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Hammoudi Halat D, Shami R, Daud A, Sami W, Soltani A, Malki A. Artificial Intelligence Readiness, Perceptions, and Educational Needs Among Dental Students: A Cross-Sectional Study. Clin Exp Dent Res 2024; 10:e925. [PMID: 38970241 PMCID: PMC11226543 DOI: 10.1002/cre2.925] [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: 04/24/2024] [Revised: 06/10/2024] [Accepted: 06/11/2024] [Indexed: 07/08/2024] Open
Abstract
OBJECTIVES With Artificial Intelligence (AI) profoundly affecting education, ensuring that students in health disciplines are ready to embrace AI is essential for their future workforce integration. This study aims to explore dental students' readiness to use AI, perceptions about AI in health education and healthcare, and their AI-related educational needs. MATERIAL AND METHODS A cross-sectional survey was conducted among dental students at the College of Dental Medicine, Qatar University. The survey assessed readiness for AI using the Medical Artificial Intelligence Readiness Scale (MAIRS). Students' perceptions of AI in healthcare and health education and their educational needs were also explored. RESULTS A total of 94 students responded to the survey. AI readiness scores were average (3.3 ± 0.64 out of 5); while participants appeared more ready for the vision and ethics domains of MAIRS, they showed less readiness regarding cognition and ability. Participants scored average on AI perceptions (3.35 ± 0.45 out of 5), with concerns regarding AI risks and disadvantages. They expressed a high need for knowledge and skills related to AI use in healthcare (84%), AI for health-related research (81.9%), and AI in radiology and imaging procedures (79.8%). Student readiness had a significant correlation with AI perceptions and perceived level of AI knowledge. CONCLUSIONS This is the first study in Qatar exploring dental students' AI readiness, perceptions, and educational needs regarding AI applications in education and healthcare. The perceived AI knowledge gaps could inform future curricular AI integration. Advancing AI skills and deepening AI comprehension can empower future dental professionals through anticipated advances in the AI-driven healthcare landscape.
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Affiliation(s)
| | - Rula Shami
- Department of Clinical Oral Health Sciences, College of Dental MedicineQU Health, Qatar UniversityDohaQatar
| | - Alaa Daud
- Department of Clinical Oral Health Sciences, College of Dental MedicineQU Health, Qatar UniversityDohaQatar
| | - Waqas Sami
- Department of Pre‐Clinical Affairs, College of NursingQU Health, Qatar UniversityDohaQatar
| | | | - Ahmed Malki
- Academic Quality DepartmentQU Health, Qatar UniversityDohaQatar
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Arif WM. Radiologic Technology Students' Perceptions on Adoption of Artificial Intelligence Technology in Radiology. Int J Gen Med 2024; 17:3129-3136. [PMID: 39049835 PMCID: PMC11268710 DOI: 10.2147/ijgm.s465944] [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: 04/06/2024] [Accepted: 07/11/2024] [Indexed: 07/27/2024] Open
Abstract
Study Purpose This study aims to analyze radiologic technology student's perceptions of artificial intelligence (AI) and its applications in radiology. Methods A quantitative cross-sectional survey was conducted. A pre-validated survey questionnaire with 17 items related to students perceptions of AI and its applications was used. The sample included radiologic technology students from three universities in Saudi Arabia. The survey was conducted online for several weeks, resulting in a sample of 280 radiologic technology students. Results Of the participants, 63.9% were aware of AI and its applications. T-tests revealed a statistically significant difference (p = 0.0471) between genders with male participants reflecting slightly higher AI awareness than female participants. Regarding the choice of radiology as specialization, 35% of the participants stated that they would not choose radiology, whereas 65% preferred it. Approximately 56% of the participants expressed concerns about the potential replacement of radiology technologists with AI, and 62.1% strongly agreed on the necessity of incorporating known ethical principles into AI. Conclusion The findings reflect a positive evaluation of the applications of this technology, which is attributed to its essential support role. However, tailored education and training programs are necessary to prepare future healthcare professionals for the increasing role of AI in medical sciences.
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Affiliation(s)
- Wejdan M Arif
- King Saud University, College of Applied Medical Sciences, Department of Radiological Sciences, Riyadh, Saudi Arabia
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Stogiannos N, Jennings M, George CS, Culbertson J, Salehi H, Furterer S, Pergola M, Culp MP, Malamateniou C. The American Society of Radiologic Technologists (ASRT) AI educator survey: A cross-sectional study to explore knowledge, experience, and use of AI within education. J Med Imaging Radiat Sci 2024; 55:101449. [PMID: 39004006 DOI: 10.1016/j.jmir.2024.101449] [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: 03/21/2024] [Revised: 05/09/2024] [Accepted: 06/04/2024] [Indexed: 07/16/2024]
Abstract
INTRODUCTION Artificial Intelligence (AI) is revolutionizing medical imaging and radiation therapy. AI-powered applications are being deployed to aid Medical Radiation Technologists (MRTs) in clinical workflows, decision-making, dose optimisation, and a wide range of other tasks. Exploring the levels of AI education provided across the United States is crucial to prepare future graduates to deliver the digital future. This study aims to assess educators' levels of AI knowledge, the current state of AI educational provisions, the perceived challenges around AI education, and important factors for future advancements. METHODS An online survey was electronically administered to all radiologic technologists in the American Society of Radiologic Technologists (ASRT) database who indicated that they had an educator role in the United States. This was distributed through the membership of the ASRT, from February to April 2023. All quantitative data was analysed using frequency and descriptive statistics. The survey's open-ended questions were analysed using a conceptual content analysis approach. RESULTS Out of 5,066 educators in the ASRT database, 373 valid responses were received, resulting in a response rate of 7.4%. Despite 84.5% of educators expressing the importance of teaching AI, 23.7% currently included AI in academic curricula. Of the 76.3% that did not include AI in their curricula, lack of AI knowledge among educators was the top reason for not integrating AI in education (59.1%). Similarly, AI-enabled tools were utilised by only 11.1% of the programs to assist teaching. The levels of trust in AI varied among educators. CONCLUSION The study found that although US educators of MRTs have a good baseline knowledge of general concepts regarding AI, they could improve on the teaching and use of AI in their curricula. AI training and guidance, adequate time to develop educational resources, and funding and support from higher education institutions were key priorities as highlighted by educators.
