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Jebreen K, Radwan E, Kammoun-Rebai W, Alattar E, Radwan A, Safi W, Radwan W, Alajez M. Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine. BMC MEDICAL EDUCATION 2024; 24:507. [PMID: 38714993 PMCID: PMC11077786 DOI: 10.1186/s12909-024-05465-4] [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: 08/01/2023] [Accepted: 04/24/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND The current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students' attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula. METHODS This study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022-2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively. RESULTS From a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields. CONCLUSION Medical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.
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Affiliation(s)
- Kamel Jebreen
- Department of Mathematics, Palestine Technical University - Kadoorie, Hebron, Palestine
- Department of Mathematics, An-Najah National University, Nablus, Palestine
- Unité de Recherche Clinique Saint-Louis Fernand-Widal Lariboisière, APHP, Paris, France
| | - Eqbal Radwan
- Department of Biology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine.
| | | | - Etimad Alattar
- Department of Biology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine
| | - Afnan Radwan
- Faculty of Education, Islamic University of Gaza, Gaza, Palestine
| | - Walaa Safi
- Department of Biotechnology, Faculty of Science, Islamic University of Gaza, Gaza, Palestine
| | - Walaa Radwan
- University College of Applied Sciences - Gaza, Gaza, Palestine
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Tozsin A, Ucmak H, Soyturk S, Aydin A, Gozen AS, Fahim MA, Güven S, Ahmed K. The Role of Artificial Intelligence in Medical Education: A Systematic Review. Surg Innov 2024:15533506241248239. [PMID: 38632898 DOI: 10.1177/15533506241248239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
BACKGROUND To examine the artificial intelligence (AI) tools currently being studied in modern medical education, and critically evaluate the level of validation and the quality of evidence presented in each individual study. METHODS This review (PROSPERO ID: CRD42023410752) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A database search was conducted using PubMed, Embase, and Cochrane Library. Articles written in the English language between 2000 and March 2023 were reviewed retrospectively using the MeSH Terms "AI" and "medical education" A total of 4642 potentially relevant studies were found. RESULTS After a thorough screening process, 36 studies were included in the final analysis. These studies consisted of 26 quantitative studies and 10 studies investigated the development and validation of AI tools. When examining the results of studies in which Support vector machines (SVMs) were employed, it has demonstrated high accuracy in assessing students' experiences, diagnosing acute abdominal pain, classifying skilled and novice participants, and evaluating surgical training levels. Particularly in the comparison of surgical skill levels, it has achieved an accuracy rate of over 92%. CONCLUSION AI tools demonstrated effectiveness in improving practical skills, diagnosing diseases, and evaluating student performance. However, further research with rigorous validation is required to identify the most effective AI tools for medical education.
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Affiliation(s)
- Atinc Tozsin
- Department of Urology, Trakya University School of Medicine, Edirne, Turkey
| | - Harun Ucmak
- Department of Urology, Meram School of Medicine, Necmettin Erbakan University, Konya, Turkey
| | - Selim Soyturk
- Department of Urology, Meram School of Medicine, Necmettin Erbakan University, Konya, Turkey
| | - Abdullatif Aydin
- MRC Centre for Transplantation, Guy's Hospital, King's College London, London, UK
- Department of Urology, King's College Hospital NHS Foundation Trust, London, UK
| | | | - Maha Al Fahim
- Medical Education Department, Sheikh Khalifa Medical City, Abu Dhabi, UAE
| | - Selcuk Güven
- Department of Urology, Meram School of Medicine, Necmettin Erbakan University, Konya, Turkey
| | - Kamran Ahmed
- MRC Centre for Transplantation, Guy's Hospital, King's College London, London, UK
- Khalifa University, Abu Dhabi, UAE
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Gordon M, Daniel M, Ajiboye A, Uraiby H, Xu NY, Bartlett R, Hanson J, Haas M, Spadafore M, Grafton-Clarke C, Gasiea RY, Michie C, Corral J, Kwan B, Dolmans D, Thammasitboon S. A scoping review of artificial intelligence in medical education: BEME Guide No. 84. MEDICAL TEACHER 2024; 46:446-470. [PMID: 38423127 DOI: 10.1080/0142159x.2024.2314198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 01/31/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND Artificial Intelligence (AI) is rapidly transforming healthcare, and there is a critical need for a nuanced understanding of how AI is reshaping teaching, learning, and educational practice in medical education. This review aimed to map the literature regarding AI applications in medical education, core areas of findings, potential candidates for formal systematic review and gaps for future research. METHODS This rapid scoping review, conducted over 16 weeks, employed Arksey and O'Malley's framework and adhered to STORIES and BEME guidelines. A systematic and comprehensive search across PubMed/MEDLINE, EMBASE, and MedEdPublish was conducted without date or language restrictions. Publications included in the review spanned undergraduate, graduate, and continuing medical education, encompassing both original studies and perspective pieces. Data were charted by multiple author pairs and synthesized into various thematic maps and charts, ensuring a broad and detailed representation of the current landscape. RESULTS The review synthesized 278 publications, with a majority (68%) from North American and European regions. The studies covered diverse AI applications in medical education, such as AI for admissions, teaching, assessment, and clinical reasoning. The review highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education. CONCLUSION The current literature has been charted. The findings underscore the need for ongoing research to explore uncharted areas and address potential risks associated with AI use in medical education. This work serves as a foundational resource for educators, policymakers, and researchers in navigating AI's evolving role in medical education. A framework to support future high utility reporting is proposed, the FACETS framework.
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Affiliation(s)
- Morris Gordon
- School of Medicine and Dentistry, University of Central Lancashire, Preston, UK
- Blackpool Hospitals NHS Foundation Trust, Blackpool, UK
| | - Michelle Daniel
- School of Medicine, University of California, San Diego, SanDiego, CA, USA
| | - Aderonke Ajiboye
- School of Medicine and Dentistry, University of Central Lancashire, Preston, UK
| | - Hussein Uraiby
- Department of Cellular Pathology, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Nicole Y Xu
- School of Medicine, University of California, San Diego, SanDiego, CA, USA
| | - Rangana Bartlett
- Department of Cognitive Science, University of California, San Diego, CA, USA
| | - Janice Hanson
- Department of Medicine and Office of Education, School of Medicine, Washington University in Saint Louis, Saint Louis, MO, USA
| | - Mary Haas
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Maxwell Spadafore
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | | | | | - Colin Michie
- School of Medicine and Dentistry, University of Central Lancashire, Preston, UK
| | - Janet Corral
- Department of Medicine, University of Nevada Reno, School of Medicine, Reno, NV, USA
| | - Brian Kwan
- School of Medicine, University of California, San Diego, SanDiego, CA, USA
| | - Diana Dolmans
- School of Health Professions Education, Faculty of Health, Maastricht University, Maastricht, NL, USA
| | - Satid Thammasitboon
- Center for Research, Innovation and Scholarship in Health Professions Education, Baylor College of Medicine, Houston, TX, USA
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Hashimoto DA, Sambasastry SK, Singh V, Kurada S, Altieri M, Yoshida T, Madani A, Jogan M. A foundation for evaluating the surgical artificial intelligence literature. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024:108014. [PMID: 38360498 DOI: 10.1016/j.ejso.2024.108014] [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: 10/22/2023] [Revised: 01/06/2024] [Accepted: 02/09/2024] [Indexed: 02/17/2024]
Abstract
With increasing growth in applications of artificial intelligence (AI) in surgery, it has become essential for surgeons to gain a foundation of knowledge to critically appraise the scientific literature, commercial claims regarding products, and regulatory and legal frameworks that govern the development and use of AI. This guide offers surgeons a framework with which to evaluate manuscripts that incorporate the use of AI. It provides a glossary of common terms, an overview of prerequisite knowledge to maximize understanding of methodology, and recommendations on how to carefully consider each element of a manuscript to assess the quality of the data on which an algorithm was trained, the appropriateness of the methodological approach, the potential for reproducibility of the experiment, and the applicability to surgical practice, including considerations on generalizability and scalability.
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Affiliation(s)
- Daniel A Hashimoto
- Penn Computer Assisted Surgery and Outcomes Laboratory, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA; Global Surgical AI Collaborative, Toronto, ON, USA.
| | - Sai Koushik Sambasastry
- Penn Computer Assisted Surgery and Outcomes Laboratory, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Vivek Singh
- Penn Computer Assisted Surgery and Outcomes Laboratory, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sruthi Kurada
- Penn Computer Assisted Surgery and Outcomes Laboratory, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Maria Altieri
- Penn Computer Assisted Surgery and Outcomes Laboratory, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Global Surgical AI Collaborative, Toronto, ON, USA
| | - Takuto Yoshida
- Surgical AI Research Academy, Department of Surgery, University Health Network, Toronto, ON, USA
| | - Amin Madani
- Global Surgical AI Collaborative, Toronto, ON, USA; Surgical AI Research Academy, Department of Surgery, University Health Network, Toronto, ON, USA
| | - Matjaz Jogan
- Penn Computer Assisted Surgery and Outcomes Laboratory, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Hassankhani A, Amoukhteh M, Valizadeh P, Jannatdoust P, Sabeghi P, Gholamrezanezhad A. Radiology as a Specialty in the Era of Artificial Intelligence: A Systematic Review and Meta-analysis on Medical Students, Radiology Trainees, and Radiologists. Acad Radiol 2024; 31:306-321. [PMID: 37349157 DOI: 10.1016/j.acra.2023.05.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 05/20/2023] [Accepted: 05/21/2023] [Indexed: 06/24/2023]
Abstract
RATIONALE AND OBJECTIVES Artificial intelligence (AI) is changing radiology by automating tasks and assisting in abnormality detection and understanding perceptions of medical students, radiology trainees, and radiologists is vital for preparing them for AI integration in radiology. MATERIALS AND METHODS A systematic review and meta-analysis were conducted following established guidelines. PubMed, Scopus, and Web of Science were searched up to March 5, 2023. Eligible studies reporting outcomes of interest were included, and relevant data were extracted and analyzed using STATA software version 17.0. RESULTS A meta-analysis of 21 studies revealed that 22.36% of individuals were less likely to choose radiology as a career due to concerns about advances in AI. Medical students showed higher rates of concern (31.94%) compared to radiology trainees and radiologists (9.16%) (P < .01). Radiology trainees and radiologists also demonstrated higher basic AI knowledge (71.84% vs 35.38%). Medical students had higher rates of belief that AI poses a threat to the radiology job market (42.66% vs 6.25%, P < .02). The pooled rate of respondents who believed that "AI will revolutionize radiology in the future" was 79.48%, with no significant differences based on participants' positions. The pooled rate of responders who believed in the integration of AI in medical curricula was 81.75% among radiology trainees and radiologists and 70.23% among medical students. CONCLUSION The study revealed growing concerns regarding the impact of AI in radiology, particularly among medical students, which can be addressed by revamping education, providing direct AI experience, addressing limitations, and emphasizing medico-legal issues to prepare for AI integration in radiology.
