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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] [MESH Headings] [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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Chang JY, Makary MS. Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging. Diagnostics (Basel) 2024; 14:1456. [PMID: 39001346 PMCID: PMC11240935 DOI: 10.3390/diagnostics14131456] [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: 05/30/2024] [Revised: 07/01/2024] [Accepted: 07/06/2024] [Indexed: 07/16/2024] Open
Abstract
The advent of artificial intelligence (AI) is revolutionizing medicine, particularly radiology. With the development of newer models, AI applications are demonstrating improved performance and versatile utility in the clinical setting. Thoracic imaging is an area of profound interest, given the prevalence of chest imaging and the significant health implications of thoracic diseases. This review aims to highlight the promising applications of AI within thoracic imaging. It examines the role of AI, including its contributions to improving diagnostic evaluation and interpretation, enhancing workflow, and aiding in invasive procedures. Next, it further highlights the current challenges and limitations faced by AI, such as the necessity of 'big data', ethical and legal considerations, and bias in representation. Lastly, it explores the potential directions for the application of AI in thoracic radiology.
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Affiliation(s)
- Jin Y Chang
- Department of Radiology, The Ohio State University College of Medicine, Columbus, OH 43210, USA
| | - Mina S Makary
- Department of Radiology, The Ohio State University College of Medicine, Columbus, OH 43210, USA
- Division of Vascular and Interventional Radiology, Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
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Kjelle E, Brandsæter IØ, Andersen ER, Hofmann B. Sustainability in healthcare by reducing low-value imaging - A narrative review. Radiography (Lond) 2024; 30 Suppl 1:30-34. [PMID: 38870571 DOI: 10.1016/j.radi.2024.05.014] [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: 03/22/2024] [Revised: 05/23/2024] [Accepted: 05/27/2024] [Indexed: 06/15/2024]
Abstract
OBJECTIVES This narrative review aims to present the concept of value in imaging and explore why we conduct low-value procedures, how to reduce this wasteful use, and what we could gain from reducing low-value imaging. KEY FINDINGS Imaging of low value to the patient contributes to thousands of metric tons of CO2 emissions, costing several billion US dollars annually. With a 20% reduction in low-value imaging, we would reduce the waste of resources related to 7.2 million procedures and, at the same time, reduce the risk of incidentalomas, overdiagnosis, and overtreatment and reduce wait times for patients in need of imaging services of high value. Multi-component initiatives targeting barriers in all levels of society and healthcare are needed to reduce low-value imaging. Radiographers are key actors in medical imaging and can make substantial contributions to this effort by, together with the radiologists, referrers, and managers, ensuring that all imaging procedures conducted are sustainable along four dimensions of sustainability: value, cost, risk, and environment. CONCLUSION Efforts to secure sustainable imaging considering the four crucial dimensions (value, cost, radiation, and environment) should be made at all levels of society and healthcare, from governmental management to the individual healthcare worker. Radiographers are vital in obtaining sustainability to ensure only sustainable imaging procedures are conducted. IMPLICATIONS FOR PRACTICE When assessing the appropriateness of imaging procedures, we need to consider the environment, safety, effectiveness, and efficiency. To obtain this, we need a collective and coordinated effort locally, nationally, and internationally to deliver sustainable imaging services.
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Affiliation(s)
- E Kjelle
- Department of Health Sciences at the Norwegian University of Science and Technology (NTNU) at Gjøvik, Postbox 191, 2802 Gjøvik Norway; Department of Optometry, Radiography, and Lighting Design at the University of South-Eastern Norway (USN) at Drammen, Post Office Box 4, 3199 Borre, Norway.