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Affiliation(s)
- Nikolaos Stogiannos
- Department of Midwifery & Radiography, School of Health and Psychological Sciences, City, University of London, UK; Magnitiki Tomografia Kerkiras, Corfu, Greece.
| | - Michael Jennings
- Senior Research Analyst, American Society of Radiologic Technologists, New Mexico, USA
| | - Craig St George
- Director of Education, American Society of Radiologic Technologists, New Mexico, USA
| | - John Culbertson
- Director of Research, American Society of Radiologic Technologists, New Mexico, USA
| | - Hugh Salehi
- Department of Biomedical Industrial & Human Factor Engineering, Wright State University, Ohio, USA
| | - Sandra Furterer
- Department of Integrated Systems Engineering, The Ohio State University, Ohio, USA
| | - Melissa Pergola
- Chief Executive Officer, American Society of Radiologic Technologists, New Mexico, USA
| | - Melissa P Culp
- Executive Vice President of Member Engagement, American Society of Radiologic Technologists, New Mexico, USA.
| | - Christina Malamateniou
- Department of Midwifery & Radiography, School of Health and Psychological Sciences, City, University of London, UK; Discipline of Medical Imaging and Radiation Therapy, College of Medicine and Health, University College Cork, Ireland; European Society of Medical Imaging Informatics, Vienna, Austria
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Pedersen MRV, Kusk MW, Lysdahlgaard S, Mork-Knudsen H, Malamateniou C, Jensen J. A Nordic survey on artificial intelligence in the radiography profession - Is the profession ready for a culture change? Radiography (Lond) 2024; 30:1106-1115. [PMID: 38781794 DOI: 10.1016/j.radi.2024.04.020] [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: 01/18/2024] [Revised: 04/12/2024] [Accepted: 04/22/2024] [Indexed: 05/25/2024]
Abstract
INTRODUCTION The impact of artificial intelligence (AI) on the radiography profession remains uncertain. Although AI has been increasingly used in clinical radiography, the perspectives of the radiography professionals in Nordic countries have yet to be examined. The primary aim was to examine views of Nordic radiographers 'on AI, with focus on perspectives, engagement, and knowledge of AI. METHODS Radiographers from Denmark, Norway, Sweden, Iceland, Greenland, and the Faroe Island were invited through social media platforms to participate in an online survey from March to June 2023. The survey encompassed 29-items and included 4 sections a) demographics, b) barriers and enablers on AI, c) perspectives and experiences of AI and d) knowledge of AI in radiography. Edgars Schein's model of organizational culture was employed to analyse Nordic radiographers' perspectives on AI. RESULTS Overall, a total of 421 respondents participated in the survey. A majority were positive/somewhat positive towards AI in radiography e.g., 77.9 % (n = 342) thought that AI would have a positive effect on the profession, and 26% thought that AI would reduce the administrative workload. Most radiographers agreed or strongly agreed that clinicians may have access to AI generated reports (76.8 %, n = 297). Nevertheless, a total of 86 (20.1%) agree or somewhat agreed that AI a potential risk for radiography. CONCLUSION Nordic radiographers are generally positive towards AI, yet uncertainties regarding its implementation persist. The findings underscore the importance of understanding these challenges for the responsible integration of AI systems. Carefully weighing the expected influence of AI against key incentives will support a seamless integration of AI for the benefit not just of the patients, but also of the radiography profession. IMPLICATIONS FOR PRACTICE Understanding incentives factors and barriers can help address uncertainties during implementation of AI in clinical practice.
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Affiliation(s)
- M R V Pedersen
- Department of Radiology, Vejle Hospital - Part of Lillebaelt Hospital, Vejle, Denmark; Department of Regional Health Research, University of Southern Denmark, Odense, Denmark; Discipline of Medical Imaging & Radiation Therapy, School of Medicine, University College Cork, Ireland.
| | - M W Kusk
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark; Department of Radiology and Nuclear Medicine, University Hospital of Southern Denmark, Esbjerg, Denmark; IRIS - Imaging Research Initiative Southwest, University Hospital of Southern Denmark, Esbjerg, Denmark; Radiography and Diagnostic Imaging, School of Medicine, University College Dublin, Dublin, Ireland
| | - S Lysdahlgaard
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark; Department of Radiology and Nuclear Medicine, University Hospital of Southern Denmark, Esbjerg, Denmark; IRIS - Imaging Research Initiative Southwest, University Hospital of Southern Denmark, Esbjerg, Denmark
| | - H Mork-Knudsen
- Department of Radiology, Haukeland University Hospital, Norway
| | - C Malamateniou
- Department of Radiography, Division of Midwifery and Radiography, School of Health and Psychological Sciences, University of London, UK; European Federation of Radiographer Societies, Churchilllaan 11, 3527 GV, Utrecht, the Netherlands
| | - J Jensen
- Research and Innovation Unit of Radiology, University Hospital of Southern Denmark, Odense Denmark; Department of Radiology, Odense University Hospital, Odense, Denmark
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Aldhafeeri FM. Navigating the ethical landscape of artificial intelligence in radiography: a cross-sectional study of radiographers' perspectives. BMC Med Ethics 2024; 25:52. [PMID: 38734602 PMCID: PMC11088142 DOI: 10.1186/s12910-024-01052-w] [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: 12/25/2023] [Accepted: 05/03/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND The integration of artificial intelligence (AI) in radiography presents transformative opportunities for diagnostic imaging and introduces complex ethical considerations. The aim of this cross-sectional study was to explore radiographers' perspectives on the ethical implications of AI in their field and identify key concerns and potential strategies for addressing them. METHODS A structured questionnaire was distributed to a diverse group of radiographers in Saudi Arabia. The questionnaire included items on ethical concerns related to AI, the perceived impact on clinical practice, and suggestions for ethical AI integration in radiography. The data were analyzed using quantitative and qualitative methods to capture a broad range of perspectives. RESULTS Three hundred eighty-eight radiographers responded and had varying levels of experience and specializations. Most (44.8%) participants were unfamiliar with the integration of AI into radiography. Approximately 32.9% of radiographers expressed uncertainty regarding the importance of transparency and explanatory capabilities in the AI systems used in radiology. Many (36.9%) participants indicated that they believed that AI systems used in radiology should be transparent and provide justifications for their decision-making procedures. A significant preponderance (44%) of respondents agreed that implementing AI in radiology may increase ethical dilemmas. However, 27.8%expressed uncertainty in recognizing and understanding the potential ethical issues that could arise from integrating AI in radiology. Of the respondents, 41.5% stated that the use of AI in radiology required establishing specific ethical guidelines. However, a significant percentage (28.9%) expressed the opposite opinion, arguing that utilizing AI in radiology does not require adherence to ethical standards. In contrast to the 46.6% of respondents voicing concerns about patient privacy over AI implementation, 41.5% of respondents did not have any such apprehensions. CONCLUSIONS This study revealed a complex ethical landscape in the integration of AI in radiography, characterized by enthusiasm and apprehension among professionals. It underscores the necessity for ethical frameworks, education, and policy development to guide the implementation of AI in radiography. These findings contribute to the ongoing discourse on AI in medical imaging and provide insights that can inform policymakers, educators, and practitioners in navigating the ethical challenges of AI adoption in healthcare.
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Affiliation(s)
- Faten Mane Aldhafeeri
- Collage of Applied Medical Sciences, University of Hafr Albatin, P.O.Box 31991, Hafr Albatin, Saudi Arabia.