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Affiliation(s)
- Amir Hassankhani
- Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1441 Eastlake Avenue Ste 2315, Los Angeles, CA 90089 (A.H., M.A., P.V., P.J., P.S., A.G.); Department of Radiology, Mayo Clinic, Rochester, Minnesota (A.H., M.A.).
| | - Melika Amoukhteh
- Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1441 Eastlake Avenue Ste 2315, Los Angeles, CA 90089 (A.H., M.A., P.V., P.J., P.S., A.G.); Department of Radiology, Mayo Clinic, Rochester, Minnesota (A.H., M.A.)
| | - Parya Valizadeh
- Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1441 Eastlake Avenue Ste 2315, Los Angeles, CA 90089 (A.H., M.A., P.V., P.J., P.S., A.G.)
| | - Payam Jannatdoust
- Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1441 Eastlake Avenue Ste 2315, Los Angeles, CA 90089 (A.H., M.A., P.V., P.J., P.S., A.G.)
| | - Paniz Sabeghi
- Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1441 Eastlake Avenue Ste 2315, Los Angeles, CA 90089 (A.H., M.A., P.V., P.J., P.S., A.G.)
| | - Ali Gholamrezanezhad
- Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1441 Eastlake Avenue Ste 2315, Los Angeles, CA 90089 (A.H., M.A., P.V., P.J., P.S., A.G.)
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Ng FYC, Thirunavukarasu AJ, Cheng H, Tan TF, Gutierrez L, Lan Y, Ong JCL, Chong YS, Ngiam KY, Ho D, Wong TY, Kwek K, Doshi-Velez F, Lucey C, Coffman T, Ting DSW. Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers. Cell Rep Med 2023; 4:101230. [PMID: 37852174 PMCID: PMC10591047 DOI: 10.1016/j.xcrm.2023.101230] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 09/04/2023] [Accepted: 09/15/2023] [Indexed: 10/20/2023]
Abstract
Current and future healthcare professionals are generally not trained to cope with the proliferation of artificial intelligence (AI) technology in healthcare. To design a curriculum that caters to variable baseline knowledge and skills, clinicians may be conceptualized as "consumers", "translators", or "developers". The changes required of medical education because of AI innovation are linked to those brought about by evidence-based medicine (EBM). We outline a core curriculum for AI education of future consumers, translators, and developers, emphasizing the links between AI and EBM, with suggestions for how teaching may be integrated into existing curricula. We consider the key barriers to implementation of AI in the medical curriculum: time, resources, variable interest, and knowledge retention. By improving AI literacy rates and fostering a translator- and developer-enriched workforce, innovation may be accelerated for the benefit of patients and practitioners.
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Affiliation(s)
- Faye Yu Ci Ng
- Artificial Intelligence and Digital Innovation, Singapore Eye Research Institute, Singapore National Eye Center, Singapore Health Service, Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Arun James Thirunavukarasu
- Artificial Intelligence and Digital Innovation, Singapore Eye Research Institute, Singapore National Eye Center, Singapore Health Service, Singapore, Singapore; University of Cambridge School of Clinical Medicine, Cambridge, UK; Oxford University Clinical Academic Graduate School, University of Oxford, Oxford, UK
| | - Haoran Cheng
- Artificial Intelligence and Digital Innovation, Singapore Eye Research Institute, Singapore National Eye Center, Singapore Health Service, Singapore, Singapore; Rollins School of Public Health, Emory University, Atlanta, GA, USA; Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Ting Fang Tan
- Artificial Intelligence and Digital Innovation, Singapore Eye Research Institute, Singapore National Eye Center, Singapore Health Service, Singapore, Singapore
| | - Laura Gutierrez
- Artificial Intelligence and Digital Innovation, Singapore Eye Research Institute, Singapore National Eye Center, Singapore Health Service, Singapore, Singapore
| | - Yanyan Lan
- Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China
| | | | - Yap Seng Chong
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Dean's Office, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Kee Yuan Ngiam
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Biomedical Engineering, School of Engineering, National University of Singapore, Singapore, Singapore
| | - Dean Ho
- Biomedical Engineering, School of Engineering, National University of Singapore, Singapore, Singapore; Insitute for Digital Medicine (WisDM), N.1 Institute for Health, National University of Singapore, Singapore, Singapore; Department of Pharmacology, National University of Singapore, Singapore, Singapore
| | - Tien Yin Wong
- Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Kenneth Kwek
- Chief Executive Office, Singapore General Hospital, SingHealth, Singapore, Singapore
| | - Finale Doshi-Velez
- Harvard Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Catherine Lucey
- Executive Vice Chancellor and Provost Office, University of California, San Francisco, San Francisco, CA, USA
| | - Thomas Coffman
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Daniel Shu Wei Ting
- Artificial Intelligence and Digital Innovation, Singapore Eye Research Institute, Singapore National Eye Center, Singapore Health Service, Singapore, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore, Singapore; Byers Eye Institute, Stanford University, Palo Alto, CA, USA.
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Sahin E. Are medical oncologists ready for the artificial intelligence revolution? Evaluation of the opinions, knowledge, and experiences of medical oncologists about artificial intelligence technologies. Med Oncol 2023; 40:327. [PMID: 37812310 DOI: 10.1007/s12032-023-02200-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 09/19/2023] [Indexed: 10/10/2023]
Abstract
The use of artificial intelligence technologies (AIT) in medicine is increasing worldwide. In this study, it was aimed to evaluate the experiences, opinions, and future expectations of medical oncologists on artificial intelligence (AI). After the reliability and validity analyses were carried out by a pilot study, the main online questionnaire was sent to the members of the "Turkish Society of Medical Oncology" mail group by an invitation e-mail. The anonymized responses of the participants were analyzed. The median age of the 156 participants was 36 (34-43) years and half (51%) were male. Most (45%) were fellows. Forty-six percent were working in university hospitals, 56% were visiting 20-40 patients a day. Medical oncologists' view of AIT was mostly positive (78%). However, some (especially women) had doubts about the reliability of AI (44%) and the establishment of its ethical/legal basis (49%). Sixty-five percent of the participants had no/superficial knowledge about AI. More than half (55%) had never used AI-based applications in their academic or clinical work. However, unlike now, 80% of the participants believed that they would use AIT frequently in their practice in the future and it would be beneficial. The most anticipated (81%) benefit was real-time information processing and real-time access to big data. Sixty-two percent believed that information about AI should be in the education curriculum. The vast majority of respondents (79%) thought that AI would not completely replace medical oncologists in the future. Some differences were found in the perception and experience of oncologists according to age, gender, title, and the number of patients examined per day. About AI, the general opinion of medical oncologists was positive, but their level of knowledge and use was low. However, they thought they would use it frequently in future and needed training.
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Affiliation(s)
- Elif Sahin
- Department of Medical Oncology, Kocaeli City Hospital, Tavsantepe mah., 41000, Izmit, Kocaeli, Turkey.
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Salastekar NV, Maxfield C, Hanna TN, Krupinski EA, Heitkamp D, Grimm LJ. Artificial Intelligence/Machine Learning Education in Radiology: Multi-institutional Survey of Radiology Residents in the United States. Acad Radiol 2023; 30:1481-1487. [PMID: 36710101 DOI: 10.1016/j.acra.2023.01.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/02/2023] [Accepted: 01/02/2023] [Indexed: 01/31/2023]
Abstract
RATIONALE AND OBJECTIVES To evaluate radiology residents' perspectives regarding inclusion of artificial intelligence/ machine learning (AI/ML) education in the residency curriculum. MATERIALS AND METHODS An online anonymous survey was sent to 759 residents at 21 US radiology residency programs. Resident demographics, sub-specialty interests, educational background and research experiences, as well as the awareness, availability, and usefulness of various resources for AI/ML education were collected. RESULTS The survey response rate was 27% (209/759). A total of 74% of respondents were male, 80% were training at large university programs, and only a minority (<20) had formal education or research experience in AI/ML. All four years of training were represented (range: 20%-38%). The majority of the residents agreed or strongly agreed (83%) that AI/ML education should be a part of the radiology residency curriculum and that such education should equip them with the knowledge to troubleshoot an AI tool in practice / determine whether a tool is working as intended (82%). Among the residency programs that offer AI/ML education, the most common resources were lecture series (43%), national informatics courses (28%), and in-house/institutional courses (26%). About 24% of the residents reported no AI/ML educational offerings in their residency curriculum. Hands on AI/ML laboratory (67%) and lecture series (61%) were reported as the most beneficial or effective. The majority of the residents preferred AI/ML education offered as a continuous course spanning the radiology residency (R1 to R4) (76%), followed by mini fellowship during R4 (32%) and as a course during PGY1 (21%). CONCLUSION Residents largely favor the inclusion of formal AI/ML education in the radiology residency curriculum, prefer hands-on learning and lectures as learning tools, and prefer a continuous AI/ML course spanning R1-R4.
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Affiliation(s)
- Ninad V Salastekar
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 100 Woodruff Circle, Atlanta, GA 30322.
| | - Charles Maxfield
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
| | - Tarek N Hanna
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 100 Woodruff Circle, Atlanta, GA 30322
| | - Elizabeth A Krupinski
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 100 Woodruff Circle, Atlanta, GA 30322
| | | | - Lars J Grimm
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
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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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Syed W, Basil A Al-Rawi M. Assessment of Awareness, Perceptions, and Opinions towards Artificial Intelligence among Healthcare Students in Riyadh, Saudi Arabia. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59050828. [PMID: 37241062 DOI: 10.3390/medicina59050828] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 04/18/2023] [Accepted: 04/22/2023] [Indexed: 05/28/2023]
Abstract
Background and Objective: The role of the pharmacist in healthcare society is unique, since they are providers of health information and medication counseling to patients. Hence, this study aimed to evaluate Awareness, Perceptions, and Opinions towards Artificial intelligence (AI) among pharmacy undergraduate students at King Saud University (KSU), Riyadh, Saudi Arabia. Materials and Methods: A cross-sectional, questionnaire-based study was conducted between December 2022 and January 2023 using online questionnaires. The data collection was carried out using convenience sampling methods among senior pharmacy students at the College of Pharmacy, King Saud University. Statistical Package for the Social Sciences version 26 was used to analyze the data (SPSS). Results: A total of one hundred and fifty-seven pharmacy students completed the questionnaires. Of these, most of them (n = 118; 75.2%) were males. About 42%, (n = 65) were in their fourth year of study. Most of the students (n = 116; 73.9%) knew about AI. In addition, 69.4% (n = 109) of the students thought that AI is a tool that helps healthcare professionals (HCP). However, more than half 57.3% (n = 90) of the students were aware that AI would assist healthcare professionals in becoming better with the widespread use of AI. Furthermore, 75.1% of the students agreed that AI reduces errors in medical practice. The mean positive perception score was 29.8 (SD = 9.63; range-0-38). The mean score was significantly associated with age (p = 0.030), year of study (p = 0.040), and nationality (p = 0.013). The gender of the participants was found to have no significant association with the mean positive perception score (p = 0.916). Conclusions: Overall, pharmacy students showed good awareness of AI in Saudi Arabia. Moreover, the majority of the students had positive perceptions about the concepts, benefits, and implementation of AI. Moreover, most students indicated that there is a need for more education and training in the field of AI. Consequently, early exposure to content related to AI in the curriculum of pharmacy is an important step to help in the wide use of these technologies in the graduates' future careers.