| | - I Ø Brandsæter
- Department of Health Sciences at the Norwegian University of Science and Technology (NTNU) at Gjøvik, Postbox 191, 2802 Gjøvik Norway
| | - E R Andersen
- Department of Health Sciences at the Norwegian University of Science and Technology (NTNU) at Gjøvik, Postbox 191, 2802 Gjøvik Norway
| | - B Hofmann
- Department of Health Sciences at the Norwegian University of Science and Technology (NTNU) at Gjøvik, Postbox 191, 2802 Gjøvik Norway; Centre of Medical Ethics at the University of Oslo, Centre of Medical Ethics, Postbox 1130, Blindern, 0318 Oslo, Norway
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Toxopeus R, Kasalak Ö, Yakar D, Noordzij W, Dierckx RAJO, Kwee TC. Is work overload associated with diagnostic errors on 18F-FDG-PET/CT? Eur J Nucl Med Mol Imaging 2024; 51:1079-1084. [PMID: 38030745 DOI: 10.1007/s00259-023-06543-3] [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: 09/29/2023] [Accepted: 11/22/2023] [Indexed: 12/01/2023]
Abstract
PURPOSE To determine the association between workload and diagnostic errors on 18F-FDG-PET/CT. MATERIALS AND METHODS This study included 103 18F-FDG-PET/CT scans with a diagnostic error that was corrected with an addendum between March 2018 and July 2023. All scans were performed at a tertiary care center. The workload of each nuclear medicine physician or radiologist who authorized the 18F-FDG-PET/CT report was determined on the day the diagnostic error was made and normalized for his or her own average daily production (workloadnormalized). A workloadnormalized of more than 100% indicates that the nuclear medicine physician or radiologist had a relative work overload, while a value of less than 100% indicates a relative work underload on the day the diagnostic error was made. The time of the day the diagnostic error was made was also recorded. Workloadnormalized was compared to 100% using a signed rank sum test, with the hypothesis that it would significantly exceed 100%. A Mann-Kendall test was performed to test the hypothesis that diagnostic errors would increase over the course of the day. RESULTS Workloadnormalized (median of 121%, interquartile range: 71 to 146%) on the days the diagnostic errors were made was significantly higher than 100% (P = 0.014). There was no significant upward trend in the frequency of diagnostic errors over the course of the day (Mann-Kendall tau = 0.05, P = 0.7294). CONCLUSION Work overload seems to be associated with diagnostic errors on 18F-FDG-PET/CT. Diagnostic errors were encountered throughout the entire working day, without any upward trend towards the end of the day.
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Affiliation(s)
- Romy Toxopeus
- Medical Imaging Center, Departments of Radiology and Nuclear Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Ömer Kasalak
- Medical Imaging Center, Departments of Radiology and Nuclear Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Derya Yakar
- Medical Imaging Center, Departments of Radiology and Nuclear Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Walter Noordzij
- Medical Imaging Center, Departments of Radiology and Nuclear Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Rudi A J O Dierckx
- Medical Imaging Center, Departments of Radiology and Nuclear Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Thomas C Kwee
- Medical Imaging Center, Departments of Radiology and Nuclear Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
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Hathaway QA. Evolving Diagnostic Interpretation: How Automated Algorithms for Autosomal Dominant Polycystic Kidney Disease (ADPKD) Address Inter-Reader Variability and Physician Burnout. Acad Radiol 2024; 31:900-901. [PMID: 38368162 DOI: 10.1016/j.acra.2024.01.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 01/30/2024] [Indexed: 02/19/2024]
Affiliation(s)
- Quincy A Hathaway
- West Virginia University School of Medicine, Department of Medical Education, 1 Medical Center Drive, Morgantown, West Virginia 26505, USA; Department of Radiology and Radiologic Sciences, Johns Hopkins University, Baltimore, Maryland, USA.