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Daniyal M, Qureshi M, Marzo RR, Aljuaid M, Shahid D. Exploring clinical specialists' perspectives on the future role of AI: evaluating replacement perceptions, benefits, and drawbacks. BMC Health Serv Res 2024; 24:587. [PMID: 38725039 PMCID: PMC11080164 DOI: 10.1186/s12913-024-10928-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 03/29/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND OF STUDY Over the past few decades, the utilization of Artificial Intelligence (AI) has surged in popularity, and its application in the medical field is witnessing a global increase. Nevertheless, the implementation of AI-based healthcare solutions has been slow in developing nations like Pakistan. This unique study aims to assess the opinion of clinical specialists on the future replacement of AI, its associated benefits, and its drawbacks in form southern region of Pakistan. MATERIAL AND METHODS A cross-sectional selective study was conducted from 140 clinical specialists (Surgery = 24, Pathology = 31, Radiology = 35, Gynecology = 35, Pediatric = 17) from the neglected southern Punjab region of Pakistan. The study was analyzed using χ2 - the test of association and the nexus between different factors was examined by multinomial logistic regression. RESULTS Out of 140 respondents, 34 (24.3%) believed hospitals were ready for AI, while 81 (57.9%) disagreed. Additionally, 42(30.0%) were concerned about privacy violations, and 70(50%) feared AI could lead to unemployment. Specialists with less than 6 years of experience are more likely to embrace AI (p = 0.0327, OR = 3.184, 95% C.I; 0.262, 3.556) and those who firmly believe that AI knowledge will not replace their future tasks exhibit a lower likelihood of accepting AI (p = 0.015, OR = 0.235, 95% C.I: (0.073, 0.758). Clinical specialists who perceive AI as a technology that encompasses both drawbacks and benefits demonstrated a higher likelihood of accepting its adoption (p = 0.084, OR = 2.969, 95% C.I; 0.865, 5.187). CONCLUSION Clinical specialists have embraced AI as the future of the medical field while acknowledging concerns about privacy and unemployment.
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Affiliation(s)
- Muhammad Daniyal
- Department of Statistics, Faculty of Computing, Islamia University of Bahawalpur, Bahawalpur, Pakistan.
| | - Moiz Qureshi
- Government Degree College, TandoJam, Hyderabad, Sindh, Pakistan
| | - Roy Rillera Marzo
- Faculty of Humanities and Health Sciences, Curtin University, Malaysia, , Miri, Sarawak, Malaysia
- Jeffrey Cheah School of Medicine and Health Sciences, Global Public Health, Monash University Malaysia, Subang Jaya, Selangor, Malaysia
| | - Mohammed Aljuaid
- Department of Health Administration, College of Business Administration, King Saud University, Riyadh, Saudi Arabia
| | - Duaa Shahid
- Hult International Business School, 02141, Cambridge, MA, USA
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Pedersen MRV, Kusk MW, Lysdahlgaard S, Mork-Knudsen H, Malamateniou C, Jensen J. Nordic radiographers' and students' perspectives on artificial intelligence - A cross-sectional online survey. Radiography (Lond) 2024; 30:776-783. [PMID: 38461583 DOI: 10.1016/j.radi.2024.02.020] [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: 11/07/2023] [Revised: 01/17/2024] [Accepted: 02/26/2024] [Indexed: 03/12/2024]
Abstract
INTRODUCTION The integration of artificial intelligence (AI) into the domain of radiography holds substantial potential in various aspects including workflow efficiency, image processing, patient positioning, and quality assurance. The successful implementation of AI within a Radiology department necessitates the participation of key stakeholders, particularly radiographers. The study aimed to provide a comprehensive investigation about Nordic radiographers' perspectives and attitudes towards AI in radiography. METHODS An online 29-item survey was distributed via social media platforms to Nordic students and radiographers working in Denmark, Norway, Sweden, Iceland, Greenland, and the Faroe Islands including items on demographics, specialization, educational background, place of work and perspectives and knowledge on AI. The items were a mix of closed-type and scaled questions, with the option for free-text responses when relevant. RESULTS The survey received responses from all Nordic countries with 586 respondents, 26.8% males, 72.1% females, and 1.1% non-binary/self-defined or preferred not to say. The mean age was 37.2 with a standard deviation (SD) of ±12.1 years, and the mean number of years since qualification was 14.2 SD ± 10.3 years. A total of 43% (n = 254) of the respondents had not received any AI training in clinical practice. Whereas 13% (n = 76) had received AI during radiography undergrad training. A total of 77.9% (n = 412) expressed interest in pursuing AI education. The majority of respondents were aware of the potential use of AI (n = 485, 82.8%) and 39.1% (n = 204) had no reservations about AI. CONCLUSION Overall, this study found that Nordic radiographers have a positive attitude toward AI. Very limited training or education has been provided to the radiographers. Especially since 82.8% reports on plans to implement AI in clinical practice. In general, awareness of AI applications is high, but the educational level is low for Nordic radiographers. IMPLICATION FOR PRACTICE This study emphasises the favourable view of AI held by students and Nordic radiographers. However, there is a need for continuous professional development to facilitate the implementation and effective utilization of AI tools within the field of radiography.
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Affiliation(s)
- M R V Pedersen
- Department of Radiology, Vejle Hospital - Part of Lillebaelt Hospital, Vejle, Denmark; Department of Radiology, Kolding Hospital- Part of Lillebaelt Hospital, Kolding, Denmark; Department of Regional Health Research, University of Southern Denmark, Odense, Denmark; Discipline of Medical Imaging & Radiation Therapy, School of Medicine, University College Cork, Ireland.
