Chatbots for future docs: exploring medical students’ attitudes and knowledge towards artificial intelligence and medical chatbots

Moldt, Julia-Astrid; Loda, Teresa; Mamlouk, Amir Madany; Nieselt, Kay; Fuhl, Wolfgang; Herrmann–Werner, Anne

doi:10.1080/10872981.2023.2182659

Cited by 84 publications

(46 citation statements)

References 38 publications

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“…The findings of this study indicate that medical students are very interested in AI in medicine and want to learn more about it in medical school 11 . The interest of medical students in this topic is not surprising given the rapid advances in AI and its potential to revolutionize healthcare.…”

Section: Discussionmentioning

confidence: 76%

Medical Students’ Attitudes toward AI in Medicine and their Expectations for Medical Education

Kimmerle¹,

Timm²,

Loda³

et al. 2023

Preprint

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Artificial intelligence (AI) is used in a variety of contexts in medicine. This involves the use of algorithms and software that analyze digital information to make diagnoses and suggest adapted therapies. It is unclear, however, what medical students know about AI in medicine, how they evaluate its application, and what they expect from their medical training accordingly. In the study presented here, we asked medical students about their assessment of AI in medicine and recorded their ideas and suggestions for considering this topic in medical education. Fifty-eight medical students took part in the survey. Almost all participants were aware of the use of AI in medicine and had an adequate understanding of it. They perceived AI in medicine to be reliable, trustworthy, and technically competent, but not particularly credible. They considered AI in medicine to be rather intelligent but not anthropomorphic. Participants were interested in the opportunities of AI in the medical context and wanted to learn more about it. They indicated that basic AI knowledge should be taught in medical studies, in particular, knowledge about modes of operation, ethics, areas of application, reliability, and possible risks. We discuss the implications of these findings for the curricular development in medical education. Medical students need to be equipped with the knowledge and skills to use AI effectively and ethically in their future practice. This includes understanding the limitations and potential biases of AI algorithms by teaching the sensible use of human oversight and continuous monitoring to catch errors in AI algorithms and ensure that final decisions are made by human clinicians.

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Section: Discussionmentioning

confidence: 76%

Medical Students’ Attitudes toward AI in Medicine and their Expectations for Medical Education

Kimmerle¹,

Timm²,

Loda³

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The findings of this study indicate that medical students are very interested in AI in medicine and want to learn more about it in medical school. 11 The interest of medical students in this topic is not surprising given the rapid advances in AI and its potential to revolutionize healthcare. Given the potential of AI in medicine, it appears to be important for medical schools to incorporate AI education into their curricula.…”

Section: Discussionmentioning

confidence: 99%

Medical Students’ Attitudes Toward AI in Medicine and their Expectations for Medical Education

Kimmerle,

Timm,

Festl-Wietek

et al. 2023

Journal of Medical Education and Curricular Development

Self Cite

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Objectives Artificial intelligence (AI) is used in a variety of contexts in medicine. This involves the use of algorithms and software that analyze digital information to make diagnoses and suggest adapted therapies. It is unclear, however, what medical students know about AI in medicine, how they evaluate its application, and what they expect from their medical training accordingly. In the study presented here, we aimed at providing answers to these questions. Methods In this survey study, we asked medical students about their assessment of AI in medicine and recorded their ideas and suggestions for considering this topic in medical education. Fifty-eight medical students completed the survey. Results Almost all participants were aware of the use of AI in medicine and had an adequate understanding of it. They perceived AI in medicine to be reliable, trustworthy, and technically competent, but did not have much faith in it. They considered AI in medicine to be rather intelligent but not anthropomorphic. Participants were interested in the opportunities of AI in the medical context and wanted to learn more about it. They indicated that basic AI knowledge should be taught in medical studies, in particular, knowledge about modes of operation, ethics, areas of application, reliability, and possible risks. Conclusions We discuss the implications of these findings for the curricular development in medical education. Medical students need to be equipped with the knowledge and skills to use AI effectively and ethically in their future practice. This includes understanding the limitations and potential biases of AI algorithms by teaching the sensible use of human oversight and continuous monitoring to catch errors in AI algorithms and ensure that final decisions are made by human clinicians.

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“…Currently, physicians remain alone during the transformation process. Training physicians to properly support this transformation process in the patient-physician relationship is the first effective step in the direction of self-determined health care for patients [ 59 ].…”

Section: Discussionmentioning

confidence: 99%

Impacts of Symptom Checkers for Laypersons’ Self-diagnosis on Physicians in Primary Care: Scoping Review

Radionova¹,

Ög²,

Wetzel³

et al. 2023

J Med Internet Res

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Background Symptom checkers (SCs) for laypersons’ self-assessment and preliminary self-diagnosis are widely used by the public. Little is known about the impact of these tools on health care professionals (HCPs) in primary care and their work. This is relevant to understanding how technological changes might affect the working world and how this is linked to work-related psychosocial demands and resources for HCPs. Objective This scoping review aimed to systematically explore the existing publications on the impacts of SCs on HCPs in primary care and to identify knowledge gaps. Methods We used the Arksey and O’Malley framework. We based our search string on the participant, concept, and context scheme and searched PubMed (MEDLINE) and CINAHL in January and June 2021. We performed a reference search in August 2021 and a manual search in November 2021. We included publications of peer-reviewed journals that focused on artificial intelligence- or algorithm-based self-diagnosing apps and tools for laypersons and had primary care or nonclinical settings as a relevant context. The characteristics of these studies were described numerically. We used thematic analysis to identify core themes. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist to report the study. Results Of the 2729 publications identified through initial and follow-up database searches, 43 full texts were screened for eligibility, of which 9 were included. Further 8 publications were included through manual search. Two publications were excluded after receiving feedback in the peer-review process. Fifteen publications were included in the final sample, which comprised 5 (33%) commentaries or nonresearch publications, 3 (20%) literature reviews, and 7 (47%) research publications. The earliest publications stemmed from 2015. We identified 5 themes. The theme finding prediagnosis comprised the comparison between SCs and physicians. We identified the performance of the diagnosis and the relevance of human factors as topics. In the theme layperson-technology relationship, we identified potentials for laypersons’ empowerment and harm through SCs. Our analysis showed potential disruptions of the physician-patient relationship and uncontested roles of HCPs in the theme (impacts on) physician-patient relationship. In the theme impacts on HCPs’ tasks, we described the reduction or increase in HCPs’ workload. We identified potential transformations of HCPs’ work and impacts on the health care system in the theme future role of SCs in health care. Conclusions The scoping review approach was suitable for this new field of research. The heterogeneity of technologies and wordings was challenging. We identified research gaps in the literature regarding the impact of artificial intelligence– or algorithm-based self-diagnosing apps or tools on the work of HCPs in primary care. Further empirical studies on HCPs’ lived experiences are needed, as the current literature depicts expectations rather than empirical findings.

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Chatbots for future docs: exploring medical students’ attitudes and knowledge towards artificial intelligence and medical chatbots

Cited by 84 publications

References 38 publications

Medical Students’ Attitudes toward AI in Medicine and their Expectations for Medical Education

Medical Students’ Attitudes toward AI in Medicine and their Expectations for Medical Education

Medical Students’ Attitudes Toward AI in Medicine and their Expectations for Medical Education

Impacts of Symptom Checkers for Laypersons’ Self-diagnosis on Physicians in Primary Care: Scoping Review

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