Stavanger University Hospital, Stavanger, Norway; Faculty of Health Sciences, University of Stavanger, Stavanger, Norway
| | - Atle Bjørnerud
- Computational Radiology & Artificial Intelligence (CRAI) Unit, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway; Department of Physics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | - Solveig Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway; Department of Health and Care Sciences, UiT, The Artic University of Norway, Tromsø, Norway.
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Lin L, Tang B, Cao L, Yan J, Zhao T, Hua F, He H. The knowledge, experience, and attitude on artificial intelligence-assisted cephalometric analysis: Survey of orthodontists and orthodontic students. Am J Orthod Dentofacial Orthop 2023; 164:e97-e105. [PMID: 37565946 DOI: 10.1016/j.ajodo.2023.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 07/01/2023] [Accepted: 07/01/2023] [Indexed: 08/12/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) developed rapidly in orthodontics, and AI-based cephalometric applications have been adopted. This study aimed to assess AI-assisted cephalometric technologies related knowledge, experience, and attitude among orthodontists and orthodontic students; describe their subject view of the applications and related technologies in orthodontics; and identify associated factors. METHODS An online cross-sectional survey based on a professional tool (www.wjx.cn) was performed from October 11-17, 2022. Participants were recruited with a purposive and snowball sampling approach. Data was collected and analyzed with descriptive statistics, chi-square tests, and multivariable generalized estimating equations. RESULTS Four hundred eighty valid questionnaires were collected and analyzed; 68.8% of the respondents agreed that AI-based cephalometric applications would replace manual and semiautomatic approaches. Practitioners using AI-assisted applications (87.5%) spent less time in cephalometric analysis than the other groups using other approaches, and 349 (72.7%) respondents considered AI-based applications could assist in obtaining more accurate analysis results. Lectures and training programs (56.0%) were the main sources of respondents' knowledge about AI. Knowledge level was associated with experience in AI-related clinical or scientific projects (P <0.001). Most respondents (88.8%) were interested in future AI applications in orthodontics. CONCLUSIONS Respondents are optimistic about the future of AI in orthodontics. AI-assisted cephalometric applications were believed to make clinical diagnostic analysis more convenient and straightforward for practitioners and even replace manual and semiautomatic approaches. The education and promotion of AI should be strengthened to elevate orthodontists' understanding.
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Affiliation(s)
- Lizhuo Lin
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China; Department of Orthodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Bojun Tang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China; Department of Orthodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Lingyun Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China; Department of Orthodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Jiarong Yan
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China; Department of Orthodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Tingting Zhao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China; Department of Orthodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China; Center for Dentofacial Development and Sleep Medicine, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Fang Hua
- Center for Dentofacial Development and Sleep Medicine, School and Hospital of Stomatology, Wuhan University, Wuhan, China; Center for Orthodontics and Pediatric Dentistry at Optics Valley Branch, School and Hospital of Stomatology, Wuhan University, Wuhan, China; Center for Evidence-Based Stomatology, School and Hospital of Stomatology, Wuhan University, Wuhan, China; Division of Dentistry, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.
| | - Hong He
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China; Department of Orthodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China; Center for Dentofacial Development and Sleep Medicine, School and Hospital of Stomatology, Wuhan University, Wuhan, China.
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Eltawil FA, Atalla M, Boulos E, Amirabadi A, Tyrrell PN. Analyzing Barriers and Enablers for the Acceptance of Artificial Intelligence Innovations into Radiology Practice: A Scoping Review. Tomography 2023; 9:1443-1455. [PMID: 37624108 PMCID: PMC10459931 DOI: 10.3390/tomography9040115] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/23/2023] [Accepted: 07/26/2023] [Indexed: 08/26/2023] Open
Abstract
OBJECTIVES This scoping review was conducted to determine the barriers and enablers associated with the acceptance of artificial intelligence/machine learning (AI/ML)-enabled innovations into radiology practice from a physician's perspective. METHODS A systematic search was performed using Ovid Medline and Embase. Keywords were used to generate refined queries with the inclusion of computer-aided diagnosis, artificial intelligence, and barriers and enablers. Three reviewers assessed the articles, with a fourth reviewer used for disagreements. The risk of bias was mitigated by including both quantitative and qualitative studies. RESULTS An electronic search from January 2000 to 2023 identified 513 studies. Twelve articles were found to fulfill the inclusion criteria: qualitative studies (n = 4), survey studies (n = 7), and randomized controlled trials (RCT) (n = 1). Among the most common barriers to AI implementation into radiology practice were radiologists' lack of acceptance and trust in AI innovations; a lack of awareness, knowledge, and familiarity with the technology; and perceived threat to the professional autonomy of radiologists. The most important identified AI implementation enablers were high expectations of AI's potential added value; the potential to decrease errors in diagnosis; the potential to increase efficiency when reaching a diagnosis; and the potential to improve the quality of patient care. CONCLUSIONS This scoping review found that few studies have been designed specifically to identify barriers and enablers to the acceptance of AI in radiology practice. The majority of studies have assessed the perception of AI replacing radiologists, rather than other barriers or enablers in the adoption of AI. To comprehensively evaluate the potential advantages and disadvantages of integrating AI innovations into radiology practice, gathering more robust research evidence on stakeholder perspectives and attitudes is essential.
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Affiliation(s)
- Fatma A. Eltawil
- Department of Medical Imaging, University of Toronto, Toronto, ON M5S 1A1, Canada; (F.A.E.); (M.A.); (E.B.)
| | - Michael Atalla
- Department of Medical Imaging, University of Toronto, Toronto, ON M5S 1A1, Canada; (F.A.E.); (M.A.); (E.B.)
| | - Emily Boulos
- Department of Medical Imaging, University of Toronto, Toronto, ON M5S 1A1, Canada; (F.A.E.); (M.A.); (E.B.)
| | - Afsaneh Amirabadi
- Diagnostic Imaging Department, The Hospital for Sick Children, Toronto, ON M5G 1E8, Canada;
| | - Pascal N. Tyrrell
- Department of Medical Imaging, University of Toronto, Toronto, ON M5S 1A1, Canada; (F.A.E.); (M.A.); (E.B.)
- Department of Statistical Sciences, University of Toronto, Toronto, ON M5G 1Z5, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
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Salastekar NV, Maxfield C, Hanna TN, Krupinski EA, Heitkamp D, Grimm LJ. Artificial Intelligence/Machine Learning Education in Radiology: Multi-institutional Survey of Radiology Residents in the United States. Acad Radiol 2023; 30:1481-1487. [PMID: 36710101 DOI: 10.1016/j.acra.2023.01.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/02/2023] [Accepted: 01/02/2023] [Indexed: 01/31/2023]
Abstract
RATIONALE AND OBJECTIVES To evaluate radiology residents' perspectives regarding inclusion of artificial intelligence/ machine learning (AI/ML) education in the residency curriculum. MATERIALS AND METHODS An online anonymous survey was sent to 759 residents at 21 US radiology residency programs. Resident demographics, sub-specialty interests, educational background and research experiences, as well as the awareness, availability, and usefulness of various resources for AI/ML education were collected. RESULTS The survey response rate was 27% (209/759). A total of 74% of respondents were male, 80% were training at large university programs, and only a minority (<20) had formal education or research experience in AI/ML. All four years of training were represented (range: 20%-38%). The majority of the residents agreed or strongly agreed (83%) that AI/ML education should be a part of the radiology residency curriculum and that such education should equip them with the knowledge to troubleshoot an AI tool in practice / determine whether a tool is working as intended (82%). Among the residency programs that offer AI/ML education, the most common resources were lecture series (43%), national informatics courses (28%), and in-house/institutional courses (26%). About 24% of the residents reported no AI/ML educational offerings in their residency curriculum. Hands on AI/ML laboratory (67%) and lecture series (61%) were reported as the most beneficial or effective. The majority of the residents preferred AI/ML education offered as a continuous course spanning the radiology residency (R1 to R4) (76%), followed by mini fellowship during R4 (32%) and as a course during PGY1 (21%). CONCLUSION Residents largely favor the inclusion of formal AI/ML education in the radiology residency curriculum, prefer hands-on learning and lectures as learning tools, and prefer a continuous AI/ML course spanning R1-R4.