| | - M W Kusk
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark; Department of Radiology and Nuclear Medicine, University Hospital of Southern Denmark, Esbjerg, Denmark; IRIS - Imaging Research Initiative Southwest, University Hospital of Southern Denmark, Esbjerg, Denmark; Radiography and Diagnostic Imaging, School of Medicine, University College Dublin, Dublin, Ireland
| | - S Lysdahlgaard
- Department of Regional Health Research, University of Southern Denmark, Odense, Denmark; Department of Radiology and Nuclear Medicine, University Hospital of Southern Denmark, Esbjerg, Denmark; IRIS - Imaging Research Initiative Southwest, University Hospital of Southern Denmark, Esbjerg, Denmark
| | - H Mork-Knudsen
- Department of Radiology, Haukeland University Hospital, Norway
| | - C Malamateniou
- Department of Radiography, Division of Midwifery and Radiography, School of Health and Psychological Sciences, City, University of London, UK; European Federation of Radiographer Societies, Churchilllaan 11, 3527 GV, Utrecht, the Netherlands
| | - J Jensen
- Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark; Department of Radiology, Odense University Hospital, Odense, Denmark
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Doherty G, McLaughlin L, Hughes C, McConnell J, Bond R, McFadden S. A scoping review of educational programmes on artificial intelligence (AI) available to medical imaging staff. Radiography (Lond) 2024; 30:474-482. [PMID: 38217933 DOI: 10.1016/j.radi.2023.12.019] [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: 10/26/2023] [Revised: 12/29/2023] [Accepted: 12/30/2023] [Indexed: 01/15/2024]
Abstract
INTRODUCTION Medical imaging is arguably the most technologically advanced field in healthcare, encompassing a range of technologies which continually evolve as computing power and human knowledge expand. Artificial Intelligence (AI) is the next frontier which medical imaging is pioneering. The rapid development and implementation of AI has the potential to revolutionise healthcare, however, to do so, staff must be competent and confident in its application, hence AI readiness is an important precursor to AI adoption. Research to ascertain the best way to deliver this AI-enabled healthcare training is in its infancy. The aim of this scoping review is to compare existing studies which investigate and evaluate the efficacy of AI educational interventions for medical imaging staff. METHODS Following the creation of a search strategy and keyword searches, screening was conducted to determine study eligibility. This consisted of a title and abstract scan, then subsequently a full-text review. Articles were included if they were empirical studies wherein an educational intervention on AI for medical imaging staff was created, delivered, and evaluated. RESULTS Of the initial 1309 records returned, n = 5 (∼0.4 %) of studies met the eligibility criteria of the review. The curricula and delivery in each of the five studies shared similar aims and a 'flipped classroom' delivery was the most utilised method. However, the depth of content covered in the curricula of each varied and measured outcomes differed greatly. CONCLUSION The findings of this review will provide insights into the evaluation of existing AI educational interventions, which will be valuable when planning AI education for healthcare staff. IMPLICATIONS FOR PRACTICE This review highlights the need for standardised and comprehensive AI training programs for imaging staff.
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Affiliation(s)
- G Doherty
- Ulster University, School of Health Sciences, Faculty of Life and Health Sciences, Shore Road, Newtownabbey, Northern Ireland, United Kingdom.
| | - L McLaughlin
- Ulster University, School of Health Sciences, Faculty of Life and Health Sciences, Shore Road, Newtownabbey, Northern Ireland, United Kingdom
| | - C Hughes
- Ulster University, School of Health Sciences, Faculty of Life and Health Sciences, Shore Road, Newtownabbey, Northern Ireland, United Kingdom
| | - J McConnell
- Leeds Teaching Hospitals NHS Trust, United Kingdom
| | - R Bond
- Ulster University, School of Computing, Faculty of Computing, Engineering and the Built Environment, Shore Road, Newtownabbey, Northern Ireland, United Kingdom
| | - S McFadden
- Ulster University, School of Health Sciences, Faculty of Life and Health Sciences, Shore Road, Newtownabbey, Northern Ireland, United Kingdom
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Lokaj B, Pugliese MT, Kinkel K, Lovis C, Schmid J. Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review. Eur Radiol 2024; 34:2096-2109. [PMID: 37658895 PMCID: PMC10873444 DOI: 10.1007/s00330-023-10181-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/07/2023] [Accepted: 07/10/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVE Although artificial intelligence (AI) has demonstrated promise in enhancing breast cancer diagnosis, the implementation of AI algorithms in clinical practice encounters various barriers. This scoping review aims to identify these barriers and facilitators to highlight key considerations for developing and implementing AI solutions in breast cancer imaging. METHOD A literature search was conducted from 2012 to 2022 in six databases (PubMed, Web of Science, CINHAL, Embase, IEEE, and ArXiv). The articles were included if some barriers and/or facilitators in the conception or implementation of AI in breast clinical imaging were described. We excluded research only focusing on performance, or with data not acquired in a clinical radiology setup and not involving real patients. RESULTS A total of 107 articles were included. We identified six major barriers related to data (B1), black box and trust (B2), algorithms and conception (B3), evaluation and validation (B4), legal, ethical, and economic issues (B5), and education (B6), and five major facilitators covering data (F1), clinical impact (F2), algorithms and conception (F3), evaluation and validation (F4), and education (F5). CONCLUSION This scoping review highlighted the need to carefully design, deploy, and evaluate AI solutions in clinical practice, involving all stakeholders to yield improvement in healthcare. CLINICAL RELEVANCE STATEMENT The identification of barriers and facilitators with suggested solutions can guide and inform future research, and stakeholders to improve the design and implementation of AI for breast cancer detection in clinical practice. KEY POINTS • Six major identified barriers were related to data; black-box and trust; algorithms and conception; evaluation and validation; legal, ethical, and economic issues; and education. • Five major identified facilitators were related to data, clinical impact, algorithms and conception, evaluation and validation, and education. • Coordinated implication of all stakeholders is required to improve breast cancer diagnosis with AI.
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Affiliation(s)
- Belinda Lokaj
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland.
- Faculty of Medicine, University of Geneva, Geneva, Switzerland.
- Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.
| | - Marie-Thérèse Pugliese
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland
| | - Karen Kinkel
- Réseau Hospitalier Neuchâtelois, Neuchâtel, Switzerland
| | - Christian Lovis
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland
| | - Jérôme Schmid
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland
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Stogiannos N, O'Regan T, Scurr E, Litosseliti L, Pogose M, Harvey H, Kumar A, Malik R, Barnes A, McEntee MF, Malamateniou C. AI implementation in the UK landscape: Knowledge of AI governance, perceived challenges and opportunities, and ways forward for radiographers. Radiography (Lond) 2024; 30:612-621. [PMID: 38325103 DOI: 10.1016/j.radi.2024.01.019] [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: 01/04/2024] [Accepted: 01/26/2024] [Indexed: 02/09/2024]
Abstract
INTRODUCTION Despite the rapid increase of AI-enabled applications deployed in clinical practice, many challenges exist around AI implementation, including the clarity of governance frameworks, usability of validation of AI models, and customisation of training for radiographers. This study aimed to explore the perceptions of diagnostic and therapeutic radiographers, with existing theoretical and/or practical knowledge of AI, on issues of relevance to the field, such as AI implementation, including knowledge of AI governance and procurement, perceptions about enablers and challenges and future priorities for AI adoption. METHODS An online survey was designed and distributed to UK-based qualified radiographers who work in medical imaging and/or radiotherapy and have some previous theoretical and/or practical knowledge of working with AI. Participants were recruited through the researchers' professional networks on social media with support from the AI advisory group of the Society and College of Radiographers. Survey questions related to AI training/education, knowledge of AI governance frameworks, data privacy procedures, AI implementation considerations, and priorities for AI adoption. Descriptive statistics were employed to analyse the data, and chi-square tests were used to explore significant relationships between variables. RESULTS In total, 88 valid responses were received. Most radiographers (56.6 %) had not received any AI-related training. Also, although approximately 63 % of them used an evaluation framework to assess AI models' performance before implementation, many (36.9 %) were still unsure about suitable evaluation methods. Radiographers requested clearer guidance on AI governance, ample time to implement AI in their practice safely, adequate funding, effective leadership, and targeted support from AI champions. AI training, robust governance frameworks, and patient and public involvement were seen as priorities for the successful implementation of AI by radiographers. CONCLUSION AI implementation is progressing within radiography, but without customised training, clearer governance, key stakeholder engagement and suitable new roles created, it will be hard to harness its benefits and minimise related risks. IMPLICATIONS FOR PRACTICE The results of this study highlight some of the priorities and challenges for radiographers in relation to AI adoption, namely the need for developing robust AI governance frameworks and providing optimal AI training.
