When I say … Self

Abbiati, Milena; Pelaccia, Thierry

doi:10.1111/medu.14683

Medical Education

2021

DOI: 10.1111/medu.14683

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When I say … Self

Milena Abbiati

Thierry Pelaccia

Abstract: Reflecting on the "self" component of "the inevitable triad" around which this issue is built, the authors offer definitional considerations to consider the term's practical implications.

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2024

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From Classroom to Clinic

Nawaz,

Opriessnig,

Usman

et al. 2024

Advances in Medical Technologies and Clinical Practice

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The medical profession is at a crossroads with technology and innovation disrupting traditional care delivery, posing challenges for training future and current workforces. Traditional memorization-based medical education may become impractical due to information overload, exemplified by the rapid growth of research. AI's ability to democratize knowledge and reshape clinical workflows will be crucial in this context. AI-powered platforms have improved medical students' assessment scores by providing real-time feedback, personalized study resources, and virtual patient simulations. These tools enhance clinical reasoning, diagnostic skills, and practical knowledge, while automated assessments offer timely, objective evaluations. Despite these benefits, a lack of understanding of AI among learners and educators, and disparities in access to AI tools, hinder its full potential. This chapter explores AI's transformative impact on medical education, addressing its applications, challenges, and integration.

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From Classroom to Clinic

Nawaz,

Opriessnig,

Usman

et al. 2024

Advances in Medical Technologies and Clinical Practice

View full text Add to dashboard Cite

show abstract

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When I say … Self

Abstract: Reflecting on the "self" component of "the inevitable triad" around which this issue is built, the authors offer definitional considerations to consider the term's practical implications.

Cited by 1 publication

References 12 publications

From Classroom to Clinic

From Classroom to Clinic

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