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Affiliation(s)
- Ninad V Salastekar
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 100 Woodruff Circle, Atlanta, GA 30322.
| | - Charles Maxfield
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
| | - Tarek N Hanna
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 100 Woodruff Circle, Atlanta, GA 30322
| | - Elizabeth A Krupinski
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 100 Woodruff Circle, Atlanta, GA 30322
| | | | - Lars J Grimm
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
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Lambert SI, Madi M, Sopka S, Lenes A, Stange H, Buszello CP, Stephan A. An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. NPJ Digit Med 2023; 6:111. [PMID: 37301946 DOI: 10.1038/s41746-023-00852-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 05/25/2023] [Indexed: 06/12/2023] Open
Abstract
Artificial intelligence (AI) in the domain of healthcare is increasing in prominence. Acceptance is an indispensable prerequisite for the widespread implementation of AI. The aim of this integrative review is to explore barriers and facilitators influencing healthcare professionals' acceptance of AI in the hospital setting. Forty-two articles met the inclusion criteria for this review. Pertinent elements to the study such as the type of AI, factors influencing acceptance, and the participants' profession were extracted from the included studies, and the studies were appraised for their quality. The data extraction and results were presented according to the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The included studies revealed a variety of facilitating and hindering factors for AI acceptance in the hospital setting. Clinical decision support systems (CDSS) were the AI form included in most studies (n = 21). Heterogeneous results with regard to the perceptions of the effects of AI on error occurrence, alert sensitivity and timely resources were reported. In contrast, fear of a loss of (professional) autonomy and difficulties in integrating AI into clinical workflows were unanimously reported to be hindering factors. On the other hand, training for the use of AI facilitated acceptance. Heterogeneous results may be explained by differences in the application and functioning of the different AI systems as well as inter-professional and interdisciplinary disparities. To conclude, in order to facilitate acceptance of AI among healthcare professionals it is advisable to integrate end-users in the early stages of AI development as well as to offer needs-adjusted training for the use of AI in healthcare and providing adequate infrastructure.
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Affiliation(s)
- Sophie Isabelle Lambert
- AIXTRA-Competence Center for Training and Patient Safety, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany.
- Department of Anesthesiology, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
| | - Murielle Madi
- Department of Nursing Science, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
| | - Saša Sopka
- AIXTRA-Competence Center for Training and Patient Safety, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
- Department of Anesthesiology, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Andrea Lenes
- AIXTRA-Competence Center for Training and Patient Safety, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Hendrik Stange
- Fraunhofer Society for the Advancement of Applied Research. Fraunhofer-Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven 1, 53757, Sankt Augustin, Bonn, Germany
| | - Claus-Peter Buszello
- Fraunhofer Society for the Advancement of Applied Research. Fraunhofer-Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven 1, 53757, Sankt Augustin, Bonn, Germany
| | - Astrid Stephan
- Department of Nursing Science, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
- Fliedner University of Applied Sciences, Geschwister-Aufricht-Straße, 940489, Düsseldorf, Germany
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Edzie EKM, Dzefi-Tettey K, Asemah AR, Brakohiapa EK, Asiamah S, Quarshie F, Amankwa AT, Raj A, Nimo O, Boadi E, Kpobi JM, Edzie RA, Osei B, Turkson V, Kusodzi H. Perspectives of radiologists in Ghana about the emerging role of artificial intelligence in radiology. Heliyon 2023; 9:e15558. [PMID: 37153404 PMCID: PMC10160753 DOI: 10.1016/j.heliyon.2023.e15558] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 04/12/2023] [Accepted: 04/13/2023] [Indexed: 05/09/2023] Open
Abstract
Background The integration of Artificial Intelligence (AI)-based technologies in medicine is advancing rapidly especially in the field of radiology. This however, is at a slow pace in Africa, hence, this study to evaluate the perspectives of Ghanaian radiologists. Methods Data for this cross-sectional prospective study was collected between September and November 2021 through an online survey and entered into SPSS for analysis. A Mann-Whitney U test assisted in checking for possible gender differences in the mean Likert scale responses on the radiologists' perspectives about AI in radiology. Statistical significance was set at P ≤ 0.05. Results The study comprised 77 radiologists, with more males (71.4%). 97.4% were aware of the concept of AI, with their initial exposure via conferences (42.9%). The majority of the respondents had average awareness (36.4%) and below average expertise (44.2%) in radiological AI usage. Most of the participants (54.5%) stated, they do not use AI in their practices. The respondents disagreed that AI will ultimately replace radiologists in the near future (average Likert score = 3.49, SD = 1.096) and that AI should be an integral part of the training of radiologists (average Likert score = 1.91, SD = 0.830). Conclusion Although the radiologists had positive opinions about the capabilities of AI, they exhibited an average awareness of and below average expertise in the usage of AI applications in radiology. They agreed on the potential life changing impact of AI and were of the view that AI will not replace radiologists but serve as a complement. There was inadequate radiological AI infrastructure in Ghana.
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Affiliation(s)
- Emmanuel Kobina Mesi Edzie
- Department of Medical Imaging, School of Medical Sciences, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana
- Corresponding author.
| | - Klenam Dzefi-Tettey
- Department of Radiology, Korle Bu Teaching Hospital, 1 Guggisberg Avenue, Accra, Ghana
| | - Abdul Raman Asemah
- Department of Medical Imaging, School of Medical Sciences, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana
| | | | - Samuel Asiamah
- Department of Radiology, Korle Bu Teaching Hospital, 1 Guggisberg Avenue, Accra, Ghana
| | - Frank Quarshie
- African Institute for Mathematical Sciences (AIMS), Summerhill Estate, East Legon Hills, Santoe, Accra, Ghana
| | - Adu Tutu Amankwa
- Department of Radiology, School of Medical Sciences, College of Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Amrit Raj
- Department of Pediatrics, School of Medical Sciences, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Obed Nimo
- Department of Imaging Technology and Sonography, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Evans Boadi
- Department of Radiology, Korle Bu Teaching Hospital, 1 Guggisberg Avenue, Accra, Ghana
| | - Joshua Mensah Kpobi
- Department of Radiology, Korle Bu Teaching Hospital, 1 Guggisberg Avenue, Accra, Ghana
| | - Richard Ato Edzie
- Department of Medical Imaging, School of Medical Sciences, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Bernard Osei
- African Institute for Mathematical Sciences (AIMS), Summerhill Estate, East Legon Hills, Santoe, Accra, Ghana
| | - Veronica Turkson
- Department of Medical Imaging, School of Medical Sciences, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Henry Kusodzi
- Department of Medical Imaging, School of Medical Sciences, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana
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Laborie LB, Naidoo J, Pace E, Ciet P, Eade C, Wagner MW, Huisman TAGM, Shelmerdine SC. European Society of Paediatric Radiology Artificial Intelligence taskforce: a new taskforce for the digital age. Pediatr Radiol 2023; 53:576-580. [PMID: 35731260 PMCID: PMC9214669 DOI: 10.1007/s00247-022-05426-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/26/2022] [Accepted: 06/03/2022] [Indexed: 11/08/2022]
Abstract
A new task force dedicated to artificial intelligence (AI) with respect to paediatric radiology was created in 2021 at the International Paediatric Radiology (IPR) meeting in Rome, Italy (a joint society meeting by the European Society of Pediatric Radiology [ESPR] and the Society for Pediatric Radiology [SPR]). The concept of a separate task force dedicated to AI was borne from an ESPR-led international survey of health care professionals' opinions, expectations and concerns regarding AI integration within children's imaging departments. In this survey, the majority (> 80%) of ESPR respondents supported the creation of a task force and helped define our key objectives. These include providing educational content about AI relevant for paediatric radiologists, brainstorming ideas for future projects and collaborating on AI-related studies with respect to collating data sets, de-identifying images and engaging in multi-case, multi-reader studies. This manuscript outlines the starting point of the ESPR AI task force and where we wish to go.