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Affiliation(s)
- N Stogiannos
- Division of Midwifery & Radiography, City, University of London, UK; Medical Imaging Department, Corfu General Hospital, Greece.
| | - T O'Regan
- The Society and College of Radiographers, London, UK.
| | - E Scurr
- The Royal Marsden NHS Foundation Trust, UK.
| | - L Litosseliti
- School of Health & Psychological Sciences, City, University of London, UK.
| | - M Pogose
- Quality Assurance and Regulatory Affairs, Hardian Health, UK.
| | | | - A Kumar
- Frimley Health NHS Foundation Trust, UK.
| | - R Malik
- Bolton NHS Foundation Trust, UK.
| | - A Barnes
- King's Technology Evaluation Centre (KiTEC), School of Biomedical Engineering & Imaging Science, King's College London, UK.
| | - M F McEntee
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Ireland.
| | - C Malamateniou
- Division of Midwifery & Radiography, City, University of London, UK; Society and College of Radiographers AI Advisory Group, London, UK; European Society of Medical Imaging Informatics, Vienna, Austria; European Federation of Radiographer Societies, Cumieira, Portugal.
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11
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Fazakarley CA, Breen M, Thompson B, Leeson P, Williamson V. Beliefs, experiences and concerns of using artificial intelligence in healthcare: A qualitative synthesis. Digit Health 2024; 10:20552076241230075. [PMID: 38347935 PMCID: PMC10860471 DOI: 10.1177/20552076241230075] [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] [Accepted: 01/16/2024] [Indexed: 02/15/2024] Open
Abstract
Objective Artificial intelligence (AI) is a developing field in the context of healthcare. As this technology continues to be implemented in patient care, there is a growing need to understand the thoughts and experiences of stakeholders in this area to ensure that future AI development and implementation is successful. The aim of this study was to conduct a literature search of qualitative studies exploring the opinions of stakeholders such as clinicians, patients, and technology experts in order to establish the most common themes and ideas that have been presented in this research. Methods A literature search was conducted of existing qualitative research on stakeholder beliefs about the use of AI use in healthcare. Twenty-one papers were selected and analysed resulting in the development of four key themes relating to patient care, patient-doctor relationships, lack of education and resources, and the need for regulations. Results Overall, patients and healthcare workers are open to the use of AI in care and appear positive about potential benefits. However, concerns were raised relating to the lack of empathy in interactions of AI tools, and potential risks that may arise from the data collection needed for AI use and development. Stakeholders in the healthcare, technology, and business sectors all stressed that there was a lack of appropriate education, funding, and guidelines surrounding AI, and these concerns needed to be addressed to ensure future implementation is safe and suitable for patient care. Conclusion Ultimately, the results found in this study highlighted that there was a need for communication between stakeholder in order for these concerns to be addressed, mitigate potential risks, and maximise benefits for patients and clinicians alike. The results also identified a need for further qualitative research in this area to further understand stakeholder experiences as AI use continues to develop.
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Affiliation(s)
| | | | | | - Paul Leeson
- RDM Division of Cardiovascular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Victoria Williamson
- King's Centre for Military Health Research, King's College London, London, UK
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12
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Sultan S, Acharya Y, Zayed O, Elzomour H, Parodi JC, Soliman O, Hynes N. Is the Cardiovascular Specialist Ready For the Fifth Revolution? The Role of Artificial Intelligence, Machine Learning, Big Data Analysis, Intelligent Swarming, and Knowledge-Centered Service on the Future of Global Cardiovascular Healthcare Delivery. J Endovasc Ther 2023; 30:877-884. [PMID: 35695277 PMCID: PMC10637093 DOI: 10.1177/15266028221102660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- Sherif Sultan
- Western Vascular Institute, Department of Vascular and Endovascular Surgery, University Hospital Galway, National University of Ireland, Galway, Galway, Ireland
- Department of Vascular Surgery and Endovascular Surgery, Galway Clinic, Royal College of Surgeons in Ireland and National University of Ireland, Galway Affiliated Hospital, Galway, Ireland
- CORRIB-CÚRAM-Vascular Group, National University of Ireland, Galway, Galway, Ireland
| | - Yogesh Acharya
- Western Vascular Institute, Department of Vascular and Endovascular Surgery, University Hospital Galway, National University of Ireland, Galway, Galway, Ireland
- Department of Vascular Surgery and Endovascular Surgery, Galway Clinic, Royal College of Surgeons in Ireland and National University of Ireland, Galway Affiliated Hospital, Galway, Ireland
| | - Omnia Zayed
- Data Science Institute, National University of Ireland, Galway, Galway, Ireland
| | - Hesham Elzomour
- Discipline of Cardiology, CORRIB-CÚRAM-Vascular Group, National University of Ireland, Galway, Galway, Ireland
| | - Juan Carlos Parodi
- Department of Vascular Surgery and Biomedical Engineering Department, Alma Mater, University of Buenos Aires, and Trinidad Hospital, Buenos Aires, Argentina
| | - Osama Soliman
- Discipline of Cardiology, CORRIB-CÚRAM-Vascular Group, National University of Ireland, Galway, Galway, Ireland
| | - Niamh Hynes
- CORRIB-CÚRAM-Vascular Group, National University of Ireland, Galway, Galway, Ireland
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13
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Veras M, Dyer JO, Rooney M, Barros Silva PG, Rutherford D, Kairy D. Usability and Efficacy of Artificial Intelligence Chatbots (ChatGPT) for Health Sciences Students: Protocol for a Crossover Randomized Controlled Trial. JMIR Res Protoc 2023; 12:e51873. [PMID: 37999958 DOI: 10.2196/51873] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/18/2023] [Accepted: 10/20/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND The integration of artificial intelligence (AI) into health sciences students' education holds significant importance. The rapid advancement of AI has opened new horizons in scientific writing and has the potential to reshape human-technology interactions. AI in education may impact critical thinking, leading to unintended consequences that need to be addressed. Understanding the implications of AI adoption in education is essential for ensuring its responsible and effective use, empowering health sciences students to navigate AI-driven technologies' evolving field with essential knowledge and skills. OBJECTIVE This study aims to provide details on the study protocol and the methods used to investigate the usability and efficacy of ChatGPT, a large language model. The primary focus is on assessing its role as a supplementary learning tool for improving learning processes and outcomes among undergraduate health sciences students, with a specific emphasis on chronic diseases. METHODS This single-blinded, crossover, randomized, controlled trial is part of a broader mixed methods study, and the primary emphasis of this paper is on the quantitative component of the overall research. A total of 50 students will be recruited for this study. The alternative hypothesis posits that there will be a significant difference in learning outcomes and technology usability between students using ChatGPT (group A) and those using standard web-based tools (group B) to access resources and complete assignments. Participants will be allocated to sequence AB or BA in a 1:1 ratio using computer-generated randomization. Both arms include students' participation in a writing assignment intervention, with a washout period of 21 days between interventions. The primary outcome is the measure of the technology usability and effectiveness of ChatGPT, whereas the secondary outcome is the measure of students' perceptions and experiences with ChatGPT as a learning tool. Outcome data will be collected up to 24 hours after the interventions. RESULTS This study aims to understand the potential benefits and challenges of incorporating AI as an educational tool, particularly in the context of student learning. The findings are expected to identify critical areas that need attention and help educators develop a deeper understanding of AI's impact on the educational field. By exploring the differences in the usability and efficacy between ChatGPT and conventional web-based tools, this study seeks to inform educators and students on the responsible integration of AI into academic settings, with a specific focus on health sciences education. CONCLUSIONS By exploring the usability and efficacy of ChatGPT compared with conventional web-based tools, this study seeks to inform educators and students about the responsible integration of AI into academic settings. TRIAL REGISTRATION ClinicalTrails.gov NCT05963802; https://clinicaltrials.gov/study/NCT05963802. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/51873.