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Affiliation(s)
- Wajid Syed
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
| | - Mahmood Basil A Al-Rawi
- Department of Optometry, College of Applied Medical Sciences, King Saud University, Riyadh 11451, Saudi Arabia
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11
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Nadgir R. Why Are We Still Sitting in the Dark? Radiology as a Career Choice in the Setting of an Emerging Technology Revolution. Acad Radiol 2023; 30:1189-1191. [PMID: 37061451 DOI: 10.1016/j.acra.2023.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 03/13/2023] [Indexed: 04/17/2023]
Affiliation(s)
- Rohini Nadgir
- Johns Hopkins Medicine, 600 N. Wolfe Street, Baltimore, MD.
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12
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Hernández-Rodríguez J, Rodríguez-Conde MJ, Santos-Sánchez JÁ, Cabrero-Fraile FJ. Development and validation of an educational software based in artificial neural networks for training in radiology (JORCAD) through an interactive learning activity. Heliyon 2023; 9:e14780. [PMID: 37025816 PMCID: PMC10070709 DOI: 10.1016/j.heliyon.2023.e14780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 02/22/2023] [Accepted: 03/16/2023] [Indexed: 03/29/2023] Open
Abstract
The use of Computer Aided Detection (CAD) software has been previously documented as a valuable tool to improve specialist training in Radiology. This research assesses the utility of an educational software tool aimed to train residents in Radiology and other related medical specialties and students from Medicine degree. This in-house developed software, called JORCAD, integrates a CAD system based in Convolutional Neural Networks (CNNs) with annotated cases from radiological image databases. The methodology followed for software validation was expert judgement after completing an interactive learning activity. Participants received a theoretical session and a software usage tutorial and afterwards utilized the application in a dedicated workstation to analyze a series of proposed cases of thorax computed tomography (CT) and mammography. A total of 26 expert participants from the Radiology Department at Salamanca University Hospital (15 specialists and 11 residents) fulfilled the activity and evaluated different aspects through a series of surveys: software usability, case navigation tools, CAD module utility for learning and JORCAD educational capabilities. Participants also graded imaging cases to establish JORCAD usefulness for training radiology residents. According to the statistical analysis of survey results and expert cases scoring, along with their opinions, it can be concluded that JORCAD software is a useful tool for training future specialists. The combination of CAD with annotated cases from validated databases enhances learning, offering a second opinion and changing the usual training paradigm. Including software as JORCAD in residency training programs of Radiology and other medical specialties would have a positive effect on trainees' background knowledge.
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Affiliation(s)
- Jorge Hernández-Rodríguez
- Department of Biomedical and Diagnostic Sciences, Faculty of Medicine, University of Salamanca, C/Alfonso X El Sabio S/n (37007), Salamanca, Spain
- Department of Medical Physics and Radiation Protection. Salamanca University Hospital. Paseo de San Vicente 58-182 (37007), Salamanca, Spain
- Corresponding author. Department of Biomedical and Diagnostic Sciences, Faculty of Medicine, University of Salamanca, C/Alfonso X El Sabio s/n (37007), Salamanca, Spain.
| | - María-José Rodríguez-Conde
- University Institute of Educational Sciences (IUCE). Grupo de Investigación en InterAcción y ELearning (GRIAL). University of Salamanca, Paseo de Canalejas 169 (37008), Salamanca, Spain
| | - José-Ángel Santos-Sánchez
- Department of Biomedical and Diagnostic Sciences, Faculty of Medicine, University of Salamanca, C/Alfonso X El Sabio S/n (37007), Salamanca, Spain
- Department of Radiology. Salamanca University Hospital. Paseo de San Vicente 58-182 (37007), Salamanca, Spain
| | - Francisco-Javier Cabrero-Fraile
- Department of Biomedical and Diagnostic Sciences, Faculty of Medicine, University of Salamanca, C/Alfonso X El Sabio S/n (37007), Salamanca, Spain
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13
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Henckert D, Malorgio A, Schweiger G, Raimann FJ, Piekarski F, Zacharowski K, Hottenrott S, Meybohm P, Tscholl DW, Spahn DR, Roche TR. Attitudes of Anesthesiologists toward Artificial Intelligence in Anesthesia: A Multicenter, Mixed Qualitative-Quantitative Study. J Clin Med 2023; 12:jcm12062096. [PMID: 36983099 PMCID: PMC10054443 DOI: 10.3390/jcm12062096] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/16/2023] [Accepted: 03/01/2023] [Indexed: 03/30/2023] Open
Abstract
Artificial intelligence (AI) is predicted to play an increasingly important role in perioperative medicine in the very near future. However, little is known about what anesthesiologists know and think about AI in this context. This is important because the successful introduction of new technologies depends on the understanding and cooperation of end users. We sought to investigate how much anesthesiologists know about AI and what they think about the introduction of AI-based technologies into the clinical setting. In order to better understand what anesthesiologists think of AI, we recruited 21 anesthesiologists from 2 university hospitals for face-to-face structured interviews. The interview transcripts were subdivided sentence-by-sentence into discrete statements, and statements were then grouped into key themes. Subsequently, a survey of closed questions based on these themes was sent to 70 anesthesiologists from 3 university hospitals for rating. In the interviews, the base level of knowledge of AI was good at 86 of 90 statements (96%), although awareness of the potential applications of AI in anesthesia was poor at only 7 of 42 statements (17%). Regarding the implementation of AI in anesthesia, statements were split roughly evenly between pros (46 of 105, 44%) and cons (59 of 105, 56%). Interviewees considered that AI could usefully be used in diverse tasks such as risk stratification, the prediction of vital sign changes, or as a treatment guide. The validity of these themes was probed in a follow-up survey of 70 anesthesiologists with a response rate of 70%, which confirmed an overall positive view of AI in this group. Anesthesiologists hold a range of opinions, both positive and negative, regarding the application of AI in their field of work. Survey-based studies do not always uncover the full breadth of nuance of opinion amongst clinicians. Engagement with specific concerns, both technical and ethical, will prove important as this technology moves from research to the clinic.
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Affiliation(s)
- David Henckert
- Institute of Anaesthesiology, University and University Hospital of Zurich, 8091 Zurich, Switzerland
| | - Amos Malorgio
- Institute of Anaesthesiology, University and University Hospital of Zurich, 8091 Zurich, Switzerland
| | - Giovanna Schweiger
- Institute of Anaesthesiology, University and University Hospital of Zurich, 8091 Zurich, Switzerland
| | - Florian J Raimann
- Department of Anaesthesiology, Intensive Care and Pain Medicine, Frankfurt University Hospital, 60590 Frankfurt am Main, Germany
| | - Florian Piekarski
- Department of Anaesthesiology, Intensive Care and Pain Medicine, Frankfurt University Hospital, 60590 Frankfurt am Main, Germany
| | - Kai Zacharowski
- Department of Anaesthesiology, Intensive Care and Pain Medicine, Frankfurt University Hospital, 60590 Frankfurt am Main, Germany
| | - Sebastian Hottenrott
- Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Wuerzburg, 97080 Wuerzburg, Germany
| | - Patrick Meybohm
- Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Wuerzburg, 97080 Wuerzburg, Germany
| | - David W Tscholl
- Institute of Anaesthesiology, University and University Hospital of Zurich, 8091 Zurich, Switzerland
| | - Donat R Spahn
- Institute of Anaesthesiology, University and University Hospital of Zurich, 8091 Zurich, Switzerland
| | - Tadzio R Roche
- Institute of Anaesthesiology, University and University Hospital of Zurich, 8091 Zurich, Switzerland
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14
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Ganapathi S, Duggal S. Exploring the experiences and views of doctors working with Artificial Intelligence in English healthcare; a qualitative study. PLoS One 2023; 18:e0282415. [PMID: 36862694 PMCID: PMC9980725 DOI: 10.1371/journal.pone.0282415] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 02/14/2023] [Indexed: 03/03/2023] Open
Abstract
BACKGROUND The National Health Service (NHS) aspires to be a world leader of Artificial Intelligence (AI) in healthcare, however, there are several barriers facing translation and implementation. A key enabler of AI within the NHS is the education and engagement of doctors, however evidence suggests that there is an overall lack of awareness of and engagement with AI. RESEARCH AIM This qualitative study explores the experiences and views of doctor developers working with AI within the NHS exploring; their role within medical AI discourse, their views on the implementation of AI more widely and how they consider the engagement of doctors with AI technologies may increase in the future. METHODS This study involved eleven semi-structured, one-to-one interviews conducted with doctors working with AI in English healthcare. Data was subjected to thematic analysis. RESULTS The findings demonstrate that there is an unstructured pathway for doctors to enter the field of AI. The doctors described the various challenges they had experienced during their career, with many arising from the differing demands of operating in a commercial and technological environment. The perceived awareness and engagement among frontline doctors was low, with two prominent barriers being the hype surrounding AI and a lack of protected time. The engagement of doctors is vital for both the development and adoption of AI. CONCLUSIONS AI offers big potential within the medical field but is still in its infancy. For the NHS to leverage the benefits of AI, it must educate and empower current and future doctors. This can be achieved through; informative education within the medical undergraduate curriculum, protecting time for current doctors to develop understanding and providing flexible opportunities for NHS doctors to explore this field.
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Affiliation(s)
- Shaswath Ganapathi
- University of Birmingham Medical School, Birmingham, United Kingdom
- * E-mail:
| | - Sandhya Duggal
- University of Birmingham Medical School, Birmingham, United Kingdom
- The Strategy Unit, Midlands Lancashire Commissioning Support Unit, Leyland, United Kingdom
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15
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Artificial Intelligence Curriculum Needs Assessment for a Pediatric Radiology Fellowship Program: What, How, and Why? Acad Radiol 2023; 30:349-358. [PMID: 35753935 DOI: 10.1016/j.acra.2022.04.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/16/2022] [Accepted: 04/30/2022] [Indexed: 01/11/2023]
Abstract
RATIONALE AND OBJECTIVES Artificial intelligence (AI) holds enormous potential for improvements in patient care, efficiency, and innovation in pediatric radiology practice. Although there is a pressing need for a radiology-specific training curriculum and formalized AI teaching, few resources are available. The purpose of our study was to perform a needs assessment for the development of an AI curriculum during pediatric radiology training and continuing education. MATERIALS AND METHODS A focus group study using a semistructured moderator-guided interview was conducted with radiology trainees' and attending radiologists' perceptions of AI, perceived competence in interpretation of AI literature, and perceived expectations from radiology AI educational programs. The focus group was audio-recorded, transcribed, and thematic analysis was performed. RESULTS The focus group was held virtually with seven participants. The following themes we identified: (1) AI knowledge, (2) previous training, (3) learning preferences, (4) AI expectations, and (5) AI concerns. The participants had no previous formal training in AI and variability in perceived needs and interests. Most preferred a case-based approach to teaching AI. They expressed incomplete understanding of AI hindered its clinical applicability and reiterated a need for improved training in the interpretation and application of AI literature in their practice. CONCLUSION We found heterogeneity in perspectives about AI; thus, a curriculum must account for the wide range of these interests and needs. Teaching the interpretation of AI research methods, literature critique, and quality control through implementation of specific scenarios could engage a variety of trainees from different backgrounds and interest levels while ensuring a baseline level of competency in AI.