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Affiliation(s)
- Lene Bjerke Laborie
- grid.412008.f0000 0000 9753 1393Department of Radiology, Section for Paediatrics, Haukeland University Hospital, Bergen, Norway
- grid.7914.b0000 0004 1936 7443Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Jaishree Naidoo
- Paediatric Diagnostic Imaging and Envisionit Deep AI, Johannesburg, South Africa
| | - Erika Pace
- grid.5072.00000 0001 0304 893XDepartment of Diagnostic Radiology, The Royal Marsden NHS Foundation Trust, London, UK
| | - Pierluigi Ciet
- grid.5645.2000000040459992XDepartment of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- grid.5645.2000000040459992XDepartment of Pediatric Pulmonology and Allergology, Erasmus MC, Sophia’s Children’s Hospital, Rotterdam, The Netherlands
| | - Christine Eade
- grid.8391.30000 0004 1936 8024University of Exeter Medical School, Exeter, UK
| | - Matthias W. Wagner
- grid.42327.300000 0004 0473 9646Department of Diagnostic Imaging, Division of Neuroradiology, The Hospital for Sick Children, Toronto, Canada
- grid.17063.330000 0001 2157 2938Department of Medical Imaging, University of Toronto, Toronto, Ontario Canada
| | - Thierry A. G. M. Huisman
- grid.39382.330000 0001 2160 926XEdward B. Singleton Department of Radiology, Texas Children’s Hospital, Baylor College of Medicine, Houston, Texas USA
| | - Susan C. Shelmerdine
- grid.424537.30000 0004 5902 9895Department of Clinical Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, Great Ormond Street, WC1H 3JH London, UK
- grid.83440.3b0000000121901201UCL Great Ormond Street Institute of Child Health, London, UK
- grid.451056.30000 0001 2116 3923NIHR Great Ormond Street Hospital Biomedical Research Centre, London, UK
- grid.464688.00000 0001 2300 7844Department of Clinical Radiology, St. George’s Hospital, London, UK
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Alsultan K. Awareness of Artificial Intelligence in Medical Imaging Among Radiologists and Radiologic Technologists. Cureus 2023; 15:e38325. [PMID: 37261164 PMCID: PMC10228162 DOI: 10.7759/cureus.38325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/30/2023] [Indexed: 06/02/2023] Open
Abstract
BACKGROUND Current technological developments in medical imaging are primarily focused on increasing the integration of artificial intelligence (AI) into all medical imaging modalities. They are already considered capable of handling tasks such as image reconstruction, processing (denoising, segmentation), analysis, and predictive modeling. The purpose of this study is to assess the awareness (knowledge, attitudes, and practices) of radiologists and radiologic technologists regarding AI in medical imaging. MATERIALS AND METHODS This cross-sectional, qualitative study focuses on radiologists and radiologic technologists in Saudi Arabia, Sudan, and Yemen. A self-administered questionnaire based on published studies was used to collect primary data. Version 25.0 of IBM SPSS Statistics (IBM Corp., Armonk, NY) was used for the statistical analysis. The demographics were summarized as frequency and percentage. Independent samples t-tests and ANOVA tests were used to evaluate and compare the degree of AI awareness among the study groups. RESULTS A total of 210 individuals completed the survey. According to demographic information, there were 134 (63.8%) radiologic technologists and 76 radiologists (36.2%). Of the participants, 131 (62%) were male, while 79 (37.6%) were female. A total of 130 (61.9%) of the targeted respondents had a positive attitude, 105 (50%) had appropriate practice, and 122 (58.1%) of them were informed (knowledgeable) about AI in medical imaging. There was a significant difference in knowledge awareness between radiologists and radiologic technologists (p-value: <0.05). Radiologists were more knowledgeable than radiologic technologists, and females were more knowledgeable than males (p-value: 0.049). For attitude awareness, there were no significant differences regarding specialization, gender, age, academic qualification, and experience (p-value > 0.05). Regarding practice awareness, it turned out that females are more knowledgeable than males (p-value: 0.007). Additionally, it was discovered that significant differences indicated that bachelor's degree holders have a higher level of practice awareness than diploma holders (p-value: <0.05). CONCLUSION Significant differences between the respondent's knowledge awareness regarding specialization, gender, and experience are linked with relatively sufficient AI-basic knowledge and positive attitude awareness among radiologists and radiologic technologists. Only half of the study participants had appropriate practical awareness; therefore, additional training could enhance practical awareness.
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Affiliation(s)
- Kamal Alsultan
- Department of Diagnostic Radiology Technology, College of Applied Medical Sciences, Taibah University, Medina, SAU
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Hashmi OU, Chan N, de Vries CF, Gangi A, Jehanli L, Lip G. Artificial intelligence in radiology: trainees want more. Clin Radiol 2023; 78:e336-e341. [PMID: 36746724 DOI: 10.1016/j.crad.2022.12.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 11/08/2022] [Accepted: 12/28/2022] [Indexed: 01/20/2023]
Abstract
AIM To understand the attitudes of UK radiology trainees towards artificial intelligence (AI) in Radiology, in particular, assessing the demand for AI education. MATERIALS AND METHODS A survey, which ran over a period of 2 months, was created using the Google Forms platform and distributed via email to all UK training programmes. RESULTS The survey was completed by 149 trainee radiologists with at least one response from all UK training programmes. Of the responses, 83.7% were interested in AI use in radiology but 71.4% had no experience of working with AI and 79.9% would like to be involved in AI-based projects. Almost all (98.7%) felt that AI should be taught during their training, yet only one respondent stated that their training programme had implemented AI teaching. Respondents indicated that basic understanding, implementation, and critical appraisal of AI software should be prioritized in teaching. Of the trainees, 74.2% agreed that AI would enhance the job of diagnostic radiologists over the next 20 years. The main concerns raised were information technology/implementation and ethical/regulatory issues. CONCLUSION Despite the current limited availability of AI-based activities and teaching within UK training programmes, UK trainees' attitudes towards AI are mostly positive with many showing interest in being involved with AI-based projects, activities, and teaching.
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Affiliation(s)
- O-U Hashmi
- East of England Imaging Academy, The Cotman Centre, Norfolk and Norwich University Hospital, Norwich, NR4 7UB, UK.
| | - N Chan
- Department of Interventional Neuroradiology, The Royal London Hospital, Whitechapel Road, London, UK
| | - C F de Vries
- Aberdeen Centre for Health Data Science, Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - A Gangi
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospital NHS Foundation Trust, Cambridge, UK
| | - L Jehanli
- North West School of Radiology, Manchester, UK
| | - G Lip
- National Health Service Grampian (NHSG), Aberdeen Royal Infirmary, Aberdeen, UK
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Önder M, Evli C, Türk E, Kazan O, Bayrakdar İŞ, Çelik Ö, Costa ALF, Gomes JPP, Ogawa CM, Jagtap R, Orhan K. Deep-Learning-Based Automatic Segmentation of Parotid Gland on Computed Tomography Images. Diagnostics (Basel) 2023; 13:diagnostics13040581. [PMID: 36832069 PMCID: PMC9955422 DOI: 10.3390/diagnostics13040581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/23/2023] [Accepted: 02/02/2023] [Indexed: 02/08/2023] Open
Abstract
This study aims to develop an algorithm for the automatic segmentation of the parotid gland on CT images of the head and neck using U-Net architecture and to evaluate the model's performance. In this retrospective study, a total of 30 anonymized CT volumes of the head and neck were sliced into 931 axial images of the parotid glands. Ground truth labeling was performed with the CranioCatch Annotation Tool (CranioCatch, Eskisehir, Turkey) by two oral and maxillofacial radiologists. The images were resized to 512 × 512 and split into training (80%), validation (10%), and testing (10%) subgroups. A deep convolutional neural network model was developed using U-net architecture. The automatic segmentation performance was evaluated in terms of the F1-score, precision, sensitivity, and the Area Under Curve (AUC) statistics. The threshold for a successful segmentation was determined by the intersection of over 50% of the pixels with the ground truth. The F1-score, precision, and sensitivity of the AI model in segmenting the parotid glands in the axial CT slices were found to be 1. The AUC value was 0.96. This study has shown that it is possible to use AI models based on deep learning to automatically segment the parotid gland on axial CT images.
