Artificial intelligence in the medical profession: ready or not, here AI comes

Pucchio, Aidan; Papa, Joshua Del; Moraes, Fábio Ynoe de

doi:10.1016/j.clinsp.2022.100010

Cited by 4 publications

(6 citation statements)

References 6 publications

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“…While fears about AI may vary given study cohorts, anxiety regarding the use of AI in a clinical context could be ameliorated by curriculum. Finally, as AI applications progress towards clinical implementation, a lack of understanding could present challenges in effective uptake by physicians [ 19 , 7 ]. Teaching could address this, in addition to other changing requirements such as the ethical and humanistic role of physicians [ 3 ].…”

Section: Discussionmentioning

confidence: 99%

Exploration of exposure to artificial intelligence in undergraduate medical education: a Canadian cross-sectional mixed-methods study

et al. 2022

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Background Emerging artificial intelligence (AI) technologies have diverse applications in medicine. As AI tools advance towards clinical implementation, skills in how to use and interpret AI in a healthcare setting could become integral for physicians. This study examines undergraduate medical students’ perceptions of AI, educational opportunities about of AI in medicine, and the desired medium for AI curriculum delivery. Methods A 32 question survey for undergraduate medical students was distributed from May–October 2021 to students to all 17 Canadian medical schools. The survey assessed the currently available learning opportunities about AI, the perceived need for learning opportunities about AI, and barriers to educating about AI in medicine. Interviews were conducted with participants to provide narrative context to survey responses. Likert scale survey questions were scored from 1 (disagree) to 5 (agree). Interview transcripts were analyzed using qualitative thematic analysis. Results We received 486 responses from 17 of 17 medical schools (roughly 5% of Canadian undergraduate medical students). The mean age of respondents was 25.34, with 45% being in their first year of medical school, 27% in their 2nd year, 15% in their 3rd year, and 10% in their 4th year. Respondents agreed that AI applications in medicine would become common in the future (94% agree) and would improve medicine (84% agree Further, respondents agreed that they would need to use and understand AI during their medical careers (73% agree; 68% agree), and that AI should be formally taught in medical education (67% agree). In contrast, a significant number of participants indicated that they did not have any formal educational opportunities about AI (85% disagree) and that AI-related learning opportunities were inadequate (74% disagree). Interviews with 18 students were conducted. Emerging themes from the interviews were a lack of formal education opportunities and non-AI content taking priority in the curriculum. Conclusion A lack of educational opportunities about AI in medicine were identified across Canada in the participating students. As AI tools are currently progressing towards clinical implementation and there is currently a lack of educational opportunities about AI in medicine, AI should be considered for inclusion in formal medical curriculum.

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Section: Discussionmentioning

confidence: 99%

Exploration of exposure to artificial intelligence in undergraduate medical education: a Canadian cross-sectional mixed-methods study

et al. 2022

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“…Difficulties in appropriate implementation or poor technical understanding could prevent appropriate use. 69 While many of these technologies remain investigational, it is important to establish their role and accuracy in a clinical context before more widespread use by clinicians. A specific AI tool could be deployed in different settings, each with different utility, for example, in a screening setting it may be less viable to access ocular biofluids, yet a lower algorithm accuracy could be acceptable, while in a specialist ophthalmology setting one might have better access to biofluids but require higher algorithm accuracy or merely use AI tools to augment the existing diagnostic process.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Analysis of Biofluid Markers in Age-Related Macular Degeneration: A Systematic Review