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16
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Silkens MEWM, Ross J, Hall M, Scarbrough H, Rockall A. The time is now: making the case for a UK registry of deployment of radiology artificial intelligence applications. Clin Radiol 2023; 78:107-114. [PMID: 36639171 DOI: 10.1016/j.crad.2022.09.132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 09/02/2022] [Accepted: 09/06/2022] [Indexed: 01/12/2023]
Abstract
Artificial intelligence (AI)-based healthcare applications (apps) are rapidly evolving, and radiology is a target specialty for their implementation. In this paper, we put the case for a national deployment registry to track the spread of AI apps into clinical use in radiology in the UK. By gathering data on the specific locations, purposes, and people associated with AI app deployment, such a registry would provide greater transparency on their spread in the radiology field. In combination with other regulatory and audit mechanisms, it would provide radiologists and patients with greater confidence and trust in AI apps. At the same time, coordination of this information would reduce costs for the National Health Service (NHS) by preventing duplication of piloting activities. This commentary discusses the need for a UK-wide registry for such apps, its benefits and risks, and critical success factors for its establishment. We conclude by noting that a critical window of opportunity has opened up for the development of a deployment registry, before the current pattern of localised clusters of activity turns into the widespread proliferation of AI apps across clinical practice.
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Affiliation(s)
- M E W M Silkens
- Centre for Healthcare Innovation Research, City University of London, London, UK.
| | - J Ross
- Department of Cancer and Surgery, Imperial College London, London, UK
| | - M Hall
- Queen Elizabeth University Hospital, Glasgow, UK
| | - H Scarbrough
- Centre for Healthcare Innovation Research, City University of London, London, UK
| | - A Rockall
- Department of Cancer and Surgery, Imperial College London, London, UK
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17
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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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Abstract
In the era of artificial intelligence (AI), a great deal of attention is being paid to AI in radiological practice. There are a large number of AI products on the radiological market based on X-rays, computed tomography, magnetic resonance imaging, and ultrasound. AI will not only change the way of radiological practice but also the way of radiological education. It is still not clearly defined about the exact role AI will play in radiological practice, but it will certainly be consolidated into radiological education in the foreseeable future. However, there are few literatures that have comprehensively summarized the attitudes, opportunities and challenges that AI can pose in the different training phases of radiologists, from university education to continuing education. Herein, we describe medical students' attitudes towards AI, summarize the role of AI in radiological education, and analyze the challenges that AI can pose in radiological education.
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Affiliation(s)
- Chao Wang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- * Correspondence: Chao Wang, Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88 Jiefang Road, Hangzhou 310009 Zhejiang, China (e-mail: )
| | - Huanhuan Xie
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Shan Wang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Siyu Yang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ling Hu
- Department of Ultrasound, Hangzhou Women’s Hospital, Hangzhou, Zhejiang, China
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Santos GNM, da Silva HEC, Figueiredo PTDS, Mesquita CRM, Melo NS, Stefani CM, Leite AF. The Introduction of Artificial Intelligence in Diagnostic Radiology Curricula: a Text and Opinion Systematic Review. INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION 2022. [DOI: 10.1007/s40593-022-00324-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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20
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Jin H, Wagner MW, Ertl-Wagner B, Khalvati F. An Educational Graphical User Interface to Construct Convolutional Neural Networks for Teaching Artificial Intelligence in Radiology. Can Assoc Radiol J 2022:8465371221144264. [PMID: 36475925 DOI: 10.1177/08465371221144264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Deep learning techniques using convolutional neural networks (CNNs) have been successfully developed for various medical image analysis tasks. However, the skills to understand and develop deep learning models are not usually taught during radiology training, which constitutes a barrier for radiologists looking to integrate machine learning (ML) into their research or clinical practice. In this work, we developed and evaluated an educational graphical user interface (GUI) to construct CNNs for teaching deep learning concepts to radiology trainees. The GUI was developed in Python using the PyQt and PyTorch frameworks. The functionality of the GUI was demonstrated through a binary classification task on a dataset of MR images of the brain. The usability of the GUI was assessed through 45-min user testing sessions with 5 neuroradiologists and neuroradiology fellows, assessing mean task completion times, the System Usability Scale (SUS), and a qualitative questionnaire as metrics. Task completion times were compared against a ML expert who performed the same tasks. After a 20-min introduction to CNNs and a walkthrough of the GUI, users were able to perform all assigned tasks successfully. There was no significant difference in task completion time compared to a ML expert. The educational GUI achieved a score of 82.5 on the SUS, suggesting that the system is highly usable. Users indicated that the GUI seems useful as an educational tool to teach ML topics to radiology trainees. An educational GUI allows interactive teaching in ML that can be incorporated into radiology training.
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Affiliation(s)
- Haiyue Jin
- Division of Engineering Science, University of Toronto, Toronto, ON, Canada
| | - Matthias W Wagner
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada,Neurosciences and Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, ON, Canada,Division of Neuroradiology, Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada
| | - Birgit Ertl-Wagner
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada,Neurosciences and Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, ON, Canada,Division of Neuroradiology, Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada
| | - Farzad Khalvati
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada,Neurosciences and Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, ON, Canada,Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada,Department of Computer Science, University of Toronto, Toronto, ON, Canada
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21
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Caparrós Galán G, Sendra Portero F. Medical students’ perceptions of the impact of artificial intelligence in radiology. RADIOLOGIA 2022; 64:516-524. [DOI: 10.1016/j.rxeng.2021.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 03/17/2021] [Indexed: 11/18/2022]
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22
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Santomartino SM, Yi PH. Systematic Review of Radiologist and Medical Student Attitudes on the Role and Impact of AI in Radiology. Acad Radiol 2022; 29:1748-1756. [PMID: 35105524 DOI: 10.1016/j.acra.2021.12.032] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/30/2021] [Accepted: 12/30/2021] [Indexed: 12/28/2022]
Abstract
RATIONALE AND OBJECTIVES The introduction of AI in radiology has prompted both excitement and hesitation within the field. We performed a systematic review of original studies evaluating the attitudes of radiologists, radiology trainees, and medical students towards AI in radiology. MATERIALS AND METHODS We searched PubMed for studies published as of August 24, 2021 for original studies evaluating attitudes of radiologists (attendings and trainees) and medical students towards AI in radiology. We summarized the baseline article characteristics and performed thematic analysis of the questions asked in each study. RESULTS Nineteen studies were included evaluating attitudes across different levels of training (medical students, radiology trainees, and radiology attendings) with representation from nearly every continent. Medical students and radiologists alike favored increased educational initiatives, and displayed interest in learning about and implementing AI solutions themselves, despite reporting of a current gap in formal AI training. There was general optimism about the role of AI in radiology, although radiologists and trainees had greater consensus than medical students. CONCLUSION Although there is interest in incorporating AI into medical education and optimism among radiologists towards AI, medical students are more divided in their views. We propose that outreach to and AI education for medical students may help improve their attitudes towards the potentially transformative technology of AI for radiology.
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Affiliation(s)
- Samantha M Santomartino
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Paul H Yi
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, Maryland; Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland.
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The Use of Artificial Intelligence in Medical Imaging: A Nationwide Pilot Survey of Trainees in Saudi Arabia. Clin Pract 2022; 12:852-866. [PMID: 36412669 PMCID: PMC9680253 DOI: 10.3390/clinpract12060090] [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: 07/24/2022] [Revised: 10/20/2022] [Accepted: 10/24/2022] [Indexed: 12/14/2022] Open
Abstract
Artificial intelligence is dramatically transforming medical imaging. In Saudi Arabia, there are a lack of studies assessing the level of artificial intelligence use and reliably determining the perceived impact of artificial intelligence on the radiology workflow and the profession. We assessed the levels of artificial intelligence use among radiology trainees and correlated the perceived impact of artificial intelligence on the workflow and profession with the behavioral intention to use artificial intelligence. This cross-sectional study enrolled radiology trainees from Saudi Arabia, and a 5-part-structured questionnaire was disseminated. The items concerning the perceived impact of artificial intelligence on the radiology workflow conformed to the six-step standard workflow in radiology, which includes ordering and scheduling, protocoling and acquisition, image interpretation, reporting, communication, and billing. We included 98 participants. Few used artificial intelligence in routine practice (7%). The perceived impact of artificial intelligence on the radiology workflow was at a considerable level in all radiology workflow steps (range, 3.64−3.97 out of 5). Behavioral intention to use artificial intelligence was linearly correlated with the perceptions of its impact on the radiology workflow and on the profession (p < 0.001). Artificial intelligence is used at a low level in radiology. The perceived impact of artificial intelligence on radiology workflow and the profession is correlated to an increase in behavioral intention to use artificial intelligence. Thus, increasing awareness about the positive impact of artificial intelligence can improve its adoption.
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Khosravi B, Rouzrokh P, Faghani S, Moassefi M, Vahdati S, Mahmoudi E, Chalian H, Erickson BJ. Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review. Diagnostics (Basel) 2022; 12:2512. [PMID: 36292201 PMCID: PMC9600598 DOI: 10.3390/diagnostics12102512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/14/2022] [Accepted: 10/15/2022] [Indexed: 01/17/2023] Open
Abstract
Machine-learning (ML) and deep-learning (DL) algorithms are part of a group of modeling algorithms that grasp the hidden patterns in data based on a training process, enabling them to extract complex information from the input data. In the past decade, these algorithms have been increasingly used for image processing, specifically in the medical domain. Cardiothoracic imaging is one of the early adopters of ML/DL research, and the COVID-19 pandemic resulted in more research focus on the feasibility and applications of ML/DL in cardiothoracic imaging. In this scoping review, we systematically searched available peer-reviewed medical literature on cardiothoracic imaging and quantitatively extracted key data elements in order to get a big picture of how ML/DL have been used in the rapidly evolving cardiothoracic imaging field. During this report, we provide insights on different applications of ML/DL and some nuances pertaining to this specific field of research. Finally, we provide general suggestions on how researchers can make their research more than just a proof-of-concept and move toward clinical adoption.
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Affiliation(s)
- Bardia Khosravi
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Pouria Rouzrokh
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Orthopedic Surgery Artificial Intelligence Laboratory (OSAIL), Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Shahriar Faghani
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Mana Moassefi
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Sanaz Vahdati
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Elham Mahmoudi
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Hamid Chalian
- Department of Radiology, Cardiothoracic Imaging, University of Washington, Seattle, WA 98195, USA
| | - Bradley J. Erickson
- Radiology Informatics Lab (RIL), Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
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Artificial Intelligence in Biological Sciences. Life (Basel) 2022; 12:life12091430. [PMID: 36143468 PMCID: PMC9505413 DOI: 10.3390/life12091430] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 08/25/2022] [Accepted: 09/10/2022] [Indexed: 12/03/2022] Open
Abstract
Artificial intelligence (AI), currently a cutting-edge concept, has the potential to improve the quality of life of human beings. The fields of AI and biological research are becoming more intertwined, and methods for extracting and applying the information stored in live organisms are constantly being refined. As the field of AI matures with more trained algorithms, the potential of its application in epidemiology, the study of host–pathogen interactions and drug designing widens. AI is now being applied in several fields of drug discovery, customized medicine, gene editing, radiography, image processing and medication management. More precise diagnosis and cost-effective treatment will be possible in the near future due to the application of AI-based technologies. In the field of agriculture, farmers have reduced waste, increased output and decreased the amount of time it takes to bring their goods to market due to the application of advanced AI-based approaches. Moreover, with the use of AI through machine learning (ML) and deep-learning-based smart programs, one can modify the metabolic pathways of living systems to obtain the best possible outputs with the minimal inputs. Such efforts can improve the industrial strains of microbial species to maximize the yield in the bio-based industrial setup. This article summarizes the potentials of AI and their application to several fields of biology, such as medicine, agriculture, and bio-based industry.