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Affiliation(s)
- Mirella Veras
- Health Sciences, Carleton University, Ottawa, ON, Canada
- Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal, Montréal, QC, Canada
| | - Joseph-Omer Dyer
- École de Réadaptation, Faculté de Médecine, Université de Montréal, Montréal, QC, Canada
- Groupe Interdisciplinaire de Recherche sur la Cognition et le Raisonnement Professionnel, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Morgan Rooney
- Teaching and Learning Services, Carleton University, Ottawa, ON, Canada
| | | | - Derek Rutherford
- School of Physiotherapy, Dalhousie University, Halifax, NS, Canada
| | - Dahlia Kairy
- Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal, Montréal, QC, Canada
- École de Réadaptation, Faculté de Médecine, Université de Montréal, Montréal, QC, Canada
- Institut Universitaire sur la Réadaptation en Déficience Physique de Montréal, Centre Intégré Universitaire de Santé et Services Sociaux du Centre-Sud-de-l'Île-de-Montréal, Montréal, QC, Canada
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Rainey C, Villikudathil AT, McConnell J, Hughes C, Bond R, McFadden S. An experimental machine learning study investigating the decision-making process of students and qualified radiographers when interpreting radiographic images. PLOS DIGITAL HEALTH 2023; 2:e0000229. [PMID: 37878569 PMCID: PMC10599497 DOI: 10.1371/journal.pdig.0000229] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/29/2023] [Indexed: 10/27/2023]
Abstract
AI is becoming more prevalent in healthcare and is predicted to be further integrated into workflows to ease the pressure on an already stretched service. The National Health Service in the UK has prioritised AI and Digital health as part of its Long-Term Plan. Few studies have examined the human interaction with such systems in healthcare, despite reports of biases being present with the use of AI in other technologically advanced fields, such as finance and aviation. Understanding is needed of how certain user characteristics may impact how radiographers engage with AI systems in use in the clinical setting to mitigate against problems before they arise. The aim of this study is to determine correlations of skills, confidence in AI and perceived knowledge amongst student and qualified radiographers in the UK healthcare system. A machine learning based AI model was built to predict if the interpreter was either a student (n = 67) or a qualified radiographer (n = 39) in advance, using important variables from a feature selection technique named Boruta. A survey, which required the participant to interpret a series of plain radiographic examinations with and without AI assistance, was created on the Qualtrics survey platform and promoted via social media (Twitter/LinkedIn), therefore adopting convenience, snowball sampling This survey was open to all UK radiographers, including students and retired radiographers. Pearson's correlation analysis revealed that males who were proficient in their profession were more likely than females to trust AI. Trust in AI was negatively correlated with age and with level of experience. A machine learning model was built, the best model predicted the image interpreter to be qualified radiographers with 0.93 area under curve and a prediction accuracy of 93%. Further testing in prospective validation cohorts using a larger sample size is required to determine the clinical utility of the proposed machine learning model.
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Affiliation(s)
- Clare Rainey
- Faculty of Life and Health Sciences, School of Health Sciences, Ulster University, York Street, Belfast, Northern Ireland, United Kingdom
| | - Angelina T. Villikudathil
- Faculty of Life and Health Sciences, School of Health Sciences, Ulster University, York Street, Belfast, Northern Ireland, United Kingdom
| | | | - Ciara Hughes
- Faculty of Life and Health Sciences, School of Health Sciences, Ulster University, York Street, Belfast, Northern Ireland, United Kingdom
| | - Raymond Bond
- Faculty of Computing, School of Computing, Engineering and the Built Environment, Ulster University, York Street, Belfast, Northern Ireland, United Kingdom
| | - Sonyia McFadden
- Faculty of Life and Health Sciences, School of Health Sciences, Ulster University, York Street, Belfast, Northern Ireland, United Kingdom
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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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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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17
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Mousavi Baigi SF, Sarbaz M, Ghaddaripouri K, Ghaddaripouri M, Mousavi AS, Kimiafar K. Attitudes, knowledge, and skills towards artificial intelligence among healthcare students: A systematic review. Health Sci Rep 2023; 6:e1138. [PMID: 36923372 PMCID: PMC10009305 DOI: 10.1002/hsr2.1138] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 01/19/2023] [Accepted: 02/16/2023] [Indexed: 03/14/2023] Open
Abstract
Background and Aims This systematic review examined healthcare students' attitudes, knowledge, and skill in Artificial Intelligence (AI). Methods On August 3, 2022, studies were retrieved from the PubMed, Embase, Scopus, and Web of Science databases. Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations were followed. We included cross-sectional studies that examined healthcare students' knowledge, attitudes, skills, and perceptions of AI in this review. Using the eligibility requirements as a guide, titles and abstracts were screened. Complete texts were then retrieved and independently reviewed per the eligibility requirements. To collect data, a standardized form was used. Results Of the 38 included studies, 29 (76%) of healthcare students had a positive and promising attitude towards AI in the clinical profession and its use in he future; however, in nine of the studies (24%), students considered AI a threat to healthcare fields and had a negative attitude towards it. Furthermore, 26 studies evaluated the knowledge of healthcare students about AI. Among these, 18 studies evaluated the level of student knowledge as low (50%). On the other hand, in six of the studies, students' high knowledge of AI was reported, and two of the studies reported average student general knowledge (almost 50%). Of the six studies, four (67%) of the students had very low skills, so they stated that they had never worked with AI. Conclusion Evidence from this review shows that healthcare students had a positive and promising attitude towards AI in medicine; however, most students had low knowledge and limited skills in working with AI. Face-to-face instruction, training manuals, and detailed instructions are therefore crucial for implementing and comprehending how AI technology works and raising students' knowledge of the advantages of AI.