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Affiliation(s)
- Merve Önder
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06000, Turkey
| | - Cengiz Evli
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06000, Turkey
| | - Ezgi Türk
- Dentomaxillofacial Radiology, Oral and Dental Health Center, Hatay 31040, Turkey
| | - Orhan Kazan
- Health Services Vocational School, Gazi University, Ankara 06560, Turkey
| | - İbrahim Şevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir 26040, Turkey
- Eskisehir Osmangazi University Center of Research and Application for Computer-Aided Diagnosis and Treatment in Health, Eskişehir 26040, Turkey
- Division of Oral and Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, MS 39216, USA
| | - Özer Çelik
- Eskisehir Osmangazi University Center of Research and Application for Computer-Aided Diagnosis and Treatment in Health, Eskişehir 26040, Turkey
- Department of Mathematics-Computer, Faculty of Science, Eskisehir Osmangazi University, Eskişehir 26040, Turkey
| | - Andre Luiz Ferreira Costa
- Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo 01506-000, SP, Brazil
| | - João Pedro Perez Gomes
- Department of Stomatology, Division of General Pathology, School of Dentistry, University of São Paulo (USP), São Paulo 13560-970, SP, Brazil
| | - Celso Massahiro Ogawa
- Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo 01506-000, SP, Brazil
| | - Rohan Jagtap
- Division of Oral and Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, MS 39216, USA
| | - Kaan Orhan
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06000, Turkey
- Department of Dental and Maxillofacial Radiodiagnostics, Medical University of Lublin, 20-093 Lublin, Poland
- Ankara University Medical Design Application and Research Center (MEDITAM), Ankara 06000, Turkey
- Correspondence: ; Tel.: +48-81-448-50-00
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Kedar S, Khazanchi D. Neurology education in the era of artificial intelligence. Curr Opin Neurol 2023; 36:51-58. [PMID: 36367213 DOI: 10.1097/wco.0000000000001130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
PURPOSE OF REVIEW The practice of neurology is undergoing a paradigm shift because of advances in the field of data science, artificial intelligence, and machine learning. To ensure a smooth transition, physicians must have the knowledge and competence to apply these technologies in clinical practice. In this review, we describe physician perception and preparedness, as well as current state for clinical applications of artificial intelligence and machine learning in neurology. RECENT FINDINGS Digital health including artificial intelligence-based/machine learning-based technology has made significant inroads into various aspects of healthcare including neurological care. Surveys of physicians and healthcare stakeholders suggests an overall positive perception about the benefits of artificial intelligence/machine learning in clinical practice. This positive perception is tempered by concerns for lack of knowledge and limited opportunities to build competence in artificial intelligence/machine learning technology. Literature about neurologist's perception and preparedness towards artificial intelligence/machine learning-based technology is scant. There are very few opportunities for physicians particularly neurologists to learn about artificial intelligence/machine learning-based technology. SUMMARY Neurologists have not been surveyed about their perception and preparedness to adopt artificial intelligence/machine learning-based technology in clinical practice. We propose development of a practical artificial intelligence/machine learning curriculum to enhance neurologists' competence in these newer technologies.
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Affiliation(s)
- Sachin Kedar
- Department of Ophthalmology
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia
| | - Deepak Khazanchi
- Department of Information Systems & Quantitative Analysis, College of Information Science and Technology, University of Nebraska at Omaha, Omaha, Nebraska, USA
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Silkens MEWM, Ross J, Hall M, Scarbrough H, Rockall A. The time is now: making the case for a UK registry of deployment of radiology artificial intelligence applications. Clin Radiol 2023; 78:107-114. [PMID: 36639171 DOI: 10.1016/j.crad.2022.09.132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 09/02/2022] [Accepted: 09/06/2022] [Indexed: 01/12/2023]
Abstract
Artificial intelligence (AI)-based healthcare applications (apps) are rapidly evolving, and radiology is a target specialty for their implementation. In this paper, we put the case for a national deployment registry to track the spread of AI apps into clinical use in radiology in the UK. By gathering data on the specific locations, purposes, and people associated with AI app deployment, such a registry would provide greater transparency on their spread in the radiology field. In combination with other regulatory and audit mechanisms, it would provide radiologists and patients with greater confidence and trust in AI apps. At the same time, coordination of this information would reduce costs for the National Health Service (NHS) by preventing duplication of piloting activities. This commentary discusses the need for a UK-wide registry for such apps, its benefits and risks, and critical success factors for its establishment. We conclude by noting that a critical window of opportunity has opened up for the development of a deployment registry, before the current pattern of localised clusters of activity turns into the widespread proliferation of AI apps across clinical practice.
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Affiliation(s)
- M E W M Silkens
- Centre for Healthcare Innovation Research, City University of London, London, UK.
| | - J Ross
- Department of Cancer and Surgery, Imperial College London, London, UK
| | - M Hall
- Queen Elizabeth University Hospital, Glasgow, UK
| | - H Scarbrough
- Centre for Healthcare Innovation Research, City University of London, London, UK
| | - A Rockall
- Department of Cancer and Surgery, Imperial College London, London, UK
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Marquis KM, Hoegger MJ, Shetty AS, Bishop GL, Balthazar P, Gould JE, Ballard DH. Results of the 2020 Survey of the American Alliance of Academic Chief Residents in Radiology. Clin Imaging 2023; 98:67-73. [PMID: 37023549 DOI: 10.1016/j.clinimag.2023.02.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/17/2022] [Accepted: 02/10/2023] [Indexed: 02/21/2023]
Abstract
RATIONALE AND OBJECTIVES An annual survey of chief residents in accredited North American radiology programs is conducted by the American Alliance of Academic Chief Residents in Radiology (A3CR2). The purpose of this study is to summarize the 2020 A3CR2 chief resident survey. MATERIALS AND METHODS An online survey was distributed to chief residents from 194 Accreditation Council on Graduate Medical Education-accredited radiology residencies. Questions were designed to gather information about residency program practices, benefits, fellowship or advanced interventional radiology (IR) training choices, and the integration of IR training. Subsets of questions focused on the perception of corporatization, non-physician providers (NPPs), and artificial intelligence (AI) in radiology and their relationship to the radiology job market. RESULTS 174 individual responses from 94 programs were provided, yielding a 48 % program response rate. Extended emergency department coverage has steadily decreased over the last 5 years (2016-2020), however only 52 % of programs have independent overnight call (without attending coverage). Regarding the impact of new integrated IR residencies on training, 42 % indicated there was no appreciable impact on their DR or IR training, while 20 % indicated DR training for IR residents suffered and 19 % indicated IR training for DR residents suffered. Corporatization in radiology was perceived as the biggest potential threat to the future job market. CONCLUSIONS Integration of IR residency did not detrimentally affect DR or IR training in most programs. Radiology resident perception of corporatization, NPPs, and AI may help residency programs shape educational content.
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Affiliation(s)
- Kaitlin M Marquis
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Mark J Hoegger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Anup S Shetty
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Grace L Bishop
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
| | - Patricia Balthazar
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Jennifer E Gould
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - David H Ballard
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
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Amann J, Vayena E, Ormond KE, Frey D, Madai VI, Blasimme A. Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke. PLoS One 2023; 18:e0279088. [PMID: 36630325 PMCID: PMC9833517 DOI: 10.1371/journal.pone.0279088] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 11/01/2022] [Indexed: 01/12/2023] Open
Abstract
INTRODUCTION Artificial intelligence (AI) has the potential to transform clinical decision-making as we know it. Powered by sophisticated machine learning algorithms, clinical decision support systems (CDSS) can generate unprecedented amounts of predictive information about individuals' health. Yet, despite the potential of these systems to promote proactive decision-making and improve health outcomes, their utility and impact remain poorly understood due to their still rare application in clinical practice. Taking the example of AI-powered CDSS in stroke medicine as a case in point, this paper provides a nuanced account of stroke survivors', family members', and healthcare professionals' expectations and attitudes towards medical AI. METHODS We followed a qualitative research design informed by the sociology of expectations, which recognizes the generative role of individuals' expectations in shaping scientific and technological change. Semi-structured interviews were conducted with stroke survivors, family members, and healthcare professionals specialized in stroke based in Germany and Switzerland. Data was analyzed using a combination of inductive and deductive thematic analysis. RESULTS Based on the participants' deliberations, we identified four presumed roles that medical AI could play in stroke medicine, including an administrative, assistive, advisory, and autonomous role AI. While most participants held positive attitudes towards medical AI and its potential to increase accuracy, speed, and efficiency in medical decision making, they also cautioned that it is not a stand-alone solution and may even lead to new problems. Participants particularly emphasized the importance of relational aspects and raised questions regarding the impact of AI on roles and responsibilities and patients' rights to information and decision-making. These findings shed light on the potential impact of medical AI on professional identities, role perceptions, and the doctor-patient relationship. CONCLUSION Our findings highlight the need for a more differentiated approach to identifying and tackling pertinent ethical and legal issues in the context of medical AI. We advocate for stakeholder and public involvement in the development of AI and AI governance to ensure that medical AI offers solutions to the most pressing challenges patients and clinicians face in clinical care.