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A Survey on the Use of Artificial Intelligence by Clinicians in Dentistry and Oral and Maxillofacial Surgery. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58081059. [PMID: 36013526 PMCID: PMC9412897 DOI: 10.3390/medicina58081059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/19/2022] [Accepted: 08/03/2022] [Indexed: 11/17/2022]
Abstract
Background: Applications of artificial intelligence (AI) in medicine and dentistry have been on the rise in recent years. In dental radiology, deep learning approaches have improved diagnostics, outperforming clinicians in accuracy and efficiency. This study aimed to provide information on clinicians' knowledge and perceptions regarding AI. Methods: A 21-item questionnaire was used to study the views of dentistry professionals on AI use in clinical practice. Results: In total, 302 questionnaires were answered and assessed. Most of the respondents rated their knowledge of AI as average (37.1%), below average (22.2%) or very poor (23.2%). The participants were largely convinced that AI would improve and bring about uniformity in diagnostics (mean Likert ± standard deviation 3.7 ± 1.27). Among the most serious concerns were the responsibility for machine errors (3.7 ± 1.3), data security or privacy issues (3.5 ± 1.24) and the divestment of healthcare to large technology companies (3.5 ± 1.28). Conclusions: Within the limitations of this study, insights into the acceptance and use of AI in dentistry are revealed for the first time.
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Saw SN, Ng KH. Current challenges of implementing artificial intelligence in medical imaging. Phys Med 2022; 100:12-17. [PMID: 35714523 DOI: 10.1016/j.ejmp.2022.06.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 04/26/2022] [Accepted: 06/11/2022] [Indexed: 12/31/2022] Open
Abstract
The idea of using artificial intelligence (AI) in medical practice has gained vast interest due to its potential to revolutionise healthcare systems. However, only some AI algorithms are utilised due to systems' uncertainties, besides the never-ending list of ethical and legal concerns. This paper intends to provide an overview of current AI challenges in medical imaging with an ultimate aim to foster better and effective communication among various stakeholders to encourage AI technology development. We identify four main challenges in implementing AI in medical imaging, supported with consequences and past events when these problems fail to mitigate. Among them is the creation of a robust AI algorithm that is fair, trustable and transparent. Another issue is on data governance, in which best practices in data sharing must be established to promote trust and protect the patients' privacy. Next, stakeholders, such as the government, technology companies and hospital management, should come to a consensus in creating trustworthy AI policies and regulatory frameworks, which is the fourth challenge, to support, encourage and spur innovation in digital AI healthcare technology. Lastly, we discussed the efforts of various organizations such as the World Health Organisation (WHO), American College of Radiology (ACR), European Society of Radiology (ESR) and Radiological Society of North America (RSNA), who are already actively pursuing ethical developments in AI. The efforts by various stakeholders will eventually overcome hurdles and the deployment of AI-driven healthcare applications in clinical practice will become a reality and hence lead to better healthcare services and outcomes.
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Affiliation(s)
- Shier Nee Saw
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Kwan Hoong Ng
- Department of Biomedical Imaging, Universiti Malaya, 50603 Kuala Lumpur, Malaysia; Department of Medical Imaging and Radiological Sciences, College of Health Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
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An interdisciplinary review of AI and HRM: Challenges and future directions. HUMAN RESOURCE MANAGEMENT REVIEW 2022. [DOI: 10.1016/j.hrmr.2022.100924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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The current state of knowledge on imaging informatics: a survey among Spanish radiologists. Insights Imaging 2022; 13:34. [PMID: 35235068 PMCID: PMC8891400 DOI: 10.1186/s13244-022-01164-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 01/22/2022] [Indexed: 11/22/2022] Open
Abstract
Background There is growing concern about the impact of artificial intelligence (AI) on radiology and the future of the profession. The aim of this study is to evaluate general knowledge and concerns about trends on imaging informatics among radiologists working in Spain (residents and attending physicians). For this purpose, an online survey among radiologists working in Spain was conducted with questions related to: knowledge about terminology and technologies, need for a regulated academic training on AI and concerns about the implications of the use of these technologies. Results A total of 223 radiologists answered the survey, of whom 76.7% were attending physicians and 23.3% residents. General terms such as AI and algorithm had been heard of or read in at least 75.8% and 57.4% of the cases, respectively, while more specific terms were scarcely known. All the respondents consider that they should pursue academic training in medical informatics and new technologies, and 92.9% of them reckon this preparation should be incorporated in the training program of the specialty. Patient safety was found to be the main concern for 54.2% of the respondents. Job loss was not seen as a peril by 45.7% of the participants.
Conclusions Although there is a lack of knowledge about AI among Spanish radiologists, there is a will to explore such topics and a general belief that radiologists should be trained in these matters. Based on the results, a consensus is needed to change the current training curriculum to better prepare future radiologists.
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Yang L, Ene IC, Arabi Belaghi R, Koff D, Stein N, Santaguida PL. Stakeholders' perspectives on the future of artificial intelligence in radiology: a scoping review. Eur Radiol 2022; 32:1477-1495. [PMID: 34545445 DOI: 10.1007/s00330-021-08214-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 06/11/2021] [Accepted: 07/12/2021] [Indexed: 12/31/2022]
Abstract
OBJECTIVES Artificial intelligence (AI) has the potential to impact clinical practice and healthcare delivery. AI is of particular significance in radiology due to its use in automatic analysis of image characteristics. This scoping review examines stakeholder perspectives on AI use in radiology, the benefits, risks, and challenges to its integration. METHODS A search was conducted from 1960 to November 2019 in EMBASE, PubMed/MEDLINE, Web of Science, Cochrane Library, CINAHL, and grey literature. Publications reflecting stakeholder attitudes toward AI were included with no restrictions. RESULTS Commentaries (n = 32), surveys (n = 13), presentation abstracts (n = 8), narrative reviews (n = 8), and a social media study (n = 1) were included from 62 eligible publications. These represent the views of radiologists, surgeons, medical students, patients, computer scientists, and the general public. Seven themes were identified (predicted impact, potential replacement, trust in AI, knowledge of AI, education, economic considerations, and medicolegal implications). Stakeholders anticipate a significant impact on radiology, though replacement of radiologists is unlikely in the near future. Knowledge of AI is limited for non-computer scientists and further education is desired. Many expressed the need for collaboration between radiologists and AI specialists to successfully improve patient care. CONCLUSIONS Stakeholder views generally suggest that AI can improve the practice of radiology and consider the replacement of radiologists unlikely. Most stakeholders identified the need for education and training on AI, as well as collaborative efforts to improve AI implementation. Further research is needed to gain perspectives from non-Western countries, non-radiologist stakeholders, on economic considerations, and medicolegal implications. KEY POINTS Stakeholders generally expressed that AI alone cannot be used to replace radiologists. The scope of practice is expected to shift with AI use affecting areas from image interpretation to patient care. Patients and the general public do not know how to address potential errors made by AI systems while radiologists believe that they should be "in-the-loop" in terms of responsibility. Ethical accountability strategies must be developed across governance levels. Students, residents, and radiologists believe that there is a lack in AI education during medical school and residency. The radiology community should work with IT specialists to ensure that AI technology benefits their work and centres patients.
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Affiliation(s)
- Ling Yang
- McMaster University, 1280 Main St W, Hamilton, ON, L8S 4L8, Canada
| | - Ioana Cezara Ene
- McMaster University, 1280 Main St W, Hamilton, ON, L8S 4L8, Canada
| | - Reza Arabi Belaghi
- University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan Province, Iran
| | - David Koff
- Department of Radiology, McMaster University, 1280 Main St W, Hamilton, ON, L8S 4L8, Canada
| | - Nina Stein
- McMaster Children's Hospital, McMaster University, 1280 Main St W, Hamilton, ON, L8N 3Z5, Canada
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Challenges of Radiology education in the era of artificial intelligence. RADIOLOGIA 2022; 64:54-59. [DOI: 10.1016/j.rxeng.2020.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 10/02/2020] [Indexed: 11/29/2022]
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Shinners L, Grace S, Smith S, Stephens A, Aggar C. Exploring healthcare professionals' perceptions of artificial intelligence: Piloting the Shinners Artificial Intelligence Perception tool. Digit Health 2022; 8:20552076221078110. [PMID: 35154807 PMCID: PMC8832586 DOI: 10.1177/20552076221078110] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 01/18/2022] [Indexed: 12/31/2022] Open
Abstract
Objective There is an urgent need to prepare the healthcare workforce for the
implementation of artificial intelligence (AI) into the healthcare setting.
Insights into workforce perception of AI could identify potential challenges
that an organisation may face when implementing this new technology. The aim
of this study was to psychometrically evaluate and pilot the Shinners
Artificial Intelligence Perception (SHAIP) questionnaire that is designed to
explore healthcare professionals’ perceptions of AI. Instrument validation
was achieved through a cross-sectional study of healthcare professionals
(n = 252) from a regional health district in
Australia. Methods and Results Exploratory factor analysis was conducted and analysis yielded a two-factor
solution consisting of 10 items and explained 51.7% of the total variance.
Factor one represented perceptions of ‘Professional impact of
AI’ (α = .832) and Factor two represented ‘Preparedness
for AI’ (α = .632). An analysis of variance indicated that ‘use
of AI’ had a significant effect on healthcare professionals’ perceptions of
both factors. ‘Discipline’ had a significant effect on Allied Health
professionals’ perception of Factor one and low mean scale score across all
disciplines suggests that all disciplines perceive that they are not
prepared for AI. Conclusions The results of this study provide preliminary support for the SHAIP tool and
a two-factor solution that measures healthcare professionals’ perceptions of
AI. Further testing is needed to establish the reliability or re-modelling
of Factor 2 and the overall performance of the SHAIP tool as a global
instrument.