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Becker M. How to prepare for a bright future of radiology in Europe. Insights Imaging 2023; 14:168. [PMID: 37816908 PMCID: PMC10564684 DOI: 10.1186/s13244-023-01525-3] [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: 08/16/2023] [Accepted: 09/16/2023] [Indexed: 10/12/2023] Open
Abstract
Because artificial intelligence (AI)-powered algorithms allow automated image analysis in a growing number of diagnostic scenarios, some healthcare stakeholders have raised doubts about the future of the entire radiologic profession. Their view disregards not only the role of radiologists in the diagnostic service chain beyond reporting, but also the many multidisciplinary and patient-related consulting tasks for which radiologists are solicited. The time commitment for these non-reporting tasks is considerable but difficult to quantify and often impossible to fulfil considering the current mismatch between workload and workforce in many countries. Nonetheless, multidisciplinary, and patient-centred consulting activities could move up on radiologists' agendas as soon as AI-based tools can save time in daily routine. Although there are many reasons why AI will assist and not replace radiologists as imaging experts in the future, it is important to position the next generation of European radiologists in view of this expected trend. To ensure radiologists' personal professional recognition and fulfilment in multidisciplinary environments, the focus of training should go beyond diagnostic reporting, concentrating on clinical backgrounds, specific communication skills with referrers and patients, and integration of imaging findings with those of other disciplines. Close collaboration between the European Society of Radiology (ESR) and European national radiologic societies can help to achieve these goals. Although each adequate treatment begins with a correct diagnosis, many health politicians see radiologic procedures mainly as a cost factor. Radiologic research should, therefore, increasingly investigate the imaging impact on treatment and outcome rather than focusing mainly on technical improvements and diagnostic accuracy alone.Critical relevance statement Strategies are presented to prepare for a successful future of the radiologic profession in Europe, if AI-powered tools can alleviate the current reporting overload: engaging in multidisciplinary activities (clinical and integrative diagnostics), enhancing the value and recognition of radiologists' role through clinical expertise, focusing radiological research on the impact on diagnosis and outcome, and promoting patient-centred radiology by enhancing communication skills.Key points • AI-powered tools will not replace radiologists but hold promise to reduce the current reporting burden, enabling them to reinvest liberated time in multidisciplinary clinical and patient-related tasks.• The skills and resources for these tasks should be considered when recruiting and teaching the next generation of radiologists, when organising departments and planning staffing.• Communication skills will play an increasing role in both multidisciplinary activities and patient-centred radiology.• The value and importance of a correct and integrative diagnosis and the cost of an incorrect imaging diagnosis should be emphasised when discussing with non-medical stakeholders in healthcare.• The radiologic community in Europe should start now to prepare for a bright future of the profession for the benefit of patients and medical colleagues alike.
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Affiliation(s)
- Minerva Becker
- Unit of Head and Neck and Maxilofacial Radiology, Division of Radiology, Diagnostic Department, Geneva University Hospitals, University of Geneva, Rue Gabrielle Perret Gentil 4, Geneva 14, CH 1211, Switzerland.
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Kasalak Ö, Alnahwi H, Toxopeus R, Pennings JP, Yakar D, Kwee TC. Work overload and diagnostic errors in radiology. Eur J Radiol 2023; 167:111032. [PMID: 37579563 DOI: 10.1016/j.ejrad.2023.111032] [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: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/16/2023]
Abstract
PURPOSE To determine the association between workload and diagnostic errors on clinical CT scans. METHOD This retrospective study was performed at a tertiary care center and covered the period from January 2020 to March 2023. All clinical CT scans that contained an addendum describing a perceptual error (i.e. failure to detect an important abnormality) in the original report that was issued on office days between 7.30 a.m. and 18.00 p.m., were included. The workload of the involved radiologist on the day of the diagnostic error was calculated in terms of relative value units, and normalized for the known average daily production of each individual radiologist (workloadnormalized). A workloadnormalized of less than 100% indicates relative work underload, while a workloadnormalized of > 100% indicates relative work overload in terms of reported examinations on an individual radiologist's basis. RESULTS A total of 49 diagnostic errors were included. Top-five locations of diagnostic errors were lung (n = 8), bone (n = 8), lymph nodes (n = 5), peritoneum (n = 5), and liver (n = 4). Workloadnormalized on the days the diagnostic errors were made was on average 121% (95% confidence interval: 106% to 136%), which was significantly higher than 100% (P = 0.008). There was no significant upward monotonic trend in diagnostic errors over the course of the day (Mann-Kendall tau of 0.005, P = 1.000), and there were no other notable temporal trends either. CONCLUSIONS Radiologists appear to have a relative work overload when they make a diagnostic error on CT. Diagnostic errors occurred throughout the entire day, without any increase towards the end of the day.
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Affiliation(s)
- Ömer Kasalak
- Medical Imaging Center, Department of Radiology, University Medical Center Groningen, University of Groningen, the Netherlands.
| | - Haider Alnahwi
- Medical Imaging Center, Department of Radiology, University Medical Center Groningen, University of Groningen, the Netherlands
| | - Romy Toxopeus
- Medical Imaging Center, Department of Radiology, University Medical Center Groningen, University of Groningen, the Netherlands
| | - Jan P Pennings
- Medical Imaging Center, Department of Radiology, University Medical Center Groningen, University of Groningen, the Netherlands
| | - Derya Yakar
- Medical Imaging Center, Department of Radiology, University Medical Center Groningen, University of Groningen, the Netherlands
| | - Thomas C Kwee
- Medical Imaging Center, Department of Radiology, University Medical Center Groningen, University of Groningen, the Netherlands
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