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Affiliation(s)
- Seyyedeh Fatemeh Mousavi Baigi
- Department of Health Information Technology, School of Paramedical and Rehabilitation SciencesMashhad University of Medical SciencesMashhadIran
- Student Research CommitteeMashhad University of Medical SciencesMashhadIran
| | - Masoumeh Sarbaz
- Department of Health Information Technology, School of Paramedical and Rehabilitation SciencesMashhad University of Medical SciencesMashhadIran
| | - Kosar Ghaddaripouri
- Department of Health Information TechnologyVarastegan Institute of Medical SciencesMashhadIran
| | - Maryam Ghaddaripouri
- Department of Laboratory Sciences, School of Paramedical and Rehabilitation SciencesMashhad University of Medical SciencesMashhadIran
| | - Atefeh Sadat Mousavi
- Department of Health Information Technology, School of Paramedical and Rehabilitation SciencesMashhad University of Medical SciencesMashhadIran
| | - Khalil Kimiafar
- Department of Health Information Technology, School of Paramedical and Rehabilitation SciencesMashhad University of Medical SciencesMashhadIran
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Akudjedu TN, Torre S, Khine R, Katsifarakis D, Newman D, Malamateniou C. Knowledge, perceptions, and expectations of Artificial intelligence in radiography practice: A global radiography workforce survey. J Med Imaging Radiat Sci 2023; 54:104-116. [PMID: 36535859 DOI: 10.1016/j.jmir.2022.11.016] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 10/19/2022] [Accepted: 11/29/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Artificial Intelligence (AI) technologies have already started impacting clinical practice across various settings worldwide, including the radiography profession. This study is aimed at exploring a world-wide view on AI technologies in relation to knowledge, perceptions, and expectations of radiography professionals. METHODS An online survey (hosted on Qualtrics) on key AI concepts was open to radiography professionals worldwide (August 1st to December 31st 2020). The survey sought both quantitative and qualitative data on topical issues relating to knowledge, perceptions, and expectations in relation to AI implementation in radiography practice. Data obtained was analysed using the Statistical Package for Social Sciences (SPSS) (v.26) and the six-phase thematic analysis approach. RESULTS A total of 314 valid responses were obtained with a fair geographical distribution. Of the respondents, 54.1% (157/290) were from North America and were predominantly clinical practicing radiographers (60.5%, 190/314). Our findings broadly relate to different perceived benefits and misgivings/shortcomings of AI implementation in radiography practice. The benefits relate to enhanced workflows and optimised workstreams while the misgivings/shortcomings revolve around de-skilling and impact on patient-centred care due to over-reliance on advanced technology following AI implementation. DISCUSSION Artificial intelligence is a tool but to operate optimally it requires human input and validation. Radiographers working at the interface between technology and the patient are key stakeholders in AI implementation. Lack of training and of transparency of AI tools create a mixed response of radiographers when they discuss their perceived benefits and challenges. It is also possible that their responses are nuanced by different regional and geographical contexts when it comes to AI deployment. Irrespective of geography, there is still a lot to be done about formalised AI training for radiographers worldwide. This is a vital step to ensure safe and effective AI implementation, adoption, and faster integration into clinical practice by healthcare workers including radiographers. CONCLUSION Advancement of AI technologies and implementation should be accompanied by proportional training of end-users in radiography and beyond. There are many benefits of AI-enabled radiography workflows and improvement on efficiencies but equally there will be widespread disruption of traditional roles and patient-centred care, which can be managed by a well-educated and well-informed workforce.
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Affiliation(s)
- Theophilus N Akudjedu
- Department of Medical Science and Public Health, Faculty of Health and Social Sciences, Institute of Medical Imaging and Visualisation, Bournemouth University, Bournemouth, Dorset, UK.
| | - Sofia Torre
- Department of Radiography, School of Health Sciences, City, University of London, Northampton Square, London, UK
| | - Ricardo Khine
- School of Health and Care Professions, Buckinghamshire New University, UK
| | | | - Donna Newman
- International Society of Radiographers and Radiological Technologists, UK
| | - Christina Malamateniou
- Department of Radiography, School of Health Sciences, City, University of London, Northampton Square, London, UK
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19
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Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study. Insights Imaging 2023; 14:25. [PMID: 36735172 PMCID: PMC9897152 DOI: 10.1186/s13244-023-01372-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 01/15/2023] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking. Therefore, this study aimed to evaluate and discuss a postgraduate-level module on AI developed in the UK for radiographers. METHODOLOGY A participatory action research methodology was applied, with participants recruited from the first cohort of students enrolled in this module and faculty members. Data were collected using online, semi-structured, individual interviews and focus group discussions. Textual data were processed using data-driven thematic analysis. RESULTS Seven students and six faculty members participated in this evaluation. Results can be summarised in the following four themes: a. participants' professional and educational backgrounds influenced their experiences, b. participants found the learning experience meaningful concerning module design, organisation, and pedagogical approaches, c. some module design and delivery aspects were identified as barriers to learning, and d. participants suggested how the ideal AI course could look like based on their experiences. CONCLUSIONS The findings of our work show that an AI module can assist educators/academics in developing similar AI education provisions for radiographers and other medical imaging and radiation sciences professionals. A blended learning delivery format, combined with customisable and contextualised content, using an interprofessional faculty approach is recommended for future similar courses.
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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: 6] [Impact Index Per Article: 6.0] [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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21
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Barreiro-Ares A, Morales-Santiago A, Sendra-Portero F, Souto-Bayarri M. Impact of the Rise of Artificial Intelligence in Radiology: What Do Students Think? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1589. [PMID: 36674348 PMCID: PMC9867061 DOI: 10.3390/ijerph20021589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 06/17/2023]
Abstract
The rise of artificial intelligence (AI) in medicine, and particularly in radiology, is becoming increasingly prominent. Its impact will transform the way the specialty is practiced and the current and future education model. The aim of this study is to analyze the perception that undergraduate medical students have about the current situation of AI in medicine, especially in radiology. A survey with 17 items was distributed to medical students between 3 January to 31 March 2022. Two hundred and eighty-one students correctly responded the questionnaire; 79.3% of them claimed that they knew what AI is. However, their objective knowledge about AI was low but acceptable. Only 24.9% would choose radiology as a specialty, and only 40% of them as one of their first three options. The applications of this technology were valued positively by most students, who give it an important Support Role, without fear that the radiologist will be replaced by AI (79.7%). The majority (95.7%) agreed with the need to implement well-established ethical principles in AI, and 80% valued academic training in AI positively. Surveyed medical students have a basic understanding of AI and perceive it as a useful tool that will transform radiology.