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Affiliation(s)
- Lucy Shinners
- (Faculty of Health), Southern Cross University, Australia
| | - Sandra Grace
- (Faculty of Health), Southern Cross University, Australia
| | - Stuart Smith
- (Faculty of Health), Southern Cross University, Australia
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Jones CM, Danaher L, Milne MR, Tang C, Seah J, Oakden-Rayner L, Johnson A, Buchlak QD, Esmaili N. Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study. BMJ Open 2021; 11:e052902. [PMID: 34930738 PMCID: PMC8689166 DOI: 10.1136/bmjopen-2021-052902] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVES Artificial intelligence (AI) algorithms have been developed to detect imaging features on chest X-ray (CXR) with a comprehensive AI model capable of detecting 124 CXR findings being recently developed. The aim of this study was to evaluate the real-world usefulness of the model as a diagnostic assistance device for radiologists. DESIGN This prospective real-world multicentre study involved a group of radiologists using the model in their daily reporting workflow to report consecutive CXRs and recording their feedback on level of agreement with the model findings and whether this significantly affected their reporting. SETTING The study took place at radiology clinics and hospitals within a large radiology network in Australia between November and December 2020. PARTICIPANTS Eleven consultant diagnostic radiologists of varying levels of experience participated in this study. PRIMARY AND SECONDARY OUTCOME MEASURES Proportion of CXR cases where use of the AI model led to significant material changes to the radiologist report, to patient management, or to imaging recommendations. Additionally, level of agreement between radiologists and the model findings, and radiologist attitudes towards the model were assessed. RESULTS Of 2972 cases reviewed with the model, 92 cases (3.1%) had significant report changes, 43 cases (1.4%) had changed patient management and 29 cases (1.0%) had further imaging recommendations. In terms of agreement with the model, 2569 cases showed complete agreement (86.5%). 390 (13%) cases had one or more findings rejected by the radiologist. There were 16 findings across 13 cases (0.5%) deemed to be missed by the model. Nine out of 10 radiologists felt their accuracy was improved with the model and were more positive towards AI poststudy. CONCLUSIONS Use of an AI model in a real-world reporting environment significantly improved radiologist reporting and showed good agreement with radiologists, highlighting the potential for AI diagnostic support to improve clinical practice.
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Affiliation(s)
- Catherine M Jones
- Annalise-AI, Sydney, New South Wales, Australia
- I-Med Radiology Network, Sydney, New South Wales, Australia
| | - Luke Danaher
- I-Med Radiology Network, Sydney, New South Wales, Australia
| | - Michael R Milne
- Annalise-AI, Sydney, New South Wales, Australia
- I-Med Radiology Network, Sydney, New South Wales, Australia
| | - Cyril Tang
- Annalise-AI, Sydney, New South Wales, Australia
| | - Jarrel Seah
- Annalise-AI, Sydney, New South Wales, Australia
- Department of Radiology, Alfred Health, Melbourne, Victoria, Australia
| | - Luke Oakden-Rayner
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, South Australia, Australia
| | | | - Quinlan D Buchlak
- Annalise-AI, Sydney, New South Wales, Australia
- School of Medicine, The University of Notre Dame Australia School of Medicine Sydney Campus, Darlinghurst, New South Wales, Australia
| | - Nazanin Esmaili
- School of Medicine, The University of Notre Dame Australia School of Medicine Sydney Campus, Darlinghurst, New South Wales, Australia
- Faculty of Engineering and IT, University of Technology Sydney, Sydney, New South Wales, Australia
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Charow R, Jeyakumar T, Younus S, Dolatabadi E, Salhia M, Al-Mouaswas D, Anderson M, Balakumar S, Clare M, Dhalla A, Gillan C, Haghzare S, Jackson E, Lalani N, Mattson J, Peteanu W, Tripp T, Waldorf J, Williams S, Tavares W, Wiljer D. Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review. JMIR MEDICAL EDUCATION 2021; 7:e31043. [PMID: 34898458 PMCID: PMC8713099 DOI: 10.2196/31043] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 10/04/2021] [Accepted: 10/04/2021] [Indexed: 05/12/2023]
Abstract
BACKGROUND As the adoption of artificial intelligence (AI) in health care increases, it will become increasingly crucial to involve health care professionals (HCPs) in developing, validating, and implementing AI-enabled technologies. However, because of a lack of AI literacy, most HCPs are not adequately prepared for this revolution. This is a significant barrier to adopting and implementing AI that will affect patients. In addition, the limited existing AI education programs face barriers to development and implementation at various levels of medical education. OBJECTIVE With a view to informing future AI education programs for HCPs, this scoping review aims to provide an overview of the types of current or past AI education programs that pertains to the programs' curricular content, modes of delivery, critical implementation factors for education delivery, and outcomes used to assess the programs' effectiveness. METHODS After the creation of a search strategy and keyword searches, a 2-stage screening process was conducted by 2 independent reviewers to determine study eligibility. When consensus was not reached, the conflict was resolved by consulting a third reviewer. This process consisted of a title and abstract scan and a full-text review. The articles were included if they discussed an actual training program or educational intervention, or a potential training program or educational intervention and the desired content to be covered, focused on AI, and were designed or intended for HCPs (at any stage of their career). RESULTS Of the 10,094 unique citations scanned, 41 (0.41%) studies relevant to our eligibility criteria were identified. Among the 41 included studies, 10 (24%) described 13 unique programs and 31 (76%) discussed recommended curricular content. The curricular content of the unique programs ranged from AI use, AI interpretation, and cultivating skills to explain results derived from AI algorithms. The curricular topics were categorized into three main domains: cognitive, psychomotor, and affective. CONCLUSIONS This review provides an overview of the current landscape of AI in medical education and highlights the skills and competencies required by HCPs to effectively use AI in enhancing the quality of care and optimizing patient outcomes. Future education efforts should focus on the development of regulatory strategies, a multidisciplinary approach to curriculum redesign, a competency-based curriculum, and patient-clinician interaction.
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Affiliation(s)
- Rebecca Charow
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
| | | | | | - Elham Dolatabadi
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
| | - Mohammad Salhia
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | - Dalia Al-Mouaswas
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | | | - Sarmini Balakumar
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | - Megan Clare
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | | | - Caitlin Gillan
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Shabnam Haghzare
- University Health Network, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | | | | | - Jane Mattson
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | - Wanda Peteanu
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | - Tim Tripp
- University Health Network, Toronto, ON, Canada
| | - Jacqueline Waldorf
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | | | - Walter Tavares
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Wilson Centre, Toronto, ON, Canada
| | - David Wiljer
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- CAMH Education, Centre for Addictions and Mental Health (CAMH), Toronto, ON, Canada
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Scott IA, Carter SM, Coiera E. Exploring stakeholder attitudes towards AI in clinical practice. BMJ Health Care Inform 2021; 28:bmjhci-2021-100450. [PMID: 34887331 PMCID: PMC8663096 DOI: 10.1136/bmjhci-2021-100450] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 11/14/2021] [Indexed: 12/31/2022] Open
Abstract
Objectives Different stakeholders may hold varying attitudes towards artificial intelligence (AI) applications in healthcare, which may constrain their acceptance if AI developers fail to take them into account. We set out to ascertain evidence of the attitudes of clinicians, consumers, managers, researchers, regulators and industry towards AI applications in healthcare. Methods We undertook an exploratory analysis of articles whose titles or abstracts contained the terms ‘artificial intelligence’ or ‘AI’ and ‘medical’ or ‘healthcare’ and ‘attitudes’, ‘perceptions’, ‘opinions’, ‘views’, ‘expectations’. Using a snowballing strategy, we searched PubMed and Google Scholar for articles published 1 January 2010 through 31 May 2021. We selected articles relating to non-robotic clinician-facing AI applications used to support healthcare-related tasks or decision-making. Results Across 27 studies, attitudes towards AI applications in healthcare, in general, were positive, more so for those with direct experience of AI, but provided certain safeguards were met. AI applications which automated data interpretation and synthesis were regarded more favourably by clinicians and consumers than those that directly influenced clinical decisions or potentially impacted clinician–patient relationships. Privacy breaches and personal liability for AI-related error worried clinicians, while loss of clinician oversight and inability to fully share in decision-making worried consumers. Both clinicians and consumers wanted AI-generated advice to be trustworthy, while industry groups emphasised AI benefits and wanted more data, funding and regulatory certainty. Discussion Certain expectations of AI applications were common to many stakeholder groups from which a set of dependencies can be defined. Conclusion Stakeholders differ in some but not all of their attitudes towards AI. Those developing and implementing applications should consider policies and processes that bridge attitudinal disconnects between different stakeholders.
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Affiliation(s)
- Ian A Scott
- Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia .,School of Clinical Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Stacy M Carter
- Australian Centre for Health Engagement Evidence and Values, School of Health and Society, University of Wollongong, Wollongong, New South Wales, Australia
| | - Enrico Coiera
- Centre for Clinical Informatics, Macquarie University, Sydney, New South Wales, Australia
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Ibanez V, Gunz S, Erne S, Rawdon EJ, Ampanozi G, Franckenberg S, Sieberth T, Affolter R, Ebert LC, Dobay A. RiFNet: Automated rib fracture detection in postmortem computed tomography. Forensic Sci Med Pathol 2021; 18:20-29. [PMID: 34709561 PMCID: PMC8921053 DOI: 10.1007/s12024-021-00431-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2021] [Indexed: 12/31/2022]
Abstract
Imaging techniques are widely used for medical diagnostics. In some cases, a lack of medical practitioners who can manually analyze the images can lead to a bottleneck. Consequently, we developed a custom-made convolutional neural network (RiFNet = Rib Fracture Network) that can detect rib fractures in postmortem computed tomography. In a retrospective cohort study, we retrieved PMCT data from 195 postmortem cases with rib fractures from July 2017 to April 2018 from our database. The computed tomography data were prepared using a plugin in the commercial imaging software Syngo.via whereby the rib cage was unfolded on a single-in-plane image reformation. Out of the 195 cases, a total of 585 images were extracted and divided into two groups labeled "with" and "without" fractures. These two groups were subsequently divided into training, validation, and test datasets to assess the performance of RiFNet. In addition, we explored the possibility of applying transfer learning techniques on our dataset by choosing two independent noncommercial off-the-shelf convolutional neural network architectures (ResNet50 V2 and Inception V3) and compared the performances of those two with RiFNet. When using pre-trained convolutional neural networks, we achieved an F1 score of 0.64 with Inception V3 and an F1 score of 0.61 with ResNet50 V2. We obtained an average F1 score of 0.91 ± 0.04 with RiFNet. RiFNet is efficient in detecting rib fractures on postmortem computed tomography. Transfer learning techniques are not necessarily well adapted to make classifications in postmortem computed tomography.
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Affiliation(s)
- Victor Ibanez
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Samuel Gunz
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Svenja Erne
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Eric J Rawdon
- Department of Mathematics, University of St. Thomas, St. Paul, Minnesota, 55105-1079, USA
| | - Garyfalia Ampanozi
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Sabine Franckenberg
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland.,Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - Till Sieberth
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Raffael Affolter
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Lars C Ebert
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland
| | - Akos Dobay
- Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland.
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Richardson ML, Garwood ER, Lee Y, Li MD, Lo HS, Nagaraju A, Nguyen XV, Probyn L, Rajiah P, Sin J, Wasnik AP, Xu K. Noninterpretive Uses of Artificial Intelligence in Radiology. Acad Radiol 2021; 28:1225-1235. [PMID: 32059956 DOI: 10.1016/j.acra.2020.01.012] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 01/08/2020] [Accepted: 01/09/2020] [Indexed: 12/12/2022]
Abstract
We deem a computer to exhibit artificial intelligence (AI) when it performs a task that would normally require intelligent action by a human. Much of the recent excitement about AI in the medical literature has revolved around the ability of AI models to recognize anatomy and detect pathology on medical images, sometimes at the level of expert physicians. However, AI can also be used to solve a wide range of noninterpretive problems that are relevant to radiologists and their patients. This review summarizes some of the newer noninterpretive uses of AI in radiology.