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Affiliation(s)
- Julia Amann
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Effy Vayena
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Kelly E. Ormond
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Dietmar Frey
- CLAIM—Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Vince I. Madai
- CLAIM—Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- QUEST Center for Responsible Research, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Berlin, Germany
- School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Birmingham, United Kingdom
| | - Alessandro Blasimme
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
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Abstract
In the era of artificial intelligence (AI), a great deal of attention is being paid to AI in radiological practice. There are a large number of AI products on the radiological market based on X-rays, computed tomography, magnetic resonance imaging, and ultrasound. AI will not only change the way of radiological practice but also the way of radiological education. It is still not clearly defined about the exact role AI will play in radiological practice, but it will certainly be consolidated into radiological education in the foreseeable future. However, there are few literatures that have comprehensively summarized the attitudes, opportunities and challenges that AI can pose in the different training phases of radiologists, from university education to continuing education. Herein, we describe medical students' attitudes towards AI, summarize the role of AI in radiological education, and analyze the challenges that AI can pose in radiological education.
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Affiliation(s)
- Chao Wang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- * Correspondence: Chao Wang, Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88 Jiefang Road, Hangzhou 310009 Zhejiang, China (e-mail: )
| | - Huanhuan Xie
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Shan Wang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Siyu Yang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ling Hu
- Department of Ultrasound, Hangzhou Women’s Hospital, Hangzhou, Zhejiang, China
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Lindqwister AL, Hassanpour S, Levy J, Sin JM. AI-RADS: Successes and challenges of a novel artificial intelligence curriculum for radiologists across different delivery formats. FRONTIERS IN MEDICAL TECHNOLOGY 2023; 4:1007708. [PMID: 36688145 PMCID: PMC9845918 DOI: 10.3389/fmedt.2022.1007708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 11/18/2022] [Indexed: 01/06/2023] Open
Abstract
Introduction Artificial intelligence and data-driven predictive modeling have become increasingly common tools integrated in clinical practice, heralding a new chapter of medicine in the digital era. While these techniques are poised to affect nearly all aspects of medicine, medical education as an institution has languished behind; this has raised concerns that the current training infrastructure is not adequately preparing future physicians for this changing clinical landscape. Our institution attempted to ameliorate this by implementing a novel artificial intelligence in radiology curriculum, "AI-RADS," in two different educational formats: a 7-month lecture series and a one-day workshop intensive. Methods The curriculum was structured around foundational algorithms within artificial intelligence. As most residents have little computer science training, algorithms were initially presented as a series of simple observations around a relatable problem (e.g., fraud detection, movie recommendations, etc.). These observations were later re-framed to illustrate how a machine could apply the underlying concepts to perform clinically relevant tasks in the practice of radiology. Secondary lessons in basic computing, such as data representation/abstraction, were integrated as well. The lessons were ordered such that these algorithms were logical extensions of each other. The 7-month curriculum consisted of seven lectures paired with seven journal clubs, resulting in an AI-focused session every two weeks. The workshop consisted of six hours of content modified for the condensed format, with a final integrative activity. Results Both formats of the AI-RADS curriculum were well received by learners, with the 7-month version and workshop garnering 9.8/10 and 4.3/5 ratings, respectively, for overall satisfaction. In both, there were increases in perceived understanding of artificial intelligence. In the 7-lecture course, 6/7 lectures achieved statistically significant (P < 0.02) differences, with the final lecture approaching significance (P = 0.07). In the one-day workshop, there was a significant increase in perceived understanding (P = 0.03). Conclusion As artificial intelligence becomes further enmeshed in clinical practice, it will become critical for physicians to have a basic understanding of how these tools work. Our AI-RADS curriculum demonstrates that it is successful in increasing learner perceived understanding in both an extended and condensed format.
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Affiliation(s)
- Alexander L. Lindqwister
- Department of Internal Medicine, California Pacific Medical Center, San Francisco, CA, United States,Correspondence: Alexander Lindqwister
| | - Saeed Hassanpour
- Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, NH, United States
| | - Joshua Levy
- Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, NH, United States,Department of Dermatology, Dartmouth Health, Lebanon, NH, United States
| | - Jessica M. Sin
- Department of Radiology, Dartmouth Health, Lebanon, NH, United States
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Artificial intelligence in emergency radiology: A review of applications and possibilities. Diagn Interv Imaging 2023; 104:6-10. [PMID: 35933269 DOI: 10.1016/j.diii.2022.07.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 07/23/2022] [Indexed: 01/10/2023]
Abstract
Artificial intelligence (AI) applications in radiology have been rising exponentially in the last decade. Although AI has found usage in various areas of healthcare, its utilization in the emergency department (ED) as a tool for emergency radiologists shows great promise towards easing some of the challenges faced daily. There have been numerous reported studies examining the application of AI-based algorithms in identifying common ED conditions to ensure more rapid reporting and in turn quicker patient care. In addition to interpretive applications, AI assists with many of the non-interpretive tasks that are encountered every day by emergency radiologists. These include, but are not limited to, protocolling, image quality control and workflow prioritization. AI continues to face challenges such as physician uptake or costs, but is a long-term investment that shows great potential to relieve many difficulties faced by emergency radiologists and ultimately improve patient outcomes. This review sums up the current advances of AI in emergency radiology, including current diagnostic applications (interpretive) and applications that stretch beyond imaging (non-interpretive), analyzes current drawbacks of AI in emergency radiology and discusses future challenges.
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Tejani AS, Elhalawani H, Moy L, Kohli M, Kahn CE. Artificial Intelligence and Radiology Education. Radiol Artif Intell 2023; 5:e220084. [PMID: 36721409 PMCID: PMC9885376 DOI: 10.1148/ryai.220084] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 09/18/2022] [Accepted: 11/02/2022] [Indexed: 06/18/2023]
Abstract
Implementation of artificial intelligence (AI) applications into clinical practice requires AI-savvy radiologists to ensure the safe, ethical, and effective use of these systems for patient care. Increasing demand for AI education reflects recognition of the translation of AI applications from research to clinical practice, with positive trainee attitudes regarding the influence of AI on radiology. However, barriers to AI education, such as limited access to resources, predispose to insufficient preparation for the effective use of AI in practice. In response, national organizations have sponsored formal and self-directed learning courses to provide introductory content on imaging informatics and AI. Foundational courses, such as the National Imaging Informatics Course - Radiology and the Radiological Society of North America Imaging AI Certificate, lay a framework for trainees to explore the creation, deployment, and critical evaluation of AI applications. This report includes additional resources for formal programming courses, video series from leading organizations, and blogs from AI and informatics communities. Furthermore, the scope of "AI and radiology education" includes AI-augmented radiology education, with emphasis on the potential for "precision education" that creates personalized experiences for trainees by accounting for varying learning styles and inconsistent, possibly deficient, clinical case volume. © RSNA, 2022 Keywords: Use of AI in Education, Impact of AI on Education, Artificial Intelligence, Medical Education, Imaging Informatics, Natural Language Processing, Precision Education.
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Carniel T, Halloy J, Dalle JM. A novel clustering approach to bipartite investor-startup networks. PLoS One 2023; 18:e0279780. [PMID: 36602981 PMCID: PMC9815571 DOI: 10.1371/journal.pone.0279780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 12/14/2022] [Indexed: 01/06/2023] Open
Abstract
We propose a novel similarity-based clustering approach to venture capital investors that takes as input the bipartite graph of funding interactions between investors and startups and returns clusterings of investors built upon 5 characteristic dimensions. We first validate that investors are clustered in a meaningful manner and present methods of visualizing cluster characteristics. We further analyze the temporal dynamics at the cluster level and observe a meaningful second-order evolution of the sectoral investment trends. Finally, and surprisingly, we report that clusters appear stable even when running the clustering algorithm with all but one of the 5 characteristic dimensions, for instance observing geography-focused clusters without taking into account the geographical dimension or sector-focused clusters without taking into account the sectoral dimension, suggesting the presence of significant underlying complex investment patterns.