Pucchio¹,

Krance²,

Pur³

et al. 2022

OPTH

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This systematic review explores the use of artificial intelligence (AI) in the analysis of biofluid markers in age-related macular degeneration (AMD). We detail the accuracy and validity of AI in diagnostic and prognostic models and biofluid markers that provide insight into AMD pathogenesis and progression. This review was conducted in accordance with the Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines. A comprehensive search was conducted across 5 electronic databases including Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, EMBASE, Medline, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics in AMD were included. Identified studies were assessed for risk of bias and critically appraised using the Joanna Briggs Institute Critical Appraisal tools. A total of 10,264 articles were retrieved from all databases and 37 studies met the inclusion criteria, including 15 cross-sectional studies, 15 prospective cohort studies, five retrospective cohort studies, one randomized controlled trial, and one case–control study. The majority of studies had a general focus on AMD (58%), while neovascular AMD (nAMD) was the focus in 11 studies (30%), and geographic atrophy (GA) was highlighted by three studies. Fifteen studies examined disease characteristics, 15 studied risk factors, and seven guided treatment decisions. Altered lipid metabolism (HDL-cholesterol, total serum triglycerides), inflammation (c-reactive protein), oxidative stress, and protein digestion were implicated in AMD development and progression. AI tools were able to both accurately differentiate controls and AMD patients with accuracies as high as 87% and predict responsiveness to anti-VEGF therapy in nAMD patients. Use of AI models such as discriminant analysis could inform prognostic and diagnostic decision-making in a clinical setting. The identified pathways provide opportunity for future studies of AMD development and could be valuable in the advancement of novel treatments.

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“…8 Currently, there are no accreditation requirements related to AI. 9 The knowledge gap between engineers, clinicians, and scientists continues to grow as health care moves to a more digital environment. Consequently, young physicians are ill-prepared to work with AI.…”

Section: Introductionmentioning

confidence: 99%

“…At present, AI is beginning to enter the field of medical education by providing learning support, assessing students’ learning, and reviewing curricula; however, several studies have urged institutes and clinical educators to begin integrating AI educational concepts into their medical curricula 6,7,9–14 . Efforts have been made to include AI education globally into each level of medical training.…”

Section: Introductionmentioning

confidence: 99%

“…A recent survey by Stanford Medicine found that 44% (230/523) of physicians and 23% (48/210) of medical students and residents reported that their education had not been helpful in preparing them for new technologies in health care 8 . Currently, there are no accreditation requirements related to AI 9 . The knowledge gap between engineers, clinicians, and scientists continues to grow as health care moves to a more digital environment.…”

Section: Introductionmentioning

confidence: 99%

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Curriculum frameworks and educational programs in artificial intelligence for medical students, residents, and practicing physicians: a scoping review protocol

Tolentino

Baradaran

Gore

et al. 2023

JBI Evidence Synthesis

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Objective: The aim of this scoping review is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of artificial intelligence (AI) for medical students, residents, and practicing physicians. Introduction: To advance the implementation of AI in clinical practice, physicians need to have a better understanding of AI and how to use it within clinical practice. Consequently, medical education must introduce AI topics and concepts into the curriculum. Curriculum frameworks are educational road maps to teaching and learning. Therefore, any existing AI curriculum frameworks must be reviewed and, if none exist, such a framework must be developed. Inclusion criteria: This review will include articles that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of articles and study designs will be included, except conference abstracts and protocols. Methods: This review will follow the JBI methodology for scoping reviews. Keywords will first be identified from relevant articles. Another search will then be conducted using the identified keywords and index terms. The following databases will be searched: MEDLINE (Ovid), Embase (Ovid), Cochrane Central Register of Controlled Trials (CENTRAL), CINAHL (EBSCOhost), and Scopus. Gray literature will also be searched. Articles will be limited to the English and French languages, commencing from the year 2000. The reference lists of all included articles will be screened for additional articles. Data will then be extracted from included articles and the results will be presented in a table.

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Artificial intelligence in the medical profession: ready or not, here AI comes

Cited by 4 publications

References 6 publications

Exploration of exposure to artificial intelligence in undergraduate medical education: a Canadian cross-sectional mixed-methods study

Exploration of exposure to artificial intelligence in undergraduate medical education: a Canadian cross-sectional mixed-methods study

Artificial Intelligence Analysis of Biofluid Markers in Age-Related Macular Degeneration: A Systematic Review

Curriculum frameworks and educational programs in artificial intelligence for medical students, residents, and practicing physicians: a scoping review protocol

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