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Affiliation(s)
| | - Elisabeth R Garwood
- Department of Radiology, University of Massachusetts, Worcester, Massachusetts
| | - Yueh Lee
- Department of Radiology, University of North Carolina, Chapel Hill, North Carolina
| | - Matthew D Li
- Department of Radiology, Harvard Medical School/Massachusetts General Hospital, Boston, Massachusets
| | - Hao S Lo
- Department of Radiology, University of Washington, Seattle, Washington
| | - Arun Nagaraju
- Department of Radiology, University of Chicago, Chicago, Illinois
| | - Xuan V Nguyen
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Linda Probyn
- Department of Radiology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario
| | - Prabhakar Rajiah
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Jessica Sin
- Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
| | - Ashish P Wasnik
- Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Kali Xu
- Department of Medicine, Santa Clara Valley Medical Center, Santa Clara, California
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Shlivko IL, Garanina OY, Klemenova IA, Uskova KA, Mironycheva AM, Dardyk VI, Laskov VN. Artificial intelligence: how it works and criteria for assessment. CONSILIUM MEDICUM 2021. [DOI: 10.26442/20751753.2021.8.201148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Artificial intelligence is a term used to describe computer technology in the modeling of intelligent behavior and critical thinking comparable to that of humans. To date, some of the first areas of medicine to be influenced by advances in artificial intelligence technologies will be those most dependent on imaging. These include ophthalmology, radiology, and dermatology. In connection with the emergence of numerous medical applications, scientists have formulated criteria for their assessment. This list included: clinical validation, regular application updates, functional focus, cost, availability of an information block for specialists and patients, compliance with the conditions of government regulation, and registration. One of the applications that meet all the requirements is the ProRodinki software package, developed for use by patients and specialists in the Russian Federation. Taking into account a widespread and rapidly developing competitive environment, it is necessary to soberly treat the resources of such applications, not exaggerating their capabilities and not considering them as a substitute for a specialist.
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Pauwels R, Del Rey YC. Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: a multicenter survey. Dentomaxillofac Radiol 2021; 50:20200461. [PMID: 33353376 DOI: 10.1259/dmfr.20200461] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVES The aim of this study was to assess the attitude of dentists and dental students in Brazil regarding the impact of artificial intelligence (AI) in oral radiology, and to evaluate the effect of an introductory AI lecture on their attitude. METHODS A questionnaire was prepared, comprising statements regarding the future role of AI in oral radiology and dentistry. A lecture of approx. 1 h was prepared, comprising the basic principles of AI and a non-exhaustive overview of AI research in medicine and dentistry. Participants filled in the questionnaire prior to the lecture. After the lecture, the questionnaire was repeated. RESULTS Throughout 7 sessions at 6 locations, 293 questionnaires were collected. The majority of participants were undergraduate dental students (57%). Prior to the lecture, there was a strong agreement regarding the various future roles and expected impact of AI in oral radiology. Approximately, one-third of participants was concerned about AI. After the lecture, agreement regarding the different roles of AI in oral radiology increased, overall excitement regarding AI increased, and concerns regarding the potential replacement of oral radiologists decreased. CONCLUSIONS A generally positive attitude towards AI was found; an introductory lecture was beneficial towards this attitude and alleviated concerns regarding the effect of AI on the oral radiology profession. Given the unprecedented, ongoing revolution of AI-augmented radiology, it is pivotal to incorporate AI topics in dental training curricula.
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Affiliation(s)
- Ruben Pauwels
- Aarhus Institute of Advanced Studies, Aarhus University, Aarhus, Denmark
| | - Yumi Chokyu Del Rey
- Department of Dental Materials and Prosthodontics, Ribeirão Preto Dental School, University of São Paulo, São Paulo, Brazil
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Valikodath NG, Cole E, Ting DSW, Campbell JP, Pasquale LR, Chiang MF, Chan RVP. Impact of Artificial Intelligence on Medical Education in Ophthalmology. Transl Vis Sci Technol 2021; 10:14. [PMID: 34125146 PMCID: PMC8212436 DOI: 10.1167/tvst.10.7.14] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Clinical care in ophthalmology is rapidly evolving as artificial intelligence (AI) algorithms are being developed. The medical community and national and federal regulatory bodies are recognizing the importance of adapting to AI. However, there is a gap in physicians’ understanding of AI and its implications regarding its potential use in clinical care, and there are limited resources and established programs focused on AI and medical education in ophthalmology. Physicians are essential in the application of AI in a clinical context. An AI curriculum in ophthalmology can help provide physicians with a fund of knowledge and skills to integrate AI into their practice. In this paper, we provide general recommendations for an AI curriculum for medical students, residents, and fellows in ophthalmology.
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Affiliation(s)
- Nita G Valikodath
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, USA
| | - Emily Cole
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, USA
| | - Daniel S W Ting
- Singapore National Eye Center, Duke-NUS Medical School, Singapore
| | - J Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA
| | - Louis R Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA
| | - Michael F Chiang
- National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - R V Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, USA
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Zufall AG, Mark EJ, Pollack K, Russell M. Buried treasure - the teaching potential of Kodachrome slides brought into the digital age. Int J Dermatol 2021; 60:1418-1424. [PMID: 34176126 DOI: 10.1111/ijd.15708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 03/27/2021] [Accepted: 05/25/2021] [Indexed: 11/30/2022]
Abstract
Clinical images on Kodachrome slides have been used for decades in dermatologic education. While the technology to view these images is becoming obsolete, many training programs possess high-quality slides that have educational benefit. The University of Virginia Department of Dermatology possesses a collection of such slides that are currently being digitized and integrated into an educational software program. We present this article as a means of providing a uniform protocol for institutions with large Kodachrome collections to do the same. Our work has proven beneficial for both medical students interested in dermatology, allowing them to gain exposure to a variety of conditions that are not well emphasized in the general curriculum, as well as for dermatology residents, who use the program as a means to hone their diagnostic skills. Not only is there educational benefit to be derived from digitizing these slides but time is of the essence, as these slides can easily become damaged or degraded, and the technology needed to scan them is quickly becoming less available.
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Affiliation(s)
- Alina Gertrud Zufall
- Department of Dermatology, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Erica Jaclyn Mark
- Department of Dermatology, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Karlyn Pollack
- Department of Dermatology, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Mark Russell
- Department of Dermatology, University of Virginia School of Medicine, Charlottesville, VA, USA
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Valikodath NG, Al-Khaled T, Cole E, Ting DSW, Tu EY, Campbell JP, Chiang MF, Hallak JA, Chan RVP. Evaluation of pediatric ophthalmologists' perspectives of artificial intelligence in ophthalmology. J AAPOS 2021; 25:164.e1-164.e5. [PMID: 34087473 PMCID: PMC8328946 DOI: 10.1016/j.jaapos.2021.01.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 01/12/2021] [Accepted: 01/13/2021] [Indexed: 12/31/2022]
Abstract
PURPOSE To survey pediatric ophthalmologists on their perspectives of artificial intelligence (AI) in ophthalmology. METHODS This is a subgroup analysis of a study previously reported. In March 2019, members of the American Association for Pediatric Ophthalmology and Strabismus (AAPOS) were recruited via the online AAPOS discussion board to voluntarily complete a Web-based survey consisting of 15 items. Survey items assessed the extent participants "agreed" or "disagreed" with statements on the perceived benefits and concerns of AI in ophthalmology. Responses were analyzed using descriptive statistics. RESULTS A total of 80 pediatric ophthalmologists who are members of AAPOS completed the survey. The mean number of years since graduating residency was 21 years (range, 0-46). Overall, 91% (73/80) reported understanding the concept of AI, 70% (56/80) believed AI will improve the practice of ophthalmology, 68% (54/80) reported willingness to incorporate AI into their clinical practice, 65% (52/80) did not believe AI will replace physicians, and 71% (57/80) believed AI should be incorporated into medical school and residency curricula. However, 15% (12/80) were concerned that AI will replace physicians, 26% (21/80) believed AI will harm the patient-physician relationship, and 46% (37/80) reported concern over the diagnostic accuracy of AI. CONCLUSIONS Most pediatric ophthalmologists in this survey viewed the role of AI in ophthalmology positively.
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Affiliation(s)
- Nita G Valikodath
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Tala Al-Khaled
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Emily Cole
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Daniel S W Ting
- Singapore National Eye Center, Duke-NUS Medical School Singapore, Singapore
| | - Elmer Y Tu
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - J Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Michael F Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Joelle A Hallak
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - R V Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois.
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Gorospe-Sarasúa L, Muñoz-Olmedo JM, Sendra-Portero F, de Luis-García R. Challenges of Radiology education in the era of artificial intelligence. RADIOLOGIA 2021; 64:S0033-8338(20)30135-1. [PMID: 33966817 DOI: 10.1016/j.rx.2020.10.003] [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: 03/12/2020] [Revised: 07/25/2020] [Accepted: 10/02/2020] [Indexed: 11/29/2022]
Abstract
Artificial intelligence is a branch of computer science that is generating great expectations in medicine and particularly in radiology. Artificial intelligence will change not only the way we practice our profession, but also the way we teach it and learn it. Although the advent of artificial intelligence has led some to question whether it is necessary to continue training radiologists, there seems to be a consensus in the recent scientific literature that we should continue to train radiologists and that we should teach future radiologists about artificial intelligence and how to exploit it. The acquisition of competency in artificial intelligence should start in medical school, be consolidated in residency programs, and be maintained and updated during continuing medical education. This article aims to describe some of the challenges that artificial intelligencve can pose in the different stages of training in radiology, from medical school through continuing medical education.
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Affiliation(s)
- L Gorospe-Sarasúa
- Servicio de Radiodiagnóstico, Hospital Universitario Ramón y Cajal, Madrid, España.
| | - J M Muñoz-Olmedo
- Servicio de Radiodiagnóstico, Hospital Universitario de La Princesa, Madrid, España
| | - F Sendra-Portero
- Servicio de Radiología y Medicina Física, Facultad de Medicina, Universidad de Málaga, Málaga, España
| | - R de Luis-García
- Escuela de Ingeniería de Telecomunicaciones, Universidad de Valladolid, Valladolid, España
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Caparrós Galán G, Sendra Portero F. Medical students' perceptions of the impact of artificial intelligence in radiology. RADIOLOGIA 2021; 64:S0033-8338(21)00084-9. [PMID: 33934846 DOI: 10.1016/j.rx.2021.03.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 03/13/2021] [Accepted: 03/17/2021] [Indexed: 12/31/2022]
Abstract
OBJECTIVES To analyze medical students' perceptions of the impact of artificial intelligence in radiology. MATERIAL AND METHODS A structured questionnaire comprising 28 items organized into six sections was distributed to students of medicine in Spain in December 2019. RESULTS A total of 341 students responded. Of these, 27 (7.9%) included radiology among their three main choices for specialization, and 51.9% considered that they clearly understood what artificial intelligence is. The overall rate of correct answers to the objective true-or-false questions about artificial intelligence was 70.7%. Whereas 75.9% expressed their disagreement with the hypothesis that artificial intelligence would replace radiologists, only 41.9% disagreed with the hypothesis that the demand for radiologists would decrease in the future. Only 36.7% expressed concerns about the role of artificial intelligence related to choosing radiology as a specialty. A greater proportion of students in the early years of medical school agreed with statements that radiologists accept artificial-intelligence-related technological changes and work with the industry to apply them as well as with statements about the need to include basic training about artificial intelligence in the medical school curriculum. CONCLUSIONS The students surveyed are aware of the impact of artificial intelligence in daily life, but not of the current debate about its potential applications in radiology. In general, they think that artificial intelligence will revolutionize radiology without having an alarming effect on the employability of radiologists. The students surveyed think that it is necessary to provide basic training about artificial intelligence in undergraduate medical school programs.