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Affiliation(s)
- Théophile Carniel
- Agoranov, Paris, France
- Université Paris Cité, CNRS, LIED UMR 8236, Paris, France
- * E-mail:
| | - José Halloy
- Université Paris Cité, CNRS, LIED UMR 8236, Paris, France
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de Margerie-Mellon C, Chassagnon G. Artificial intelligence: A critical review of applications for lung nodule and lung cancer. Diagn Interv Imaging 2023; 104:11-17. [PMID: 36513593 DOI: 10.1016/j.diii.2022.11.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is a broad concept that usually refers to computer programs that can learn from data and perform certain specific tasks. In the recent years, the growth of deep learning, a successful technique for computer vision tasks that does not require explicit programming, coupled with the availability of large imaging databases fostered the development of multiple applications in the medical imaging field, especially for lung nodules and lung cancer, mostly through convolutional neural networks (CNN). Some of the first applications of AI is this field were dedicated to automated detection of lung nodules on X-ray and computed tomography (CT) examinations, with performances now reaching or exceeding those of radiologists. For lung nodule segmentation, CNN-based algorithms applied to CT images show excellent spatial overlap index with manual segmentation, even for irregular and ground glass nodules. A third application of AI is the classification of lung nodules between malignant and benign, which could limit the number of follow-up CT examinations for less suspicious lesions. Several algorithms have demonstrated excellent capabilities for the prediction of the malignancy risk when a nodule is discovered. These different applications of AI for lung nodules are particularly appealing in the context of lung cancer screening. In the field of lung cancer, AI tools applied to lung imaging have been investigated for distinct aims. First, they could play a role for the non-invasive characterization of tumors, especially for histological subtype and somatic mutation predictions, with a potential therapeutic impact. Additionally, they could help predict the patient prognosis, in combination to clinical data. Despite these encouraging perspectives, clinical implementation of AI tools is only beginning because of the lack of generalizability of published studies, of an inner obscure working and because of limited data about the impact of such tools on the radiologists' decision and on the patient outcome. Radiologists must be active participants in the process of evaluating AI tools, as such tools could support their daily work and offer them more time for high added value tasks.
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Affiliation(s)
- Constance de Margerie-Mellon
- Université Paris Cité, Laboratory of Imaging Biomarkers, Center for Research on Inflammation, UMR 1149, INSERM, 75018 Paris, France; Department of Radiology, Hôpital Saint-Louis APHP, 75010 Paris, France
| | - Guillaume Chassagnon
- Université Paris Cité, Faculté de Médecine, 75006 Paris, France; Department of Radiology, Hôpital Cochin APHP, 75014 Paris, France
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Santos GNM, da Silva HEC, Figueiredo PTDS, Mesquita CRM, Melo NS, Stefani CM, Leite AF. The Introduction of Artificial Intelligence in Diagnostic Radiology Curricula: a Text and Opinion Systematic Review. INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION 2022. [DOI: 10.1007/s40593-022-00324-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Caparrós Galán G, Sendra Portero F. Medical students’ perceptions of the impact of artificial intelligence in radiology. RADIOLOGIA 2022; 64:516-524. [DOI: 10.1016/j.rxeng.2021.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 03/17/2021] [Indexed: 11/18/2022]
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Santomartino SM, Yi PH. Systematic Review of Radiologist and Medical Student Attitudes on the Role and Impact of AI in Radiology. Acad Radiol 2022; 29:1748-1756. [PMID: 35105524 DOI: 10.1016/j.acra.2021.12.032] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/30/2021] [Accepted: 12/30/2021] [Indexed: 12/28/2022]
Abstract
RATIONALE AND OBJECTIVES The introduction of AI in radiology has prompted both excitement and hesitation within the field. We performed a systematic review of original studies evaluating the attitudes of radiologists, radiology trainees, and medical students towards AI in radiology. MATERIALS AND METHODS We searched PubMed for studies published as of August 24, 2021 for original studies evaluating attitudes of radiologists (attendings and trainees) and medical students towards AI in radiology. We summarized the baseline article characteristics and performed thematic analysis of the questions asked in each study. RESULTS Nineteen studies were included evaluating attitudes across different levels of training (medical students, radiology trainees, and radiology attendings) with representation from nearly every continent. Medical students and radiologists alike favored increased educational initiatives, and displayed interest in learning about and implementing AI solutions themselves, despite reporting of a current gap in formal AI training. There was general optimism about the role of AI in radiology, although radiologists and trainees had greater consensus than medical students. CONCLUSION Although there is interest in incorporating AI into medical education and optimism among radiologists towards AI, medical students are more divided in their views. We propose that outreach to and AI education for medical students may help improve their attitudes towards the potentially transformative technology of AI for radiology.
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Affiliation(s)
- Samantha M Santomartino
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Paul H Yi
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, Maryland; Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland.
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van der Zander QEW, van der Ende-van Loon MCM, Janssen JMM, Winkens B, van der Sommen F, Masclee AAM, Schoon EJ. Artificial intelligence in (gastrointestinal) healthcare: patients' and physicians' perspectives. Sci Rep 2022; 12:16779. [PMID: 36202957 PMCID: PMC9537305 DOI: 10.1038/s41598-022-20958-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/21/2022] [Indexed: 12/01/2022] Open
Abstract
Artificial intelligence (AI) is entering into daily life and has the potential to play a significant role in healthcare. Aim was to investigate the perspectives (knowledge, experience, and opinion) on AI in healthcare among patients with gastrointestinal (GI) disorders, gastroenterologists, and GI-fellows. In this prospective questionnaire study 377 GI-patients, 35 gastroenterologists, and 45 GI-fellows participated. Of GI-patients, 62.5% reported to be familiar with AI and 25.0% of GI-physicians had work-related experience with AI. GI-patients preferred their physicians to use AI (mean 3.9) and GI-physicians were willing to use AI (mean 4.4, on 5-point Likert-scale). More GI-physicians believed in an increase in quality of care (81.3%) than GI-patients (64.9%, χ2(2) = 8.2, p = 0.017). GI-fellows expected AI implementation within 6.0 years, gastroenterologists within 4.2 years (t(76) = − 2.6, p = 0.011), and GI-patients within 6.1 years (t(193) = − 2.0, p = 0.047). GI-patients and GI-physicians agreed on the most important advantages of AI in healthcare: improving quality of care, time saving, and faster diagnostics and shorter waiting times. The most important disadvantage for GI-patients was the potential loss of personal contact, for GI-physicians this was insufficiently developed IT infrastructures. GI-patients and GI-physicians hold positive perspectives towards AI in healthcare. Patients were significantly more reserved compared to GI-fellows and GI-fellows were more reserved compared to gastroenterologists.
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Affiliation(s)
- Quirine E W van der Zander
- Division of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht, The Netherlands. .,GROW, School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.
| | | | - Janneke M M Janssen
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Bjorn Winkens
- Department of Methodology and Statistics, Maastricht University, Maastricht, The Netherlands.,CAPHRI, Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Ad A M Masclee
- Division of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Erik J Schoon
- GROW, School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.,Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
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Chen M, Zhang B, Cai Z, Seery S, Gonzalez MJ, Ali NM, Ren R, Qiao Y, Xue P, Jiang Y. Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey. Front Med (Lausanne) 2022; 9:990604. [PMID: 36117979 PMCID: PMC9472134 DOI: 10.3389/fmed.2022.990604] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background Artificial intelligence (AI) needs to be accepted and understood by physicians and medical students, but few have systematically assessed their attitudes. We investigated clinical AI acceptance among physicians and medical students around the world to provide implementation guidance. Materials and methods We conducted a two-stage study, involving a foundational systematic review of physician and medical student acceptance of clinical AI. This enabled us to design a suitable web-based questionnaire which was then distributed among practitioners and trainees around the world. Results Sixty studies were included in this systematic review, and 758 respondents from 39 countries completed the online questionnaire. Five (62.50%) of eight studies reported 65% or higher awareness regarding the application of clinical AI. Although, only 10–30% had actually used AI and 26 (74.28%) of 35 studies suggested there was a lack of AI knowledge. Our questionnaire uncovered 38% awareness rate and 20% utility rate of clinical AI, although 53% lacked basic knowledge of clinical AI. Forty-five studies mentioned attitudes toward clinical AI, and over 60% from 38 (84.44%) studies were positive about AI, although they were also concerned about the potential for unpredictable, incorrect results. Seventy-seven percent were optimistic about the prospect of clinical AI. The support rate for the statement that AI could replace physicians ranged from 6 to 78% across 40 studies which mentioned this topic. Five studies recommended that efforts should be made to increase collaboration. Our questionnaire showed 68% disagreed that AI would become a surrogate physician, but believed it should assist in clinical decision-making. Participants with different identities, experience and from different countries hold similar but subtly different attitudes. Conclusion Most physicians and medical students appear aware of the increasing application of clinical AI, but lack practical experience and related knowledge. Overall, participants have positive but reserved attitudes about AI. In spite of the mixed opinions around clinical AI becoming a surrogate physician, there was a consensus that collaborations between the two should be strengthened. Further education should be conducted to alleviate anxieties associated with change and adopting new technologies.