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Affiliation(s)
- Andrés Barreiro-Ares
- Department of Radiology, School of Medicine, University of Santiago de Compostela/CHUS/IDIS (Instituto de Investigación Sanitaria de Santiago), 15782 Santiago de Compostela, Spain
| | - Annia Morales-Santiago
- Department of Radiology, School of Medicine, University of Santiago de Compostela/CHUS/IDIS (Instituto de Investigación Sanitaria de Santiago), 15782 Santiago de Compostela, Spain
| | - Francisco Sendra-Portero
- Department of Radiology and Physical Medicine, School of Medicine, University of Malaga, 29010 Málaga, Spain
| | - Miguel Souto-Bayarri
- Department of Radiology, School of Medicine, University of Santiago de Compostela/CHUS/IDIS (Instituto de Investigación Sanitaria de Santiago), 15782 Santiago de Compostela, Spain
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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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Schwendicke F, Chaurasia A, Wiegand T, Uribe SE, Fontana M, Akota I, Tryfonos O, Krois J. Artificial intelligence for oral and dental healthcare: Core education curriculum. J Dent 2023; 128:104363. [PMID: 36410581 DOI: 10.1016/j.jdent.2022.104363] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/09/2022] [Accepted: 11/11/2022] [Indexed: 11/22/2022] Open
Abstract
OBJECTIVES Artificial intelligence (AI) is swiftly entering oral health services and dentistry, while most providers show limited knowledge and skills to appraise dental AI applications. We aimed to define a core curriculum for both undergraduate and postgraduate education, establishing a minimum set of outcomes learners should acquire when taught about oral and dental AI. METHODS Existing curricula and other documents focusing on literacy of medical professionals around AI were screened and relevant items extracted. Items were scoped and adapted using expert interviews with members of the IADR's e-oral health group, the ITU/WHO's Focus Group AI for Health and the Association for Dental Education in Europe. Learning outcome levels were defined and each item assigned to a level. Items were systematized into domains and a curricular structure defined. The resulting curriculum was consented using an online Delphi process. RESULTS Four domains of learning outcomes emerged, with most outcomes being on the "knowledge" level: (1) Basic definitions and terms, the reasoning behind AI and the principle of machine learning, the idea of training, validating and testing models, the definition of reference tests, the contrast between dynamic and static AI, and the problem of AI being a black box and requiring explainability should be known. (2) Use cases, the required types of AI to address them, and the typical setup of AI software for dental purposes should be taught. (3) Evaluation metrics, their interpretation, the relevant impact of AI on patient or societal health outcomes and associated examples should be considered. (4) Issues around generalizability and representativeness, explainability, autonomy and accountability and the need for governance should be highlighted. CONCLUSION Both educators and learners should consider this core curriculum during planning, conducting and evaluating oral and dental AI education. CLINICAL SIGNIFICANCE A core curriculum on oral and dental AI may help to increase oral and dental healthcare providers' literacy around AI, allowing them to critically appraise AI applications and to use them consciously and on an informed basis.
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Affiliation(s)
- Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany; ITU/WHO Focus Group AI on Health, Topic Group Dentistry, Switzerland.
| | - Akhilanand Chaurasia
- ITU/WHO Focus Group AI on Health, Topic Group Dentistry, Switzerland; Department of Oral Medicine and Radiology, King George's Medical University, Lucknow, India
| | - Thomas Wiegand
- ITU/WHO Focus Group AI on Health, Topic Group Dentistry, Switzerland; Technical University Berlin, Berlin, Germany
| | - Sergio E Uribe
- ITU/WHO Focus Group AI on Health, Topic Group Dentistry, Switzerland; Bioinformatics Lab & Dept of Conservative Dentistry and Oral Health, Riga Stradins University, Riga, Latvia; School of Dentistry, Universidad Austral de Chile, Valdivia, Chile
| | - Margherita Fontana
- Cariology, Restorative Sciences & Endodontics, University of Michigan, Ann Arbor, United States
| | - Ilze Akota
- Department of Oral and Maxillofacial surgery, Riga Stradins University, Riga, Latvia
| | - Olga Tryfonos
- ITU/WHO Focus Group AI on Health, Topic Group Dentistry, Switzerland; Department of Periodontology and Oral Biochemistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherland
| | - Joachim Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany; ITU/WHO Focus Group AI on Health, Topic Group Dentistry, Switzerland
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Singapore radiographers' perceptions and expectations of artificial intelligence - A qualitative study. J Med Imaging Radiat Sci 2022; 53:554-563. [PMID: 36115823 DOI: 10.1016/j.jmir.2022.08.005] [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: 05/23/2022] [Revised: 08/03/2022] [Accepted: 08/11/2022] [Indexed: 12/24/2022]
Abstract
INTRODUCTION With the emergence of artificial intelligence (AI) in medical imaging, radiographers are likely to be at the forefront of this technological advancement. Studies have therefore been conducted recently to understand radiographers' opinions on AI adoption. This study extends that work by using a qualitative approach to further explore radiographers' knowledge, perceptions, and expectations of AI. METHOD Six online focus groups were conducted with 22 radiographers from the three public healthcare clusters in Singapore. They were purposively sampled, and participants were recruited from a broad demographic background with varying years of working experience and designations. The focus group sessions were transcribed verbatim and thematic analysis was performed on their responses. RESULTS Participants demonstrated limited knowledge of AI. Their perceptions of AI were mixed, recognising its benefits in increasing efficiency and improving patient care, but also aware of its limitations in accuracy and bias. On how patients may perceive AI, participants felt that patients would accept AI if they felt it improves their care but may reject it once they lose trust in it. Expectations wise, participants envisioned several applications in pre-, peri‑, and post-procedural workflows including order vetting, patient positioning, language translation, and artefact removal. On radiographers' role and career opportunities, some participants see an opportunity for radiographers to specialise in AI, becoming involved in algorithm development and its clinical implementation. DISCUSSION Our findings suggest that widespread implementation of AI would require limited knowledge amongst radiographers and current AI limitations to be addressed. While radiographers are positively anticipating the integration of AI into their practices, they should also become actively involved in the development of AI tools such that those they envisioned. This would help align optimal use of AI tools and radiographer role changes. Patients' acceptance and reactions to AI also warrant further research.
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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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Lewis DS, Downing PC, Hayre D. South African radiography leadership co-constructing radiation protection change ideas. J Med Imaging Radiat Sci 2022; 53:248-255. [DOI: 10.1016/j.jmir.2022.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/09/2022] [Accepted: 03/10/2022] [Indexed: 10/18/2022]
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