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Affiliation(s)
- G Caparrós Galán
- Departamento de Radiología y Medicina Física, Facultad de Medicina, Málaga, España
| | - F Sendra Portero
- Departamento de Radiología y Medicina Física, Facultad de Medicina, Málaga, España.
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Chang KP, Lin SH, Chu YW. Artificial intelligence in gastrointestinal radiology: A review with special focus on recent development of magnetic resonance and computed tomography. Artif Intell Gastroenterol 2021; 2:27-41. [DOI: 10.35712/aig.v2.i2.27] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/21/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI), particularly the deep learning technology, have been proven influential to radiology in the recent decade. Its ability in image classification, segmentation, detection and reconstruction tasks have substantially assisted diagnostic radiology, and has even been viewed as having the potential to perform better than radiologists in some tasks. Gastrointestinal radiology, an important subspecialty dealing with complex anatomy and various modalities including endoscopy, have especially attracted the attention of AI researchers and engineers worldwide. Consequently, recently many tools have been developed for lesion detection and image construction in gastrointestinal radiology, particularly in the fields for which public databases are available, such as diagnostic abdominal magnetic resonance imaging (MRI) and computed tomography (CT). This review will provide a framework for understanding recent advancements of AI in gastrointestinal radiology, with a special focus on hepatic and pancreatobiliary diagnostic radiology with MRI and CT. For fields where AI is less developed, this review will also explain the difficulty in AI model training and possible strategies to overcome the technical issues. The authors’ insights of possible future development will be addressed in the last section.
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Affiliation(s)
- Kai-Po Chang
- PhD Program in Medical Biotechnology, National Chung Hsing University, Taichung 40227, Taiwan
- Department of Pathology, China Medical University Hospital, Taichung 40447, Taiwan
| | - Shih-Huan Lin
- PhD Program in Medical Biotechnology, National Chung Hsing University, Taichung 40227, Taiwan
| | - Yen-Wei Chu
- PhD Program in Medical Biotechnology, National Chung Hsing University, Taichung 40227, Taiwan
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung 40227, Taiwan
- Institute of Molecular Biology, National Chung Hsing University, Taichung 40227, Taiwan
- Agricultural Biotechnology Center, National Chung Hsing University, Taichung 40227, Taiwan
- Biotechnology Center, National Chung Hsing University, Taichung 40227, Taiwan
- PhD Program in Translational Medicine, National Chung Hsing University, Taichung 40227, Taiwan
- Rong Hsing Research Center for Translational Medicine, Taichung 40227, Taiwan
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Thoracic Radiologists' Versus Computer Scientists' Perspectives on the Future of Artificial Intelligence in Radiology. J Thorac Imaging 2021; 35:255-259. [PMID: 31609778 DOI: 10.1097/rti.0000000000000453] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND There is intense interest and speculation in the application of artificial intelligence (AI) to radiology. The goals of this investigation were (1) to assess thoracic radiologists' perspectives on the role and expected impact of AI in radiology, and (2) to compare radiologists' perspectives with those of computer science (CS) experts working in the AI development. METHODS An online survey was developed and distributed to chest radiologists and CS experts at leading academic centers and societies, comparing their expectations of AI's influence on radiologists' jobs, job satisfaction, salary, and role in society. RESULTS A total of 95 radiologists and 45 computer scientists responded. Computer scientists reported having read more scientific journal articles on AI/machine learning in the past year than radiologists (mean [95% confidence interval]=17.1 [9.01-25.2] vs. 7.3 [4.7-9.9], P=0.0047). The impact of AI in radiology is expected to be high, with 57.8% and 73.3% of computer scientists and 31.6% and 61.1% of chest radiologists predicting radiologists' job will be dramatically different in 5 to 10 years, and 10 to 20 years, respectively. Although very few practitioners in both fields expect radiologists to become obsolete, with 0% expecting radiologist obsolescence in 5 years, in the long run, significantly more computer scientists (15.6%) predict radiologist obsolescence in 10 to 20 years, as compared with 3.2% of radiologists reporting the same (P=0.0128). Overall, both chest radiologists and computer scientists are optimistic about the future of AI in radiology, with large majorities expecting radiologists' job satisfaction to increase or stay the same (89.5% of radiologists vs. 86.7% of CS experts, P=0.7767), radiologists' salaries to increase or stay the same (83.2% of radiologists vs. 73.4% of CS experts, P=0.1827), and the role of radiologists in society to improve or stay the same (88.4% vs. 86.7%, P=0.7857). CONCLUSIONS Thoracic radiologists and CS experts are generally positive on the impact of AI in radiology. However, a larger percentage, but still small minority, of computer scientists predict radiologist obsolescence in 10 to 20 years. As the future of AI in radiology unfolds, this study presents a historical timestamp of which group of experts' perceptions were closer to eventual reality.
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Tejani AS. Identifying and Addressing Barriers to an Artificial Intelligence Curriculum. J Am Coll Radiol 2021; 18:605-607. [DOI: 10.1016/j.jacr.2020.10.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 10/02/2020] [Indexed: 12/31/2022]
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Weisberg EM, Fishman EK. Developing a curriculum in artificial intelligence for emergency radiology. Emerg Radiol 2021; 27:359-360. [PMID: 32643068 PMCID: PMC7341461 DOI: 10.1007/s10140-020-01795-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Edmund M Weisberg
- Johns Hopkins Medicine, 601 North Caroline Street, JHOC 3262, Baltimore, MD, 21287, USA.
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An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude. Eur Radiol 2021; 31:7058-7066. [PMID: 33744991 PMCID: PMC8379099 DOI: 10.1007/s00330-021-07781-5] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Revised: 01/13/2021] [Accepted: 02/12/2021] [Indexed: 02/07/2023]
Abstract
Objectives Radiologists’ perception is likely to influence the adoption of artificial intelligence (AI) into clinical practice. We investigated knowledge and attitude towards AI by radiologists and residents in Europe and beyond. Methods Between April and July 2019, a survey on fear of replacement, knowledge, and attitude towards AI was accessible to radiologists and residents. The survey was distributed through several radiological societies, author networks, and social media. Independent predictors of fear of replacement and a positive attitude towards AI were assessed using multivariable logistic regression. Results The survey was completed by 1,041 respondents from 54 mostly European countries. Most respondents were male (n = 670, 65%), median age was 38 (24–74) years, n = 142 (35%) residents, and n = 471 (45%) worked in an academic center. Basic AI-specific knowledge was associated with fear (adjusted OR 1.56, 95% CI 1.10–2.21, p = 0.01), while intermediate AI-specific knowledge (adjusted OR 0.40, 95% CI 0.20–0.80, p = 0.01) or advanced AI-specific knowledge (adjusted OR 0.43, 95% CI 0.21–0.90, p = 0.03) was inversely associated with fear. A positive attitude towards AI was observed in 48% (n = 501) and was associated with only having heard of AI, intermediate (adjusted OR 11.65, 95% CI 4.25–31.92, p < 0.001), or advanced AI-specific knowledge (adjusted OR 17.65, 95% CI 6.16–50.54, p < 0.001). Conclusions Limited AI-specific knowledge levels among radiology residents and radiologists are associated with fear, while intermediate to advanced AI-specific knowledge levels are associated with a positive attitude towards AI. Additional training may therefore improve clinical adoption. Key Points • Forty-eight percent of radiologists and residents have an open and proactive attitude towards artificial intelligence (AI), while 38% fear of replacement by AI. • Intermediate and advanced AI-specific knowledge levels may enhance adoption of AI in clinical practice, while rudimentary knowledge levels appear to be inhibitive. • AI should be incorporated in radiology training curricula to help facilitate its clinical adoption. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-07781-5.
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Scheetz J, Rothschild P, McGuinness M, Hadoux X, Soyer HP, Janda M, Condon JJJ, Oakden-Rayner L, Palmer LJ, Keel S, van Wijngaarden P. A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology. Sci Rep 2021; 11:5193. [PMID: 33664367 PMCID: PMC7933437 DOI: 10.1038/s41598-021-84698-5] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 02/12/2021] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence technology has advanced rapidly in recent years and has the potential to improve healthcare outcomes. However, technology uptake will be largely driven by clinicians, and there is a paucity of data regarding the attitude that clinicians have to this new technology. In June-August 2019 we conducted an online survey of fellows and trainees of three specialty colleges (ophthalmology, radiology/radiation oncology, dermatology) in Australia and New Zealand on artificial intelligence. There were 632 complete responses (n = 305, 230, and 97, respectively), equating to a response rate of 20.4%, 5.1%, and 13.2% for the above colleges, respectively. The majority (n = 449, 71.0%) believed artificial intelligence would improve their field of medicine, and that medical workforce needs would be impacted by the technology within the next decade (n = 542, 85.8%). Improved disease screening and streamlining of monotonous tasks were identified as key benefits of artificial intelligence. The divestment of healthcare to technology companies and medical liability implications were the greatest concerns. Education was identified as a priority to prepare clinicians for the implementation of artificial intelligence in healthcare. This survey highlights parallels between the perceptions of different clinician groups in Australia and New Zealand about artificial intelligence in medicine. Artificial intelligence was recognized as valuable technology that will have wide-ranging impacts on healthcare.
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Affiliation(s)
- Jane Scheetz
- Level 7, Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, 32 Gisborne Street, Melbourne, VIC, 3002, Australia
| | - Philip Rothschild
- Level 7, Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, 32 Gisborne Street, Melbourne, VIC, 3002, Australia
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Myra McGuinness
- Level 7, Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, 32 Gisborne Street, Melbourne, VIC, 3002, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Xavier Hadoux
- Level 7, Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, 32 Gisborne Street, Melbourne, VIC, 3002, Australia
| | - H Peter Soyer
- Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
- Dermatology Department, Princess Alexandra Hospital, Brisbane, QLD, Australia
| | - Monika Janda
- Centre for Health Services Research, The University of Queensland, Brisbane, Australia
| | - James J J Condon
- School of Public Health, University of Adelaide, Adelaide, Australia
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia
| | - Luke Oakden-Rayner
- School of Public Health, University of Adelaide, Adelaide, Australia
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia
| | - Lyle J Palmer
- School of Public Health, University of Adelaide, Adelaide, Australia
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia
| | - Stuart Keel
- Level 7, Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, 32 Gisborne Street, Melbourne, VIC, 3002, Australia
| | - Peter van Wijngaarden
- Level 7, Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, 32 Gisborne Street, Melbourne, VIC, 3002, Australia.
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia.
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