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Affiliation(s)
- Mingyang Chen
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ziting Cai
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Samuel Seery
- Faculty of Health and Medicine, Division of Health Research, Lancaster University, Lancaster, United Kingdom
| | | | - Nasra M. Ali
- The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Ran Ren
- Global Health Research Center, Dalian Medical University, Dalian, China
| | - Youlin Qiao
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Youlin Qiao,
| | - Peng Xue
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Peng Xue,
| | - Yu Jiang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Yu Jiang,
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Coakley S, Young R, Moore N, England A, O'Mahony A, O'Connor OJ, Maher M, McEntee MF. Radiographers' knowledge, attitudes and expectations of artificial intelligence in medical imaging. Radiography (Lond) 2022; 28:943-948. [PMID: 35839662 DOI: 10.1016/j.radi.2022.06.020] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 06/21/2022] [Accepted: 06/24/2022] [Indexed: 11/29/2022]
Abstract
INTRODUCTION Artificial intelligence (AI) is increasingly utilised in medical imaging systems and processes, and radiographers must embrace this advancement. This study aimed to investigate perceptions, knowledge, and expectations towards integrating AI into medical imaging amongst a sample of radiographers and determine the current state of AI education within the community. METHODS A cross-sectional online quantitative study targeting radiographers based in Europe was conducted over ten weeks. Captured data included demographical information, participants' perceptions and understanding of AI, expectations of AI and AI-related educational backgrounds. Both descriptive and inferential statistical techniques were used to analyse the obtained data. RESULTS A total of 96 valid responses were collected. Of these, 64% correctly identified the true definition of AI from a range of options, but fewer (37%) fully understood the difference between AI, machine learning and deep learning. The majority of participants (83%) agreed they were excited about the advancement of AI, though a level of apprehensiveness remained amongst 29%. A severe lack of education on AI was noted, with only 8% of participants having received AI teachings in their pre-registration qualification. CONCLUSION Overall positive attitudes towards AI implementation were observed. The slight apprehension may stem from the lack of technical understanding of AI technologies and AI training within the community. Greater educational programs focusing on AI principles are required to help increase European radiography workforce engagement and involvement in AI technologies. IMPLICATIONS FOR PRACTICE This study offers insight into the current perspectives of European based radiographers on AI in radiography to help facilitate the embracement of AI technology and convey the need for AI-focused education within the profession.
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Affiliation(s)
- S Coakley
- Discipline of Medical Imaging and Radiation Therapy, School of Medicine, University College Cork, Ireland
| | - R Young
- Discipline of Medical Imaging and Radiation Therapy, School of Medicine, University College Cork, Ireland
| | - N Moore
- Discipline of Medical Imaging and Radiation Therapy, School of Medicine, University College Cork, Ireland
| | - A England
- Discipline of Medical Imaging and Radiation Therapy, School of Medicine, University College Cork, Ireland.
| | - A O'Mahony
- Department of Radiology, Cork University Hospital, Ireland
| | - O J O'Connor
- Department of Radiology, Cork University Hospital, Ireland
| | - M Maher
- Department of Radiology, Cork University Hospital, Ireland
| | - M F McEntee
- Discipline of Medical Imaging and Radiation Therapy, School of Medicine, University College Cork, Ireland
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Rainey C, O'Regan T, Matthew J, Skelton E, Woznitza N, Chu KY, Goodman S, McConnell J, Hughes C, Bond R, Malamateniou C, McFadden S. UK reporting radiographers' perceptions of AI in radiographic image interpretation - Current perspectives and future developments. Radiography (Lond) 2022; 28:881-888. [PMID: 35780627 DOI: 10.1016/j.radi.2022.06.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/07/2022] [Accepted: 06/13/2022] [Indexed: 02/03/2023]
Abstract
INTRODUCTION Radiographer reporting is accepted practice in the UK. With a national shortage of radiographers and radiologists, artificial intelligence (AI) support in reporting may help minimise the backlog of unreported images. Modern AI is not well understood by human end-users. This may have ethical implications and impact human trust in these systems, due to over- and under-reliance. This study investigates the perceptions of reporting radiographers about AI, gathers information to explain how they may interact with AI in future and identifies features perceived as necessary for appropriate trust in these systems. METHODS A Qualtrics® survey was designed and piloted by a team of UK AI expert radiographers. This paper reports the third part of the survey, open to reporting radiographers only. RESULTS 86 responses were received. Respondents were confident in how an AI reached its decision (n = 53, 62%). Less than a third of respondents would be confident communicating the AI decision to stakeholders. Affirmation from AI would improve confidence (n = 49, 57%) and disagreement would make respondents seek a second opinion (n = 60, 70%). There is a moderate trust level in AI for image interpretation. System performance data and AI visual explanations would increase trust. CONCLUSIONS Responses indicate that AI will have a strong impact on reporting radiographers' decision making in the future. Respondents are confident in how an AI makes decisions but less confident explaining this to others. Trust levels could be improved with explainable AI solutions. IMPLICATIONS FOR PRACTICE This survey clarifies UK reporting radiographers' perceptions of AI, used for image interpretation, highlighting key issues with AI integration.
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Affiliation(s)
- C Rainey
- Ulster University, School of Health Sciences, Faculty of Life and Health Sciences, Shore Road, Newtownabbey, N. Ireland.
| | - T O'Regan
- The Society and College of Radiographers, 207 Providence Square, Mill Street, London, UK
| | - J Matthew
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - E Skelton
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK; Department of Radiography, Division of Midwifery and Radiography, School of Health Sciences, City, University of London, London, UK
| | - N Woznitza
- University College London Hospitals, Bloomsbury, London, UK; School of Allied & Public Health Professions, Canterbury Christ Church University, Canterbury, UK
| | - K-Y Chu
- Department of Oncology, Oxford Institute for Radiation Oncology, University of Oxford, Oxford, UK; Radiotherapy Department, Churchill Hospital, Oxford University Hospitals NHS FT, Oxford, UK
| | - S Goodman
- The Society and College of Radiographers, 207 Providence Square, Mill Street, London, UK
| | | | - C Hughes
- Ulster University, School of Health Sciences, Faculty of Life and Health Sciences, Shore Road, Newtownabbey, N. Ireland
| | - R Bond
- Ulster University, School of Computing, Faculty of Computing, Engineering and the Built Environment, Shore Road, Newtownabbey, N. Ireland
| | - C Malamateniou
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK; Department of Radiography, Division of Midwifery and Radiography, School of Health Sciences, City, University of London, London, UK
| | - S McFadden
- Ulster University, School of Health Sciences, Faculty of Life and Health Sciences, Shore Road, Newtownabbey, N. Ireland
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Saw SN, Ng KH. Current challenges of implementing artificial intelligence in medical imaging. Phys Med 2022; 100:12-17. [PMID: 35714523 DOI: 10.1016/j.ejmp.2022.06.003] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 04/26/2022] [Accepted: 06/11/2022] [Indexed: 12/31/2022] Open
Abstract
The idea of using artificial intelligence (AI) in medical practice has gained vast interest due to its potential to revolutionise healthcare systems. However, only some AI algorithms are utilised due to systems' uncertainties, besides the never-ending list of ethical and legal concerns. This paper intends to provide an overview of current AI challenges in medical imaging with an ultimate aim to foster better and effective communication among various stakeholders to encourage AI technology development. We identify four main challenges in implementing AI in medical imaging, supported with consequences and past events when these problems fail to mitigate. Among them is the creation of a robust AI algorithm that is fair, trustable and transparent. Another issue is on data governance, in which best practices in data sharing must be established to promote trust and protect the patients' privacy. Next, stakeholders, such as the government, technology companies and hospital management, should come to a consensus in creating trustworthy AI policies and regulatory frameworks, which is the fourth challenge, to support, encourage and spur innovation in digital AI healthcare technology. Lastly, we discussed the efforts of various organizations such as the World Health Organisation (WHO), American College of Radiology (ACR), European Society of Radiology (ESR) and Radiological Society of North America (RSNA), who are already actively pursuing ethical developments in AI. The efforts by various stakeholders will eventually overcome hurdles and the deployment of AI-driven healthcare applications in clinical practice will become a reality and hence lead to better healthcare services and outcomes.
