Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers

Ng, Faye Yu Ci; Thirunavukarasu, Arun James; Cheng, Haoran; Tan, Ting Fang; Gutierrez, Laura; Lan, Yanyan; Ong, Jasmine Chiat Ling; Chong, Yap Seng; Ngiam, Kee Yuan; Ho, Dean; Wong, Tien Yin; Kwek, Kenneth; Doshi-Velez, Finale; Lucey, Catherine; Coffman, Thomas; Ting, Daniel Shu Wei

doi:10.1016/j.xcrm.2023.101230

Cited by 16 publications

(5 citation statements)

References 76 publications

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“…Some studies state that many students claim never to have heard of machine learning, and their medical school taught them little to nothing about AI [ 52 , 53 ]. Most medical students are willing to learn more about AI, but many face obstacles to understanding the statistical techniques employed in AI research, prospective applications, or interpreting the results of AI-related publications [ 54 ].…”

Section: Discussionmentioning

confidence: 99%

Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine

Jebreen,

Radwan,

Kammoun-Rebai

et al. 2024

BMC Med Educ

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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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Section: Discussionmentioning

confidence: 99%

Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine

Jebreen,

Radwan,

Kammoun-Rebai

et al. 2024

BMC Med Educ

View full text Add to dashboard Cite

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“…The literature emphasizes the need to introduce AI in the medical education curriculum [ 12 , 13 , 15 - 20 ]; however, there are several challenges that have been discussed in terms of implementing this type of education. This includes insufficient time, insufficient resources (eg, lack of teaching staff or knowledge), and variable aptitude and interest in AI [ 80 - 82 ]. However, this review details several approaches to implementation as well as 6 studies that have evaluated their educational program.…”

Section: Discussionmentioning

confidence: 99%

Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review

Tolentino,

Baradaran,

Gore

et al. 2024

JMIR Med Educ

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Background The successful integration of artificial intelligence (AI) into clinical practice is contingent upon physicians’ comprehension of AI principles and its applications. Therefore, it is essential for medical education curricula to incorporate AI topics and concepts, providing future physicians with the foundational knowledge and skills needed. However, there is a knowledge gap in the current understanding and availability of structured AI curriculum frameworks tailored for medical education, which serve as vital guides for instructing and facilitating the learning process. Objective The overall aim of this study is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of AI for medical students, residents, and practicing physicians. Methods We followed a validated framework and the Joanna Briggs Institute methodological guidance for scoping reviews. An information specialist performed a comprehensive search from 2000 to May 2023 in the following bibliographic databases: MEDLINE (Ovid), Embase (Ovid), CENTRAL (Cochrane Library), CINAHL (EBSCOhost), and Scopus as well as the gray literature. Papers were limited to English and French languages. This review included papers that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of papers and study designs were included, except conference abstracts and protocols. Two reviewers independently screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results. Results Of the 5104 papers screened, 21 papers relevant to our eligibility criteria were identified. In total, 90% (19/21) of the papers altogether described 30 current or previously offered educational programs, and 10% (2/21) of the papers described elements of a curriculum framework. One framework describes a general approach to integrating AI curricula throughout the medical learning continuum and another describes a core curriculum for AI in ophthalmology. No papers described a theory, pedagogy, or framework that guided the educational programs. Conclusions This review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education. To build on this foundation, future researchers are encouraged to engage in a multidisciplinary approach to curriculum redesign. In addition, it is encouraged to initiate dialogues on the integration of AI into medical curriculum planning and to investigate the development, deployment, and appraisal of these innovative educational programs. International Registered Report Identifier (IRRID) RR2-10.11124/JBIES-22-00374

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“…Academic institutions are responsible for providing education and training in AI and related fields, as well as conducting research to advance the understanding and application of AI in healthcare. [69] They can facilitate interdisciplinary collaboration by offering joint programs, courses, and research projects that bring together students and faculty from diverse disciplines including medicine, computer science, engineering, and ethics. [70,71] By promoting interdisciplinary education and research, academia can help bridge the gap between data science and clinical applications, ensuring that AI technologies are developed and implemented effectively in healthcare settings.…”

Section: Academiamentioning

confidence: 99%

The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare

Patel,

Gu,

Esper

et al. 2024

Preprint

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Interdisciplinary conferences play a vital role in advancing explainable Artificial Intelligence (AI) within increasingly intersecting healthcare and computational biology arenas. Challenges result-ing from algorithmic bias and the necessity for domain-specific Explainable AI (XAI) techniques underscore the increasing significance of transparent and interpretable AI systems used in patient care. Collaborative efforts between clinicians, data scientists, and various stakeholders are nec-essary to innovate solutions tailored to healthcare needs. Specialized conferences may foster these interdisciplinary interactions, providing platforms for knowledge exchange, networking, and collaborative partnerships. The legal, ethical, and societal dimensions of medical AI advocate for an integrative approach that aligns technological advancements with patient-centered care val-ues. The role of specialized conferences is therefore essential for shaping future directions in ex-plainable AI and computational biology that contribute to improved patient outcomes and healthcare innovations.

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Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers

Cited by 16 publications

References 76 publications

Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine

Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine

Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review

The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare

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