Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes

Grunhut, Joel; Wyatt, Adam T M; Marques, Oge

doi:10.1177/23821205211036836

Cited by 80 publications

(94 citation statements)

References 88 publications

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“…In 2018, the American Medical Association called for “Research regarding the effectiveness of AI instruction in medical education on learning and clinical outcomes” [ 21 ]. Three years later, the available literature suggests UME has been slow to address this call [ 17 ]. Thus, further efforts should be made to advance this original call into practice.…”

Section: A Methodological Approachmentioning

confidence: 99%

“…Currently, the state of AI in medical education is in its infancy and speculative stages [ 13 - 16 ]. Previously, we have shown that the majority of published literature on the topic call for change in undergraduate medical education and that research is necessary to support curricular changes [ 17 ]. Even so, few have given thought to the steps that must be taken to create this change.…”

Section: Introductionmentioning

confidence: 99%

“…On an individual student level, students can help drive change in their education with a proactive engagement with AI. Previously, we have shown that most of the current literature agrees that medical students do not need to learn how to code and create AI tools, but they should understand how AI works and its limitations [ 17 ]. Students can read about AI advancements in health care in medical journals.…”

Section: Introductionmentioning

confidence: 99%

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Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum

Grunhut¹,

Marques²,

Wyatt³

2022

JMIR Med Educ

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Artificial intelligence (AI) is on course to become a mainstay in the patient’s room, physician’s office, and the surgical suite. Current advancements in health care technology might put future physicians in an insufficiently equipped position to deal with the advancements and challenges brought about by AI and machine learning solutions. Physicians will be tasked regularly with clinical decision-making with the assistance of AI-driven predictions. Present-day physicians are not trained to incorporate the suggestions of such predictions on a regular basis nor are they knowledgeable in an ethical approach to incorporating AI in their practice and evolving standards of care. Medical schools do not currently incorporate AI in their curriculum due to several factors, including the lack of faculty expertise, the lack of evidence to support the growing desire by students to learn about AI, or the lack of Liaison Committee on Medical Education’s guidance on AI in medical education. Medical schools should incorporate AI in the curriculum as a longitudinal thread in current subjects. Current students should understand the breadth of AI tools, the framework of engineering and designing AI solutions to clinical issues, and the role of data in the development of AI innovations. Study cases in the curriculum should include an AI recommendation that may present critical decision-making challenges. Finally, the ethical implications of AI in medicine must be at the forefront of any comprehensive medical education.

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Section: A Methodological Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum

Grunhut¹,

Marques²,

Wyatt³

2022

JMIR Med Educ

Self Cite

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“…Based on these considerations and evidence, we hypothesize that training practitioners can positively affect their attitudes towards adopting AI‐powered technology. However, what exactly practitioners should know about AI to understand the nature of AI, to be able to explain and justify the origin of AI decisions, and to use AI's results in their practice effectively is still an active research area (Grunhut et al, 2021; McCoy et al, 2020). We aim to contribute to this literature with a professional development program (PDP) designed to improve K‐12 teachers' knowledge of AI and trust in AI‐EdTech.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Teachers' trust in AI‐powered educational technology and a professional development program to improve it

Nazaretsky

Ariely

Cukurova

et al. 2022

Brit J Educational Tech

149

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Evidence from various domains underlines the critical role that human factors, and especially trust, play in adopting technology by practitioners. In the case of Artificial Intelligence (AI) powered tools, the issue is even more complex due to practitioners' AI‐specific misconceptions, myths and fears (e.g., mass unemployment and privacy violations). In recent years, AI has been incorporated increasingly into K‐12 education. However, little research has been conducted on the trust and attitudes of K‐12 teachers towards the use and adoption of AI‐powered Educational Technology (AI‐EdTech). This paper sheds light on teachers' trust in AI‐EdTech and presents effective professional development strategies to increase teachers' trust and willingness to apply AI‐EdTech in their classrooms. Our experiments with K‐12 science teachers were conducted around their interactions with a specific AI‐powered assessment tool (termed AI‐Grader) using both synthetic and real data. The results indicate that presenting teachers with some explanations of (i) how AI makes decisions, particularly compared to the human experts, and (ii) how AI can complement and give additional strengths to teachers, rather than replacing them, can reduce teachers' concerns and improve their trust in AI‐EdTech. The contribution of this research is threefold. First, it emphasizes the importance of increasing teachers' theoretical and practical knowledge about AI in educational settings to gain their trust in AI‐EdTech in K‐12 education. Second, it presents a teacher professional development program (PDP), as well as the discourse analysis of teachers who completed it. Third, based on the results observed, it presents clear suggestions for future PDPs aiming to improve teachers' trust in AI‐EdTech. What is already known about this topic Human factors, and especially trust, play a critical role in practitioners' adoption of technology. In recent years, AI has been incorporated increasingly into K‐12 education. Little research has been conducted on the trust and attitudes of K‐12 teachers towards the use and adoption of AI‐powered Educational Technology. What this paper adds This research emphasizes the importance of increasing teachers' theoretical and practical knowledge about AI in educational settings to gain their trust in AI‐EdTech in K‐12 education. It presents a teacher professional development program (PDP) to increase teachers' trust in AI‐EdTech, as well as the discourse analysis of teachers who completed it. It presents clear suggestions for future PDPs aiming at improving teachers' trust in AI‐EdTech. Implications for practice and/or policy Pre‐ and in‐service teacher education programs that aim to increase teachers' trust in AI‐EdTech should include a section providing teachers with a basic understanding of AI. PDPs aimed to increase teachers' trust in AI‐EdTech should focus on concrete pedagogical tasks and specific AI‐powered tools that are considered by teachers as helpful and worth the effort to learn. AI‐EdTech should not restr...

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“…The most recent versions of the Royal Colleges curricula in haematology in the UK or Ireland, for example, do not mention AI in otherwise very comprehensive documents. 4 Although examples of the successful implementation of novel AI-related modules in undergraduate curricula in North America have been described, 5,6 we are not aware of any longitudinal courses in medical AI that span the undergraduate-postgraduate interface in medical specialties, including haematology. We believe that trainees in haematology should have a working knowledge of AI in order to embrace it as a tool for optimising clinical care.…”

Section: W I D E R P E R S P E C T I V E Smentioning

confidence: 99%

Integrating artificial intelligence into haematology training and practice: Opportunities, threats and proposed solutions

2022

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There remains a limited emphasis on the use beyond the research domain of artificial intelligence (AI) in haematology and it does not feature significantly in postgraduate medical education and training. This perspective article considers recent developments in the field of AI research in haematology and anticipates the potential benefits and risks associated with its deeper integration into the specialty. Anxiety towards the greater use of AI in healthcare stems from legitimate concerns surrounding data protection, lack of transparency in clinical decision-making, and erosion of the doctor-patient relationship. The specialty of haematology has successfully embraced multiple disruptive innovations. We are at the cusp of a new era of closer integration of AI into routine haematology practice that will ultimately benefit patient care but to harness its benefits the next generation of haematologists will need access to bespoke learning opportunities with input from data scientists.

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Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes

Cited by 80 publications

References 88 publications

Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum

Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum

Teachers' trust in AI‐powered educational technology and a professional development program to improve it

Integrating artificial intelligence into haematology training and practice: Opportunities, threats and proposed solutions

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