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Affiliation(s)
- Shier Nee Saw
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Kwan Hoong Ng
- Department of Biomedical Imaging, Universiti Malaya, 50603 Kuala Lumpur, Malaysia; Department of Medical Imaging and Radiological Sciences, College of Health Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
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Tulk Jesso S, Kelliher A, Sanghavi H, Martin T, Henrickson Parker S. Inclusion of Clinicians in the Development and Evaluation of Clinical Artificial Intelligence Tools: A Systematic Literature Review. Front Psychol 2022; 13:830345. [PMID: 35465567 PMCID: PMC9022040 DOI: 10.3389/fpsyg.2022.830345] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 02/09/2022] [Indexed: 12/11/2022] Open
Abstract
The application of machine learning (ML) and artificial intelligence (AI) in healthcare domains has received much attention in recent years, yet significant questions remain about how these new tools integrate into frontline user workflow, and how their design will impact implementation. Lack of acceptance among clinicians is a major barrier to the translation of healthcare innovations into clinical practice. In this systematic review, we examine when and how clinicians are consulted about their needs and desires for clinical AI tools. Forty-five articles met criteria for inclusion, of which 24 were considered design studies. The design studies used a variety of methods to solicit and gather user feedback, with interviews, surveys, and user evaluations. Our findings show that tool designers consult clinicians at various but inconsistent points during the design process, and most typically at later stages in the design cycle (82%, 19/24 design studies). We also observed a smaller amount of studies adopting a human-centered approach and where clinician input was solicited throughout the design process (22%, 5/24). A third (15/45) of all studies reported on clinician trust in clinical AI algorithms and tools. The surveyed articles did not universally report validation against the “gold standard” of clinical expertise or provide detailed descriptions of the algorithms or computational methods used in their work. To realize the full potential of AI tools within healthcare settings, our review suggests there are opportunities to more thoroughly integrate frontline users’ needs and feedback in the design process.
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Affiliation(s)
- Stephanie Tulk Jesso
- Fralin Biomedical Research Institute, Virginia Tech, Roanoke, VA, United States.,Institute for Creativity, Arts, and Technology, Blacksburg, VA, United States
| | - Aisling Kelliher
- Department of Computer Science, College of Engineering, Virginia Tech, Blacksburg, VA, United States
| | | | - Thomas Martin
- Institute for Creativity, Arts, and Technology, Blacksburg, VA, United States.,Department of Electrical and Computer Engineering, College of Engineering, Virginia Tech, Blacksburg, VA, United States
| | - Sarah Henrickson Parker
- Fralin Biomedical Research Institute, Virginia Tech, Roanoke, VA, United States.,Department of Health Systems and Implementation Science, Virginia Tech Carilion School of Medicine, Roanoke, VA, United States
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Mulryan P, Ni Chleirigh N, O'Mahony AT, Crowley C, Ryan D, McLaughlin P, McEntee M, Maher M, O'Connor OJ. An evaluation of information online on artificial intelligence in medical imaging. Insights Imaging 2022; 13:79. [PMID: 35467250 PMCID: PMC9038977 DOI: 10.1186/s13244-022-01209-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 03/12/2022] [Indexed: 08/24/2023] Open
Abstract
Background Opinions seem somewhat divided when considering the effect of artificial intelligence (AI) on medical imaging. The aim of this study was to characterise viewpoints presented online relating to the impact of AI on the field of radiology and to assess who is engaging in this discourse.
Methods Two search methods were used to identify online information relating to AI and radiology. Firstly, 34 terms were searched using Google and the first two pages of results for each term were evaluated. Secondly, a Rich Search Site (RSS) feed evaluated incidental information over 3 weeks. Webpages were evaluated and categorized as having a positive, negative, balanced, or neutral viewpoint based on study criteria. Results Of the 680 webpages identified using the Google search engine, 248 were deemed relevant and accessible. 43.2% had a positive viewpoint, 38.3% a balanced viewpoint, 15.3% a neutral viewpoint, and 3.2% a negative viewpoint. Peer-reviewed journals represented the most common webpage source (48%), followed by media (29%), commercial sources (12%), and educational sources (8%). Commercial webpages had the highest proportion of positive viewpoints (66%). Radiologists were identified as the most common author group (38.9%). The RSS feed identified 177 posts of which were relevant and accessible. 86% of posts were of media origin expressing positive viewpoints (64%). Conclusion The overall opinion of the impact of AI on radiology presented online is a positive one. Consistency across a range of sources and author groups exists. Radiologists were significant contributors to this online discussion and the results may impact future recruitment. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01209-4.
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Affiliation(s)
- Philip Mulryan
- Cork University Hospital/Mercy University Hospital, Cork, Ireland
| | | | | | - Claire Crowley
- Cork University Hospital/Mercy University Hospital, Cork, Ireland
| | | | | | | | - Michael Maher
- Cork University Hospital/Mercy University Hospital, Cork, Ireland.,University College Cork, Cork, Ireland
| | - Owen J O'Connor
- Cork University Hospital/Mercy University Hospital, Cork, Ireland.,University College Cork, Cork, Ireland
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Ahmed Z, Bhinder KK, Tariq A, Tahir MJ, Mehmood Q, Tabassum MS, Malik M, Aslam S, Asghar MS, Yousaf Z. Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Pakistan: A cross-sectional online survey. Ann Med Surg (Lond) 2022; 76:103493. [PMID: 35308436 PMCID: PMC8928127 DOI: 10.1016/j.amsu.2022.103493] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/05/2022] [Accepted: 03/06/2022] [Indexed: 11/27/2022] Open
Abstract
Background The use of Artificial intelligence (AI) has gained popularity during the last few decades and its use in medicine is increasing globally. Developing countries like Pakistan are lagging in the implementation of AI-based solutions in healthcare. There is a need to incorporate AI in the health system which may help not only in expediting diagnosis and management but also injudicious resource allocation. Objective To determine the knowledge, attitude, and practice of AI among doctors and medical students in Pakistan. Materials and methods We conducted a cross-sectional study using an online questionnaire-based survey regarding demographic details, knowledge, perception, and practice of AI. A sample of 470 individuals including doctors and medical students were selected using the convenient sampling technique. The chi-square test was applied for the comparison of variables. Results Out of 470 individuals, 223(47.45%) were doctors and 247(52.55%) were medical students. Among these, 165(74%) doctors and 170(68.8%) medical students had a basic knowledge of AI but only 61(27.3%) doctors and 48(19.4%) students were aware of its medical applications. Regarding attitude, 237(76.7%) individuals supported AI's inclusion in curriculum, 368(78.3%) and 305(64.9%), 281(59.8%) and 269(57.2%) acknowledged its necessity in radiology, pathology, and COVID-19 pandemic respectively. Conclusion The majority of doctors and medical students lack knowledge about AI and its applications, but had a positive view of AI in the field of medicine and were willing to adopt it. The majority of doctors and medical students lack knowledge about AI and its applications. Developing countries like Pakistan are lagging in the implementation of AI-based solutions in healthcare.
There is a need to incorporate AI in the health system which may help in expediting diagnosis, management and injudicious resource allocation. More resources need to be allocated for the planning and implementation of AI in the medical curriculum.
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50
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The current state of knowledge on imaging informatics: a survey among Spanish radiologists. Insights Imaging 2022; 13:34. [PMID: 35235068 PMCID: PMC8891400 DOI: 10.1186/s13244-022-01164-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 01/22/2022] [Indexed: 11/22/2022] Open
Abstract
Background There is growing concern about the impact of artificial intelligence (AI) on radiology and the future of the profession. The aim of this study is to evaluate general knowledge and concerns about trends on imaging informatics among radiologists working in Spain (residents and attending physicians). For this purpose, an online survey among radiologists working in Spain was conducted with questions related to: knowledge about terminology and technologies, need for a regulated academic training on AI and concerns about the implications of the use of these technologies. Results A total of 223 radiologists answered the survey, of whom 76.7% were attending physicians and 23.3% residents. General terms such as AI and algorithm had been heard of or read in at least 75.8% and 57.4% of the cases, respectively, while more specific terms were scarcely known. All the respondents consider that they should pursue academic training in medical informatics and new technologies, and 92.9% of them reckon this preparation should be incorporated in the training program of the specialty. Patient safety was found to be the main concern for 54.2% of the respondents. Job loss was not seen as a peril by 45.7% of the participants.
Conclusions Although there is a lack of knowledge about AI among Spanish radiologists, there is a will to explore such topics and a general belief that radiologists should be trained in these matters. Based on the results, a consensus is needed to change the current training curriculum to better prepare future radiologists.
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