Literature DB >> 31658842

Humans and machines: Moving towards a more symbiotic approach to learning clinical reasoning.

Ralph Pinnock1, Jenny McDonald2, Darren Ritchie3, Steven J Durning4,5.   

Abstract

Artificial intelligence is a growing phenomenon that is driving major changes to how we deliver healthcare. One of its most significant and challenging contributions is likely to be in diagnosis. Artificial intelligence is challenging the physician's exclusive role in diagnosis and in some areas, its diagnostic accuracy exceeds that of humans. We argue that we urgently need to consider how we will incorporate AI into our teaching of clinical reasoning in the undergraduate curriculum; students need to successfully navigate the benefits and potential issues of new and developing approaches to AI in clinical diagnosis. We offer a pedagogical framework for this challenging change to our curriculum.

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Year:  2019        PMID: 31658842     DOI: 10.1080/0142159X.2019.1679361

Source DB:  PubMed          Journal:  Med Teach        ISSN: 0142-159X            Impact factor:   3.650


  1 in total

1.  Development and Validation of a Machine Learning Model for Automated Assessment of Resident Clinical Reasoning Documentation.

Authors:  Verity Schaye; Benedict Guzman; Jesse Burk-Rafel; Marina Marin; Ilan Reinstein; David Kudlowitz; Louis Miller; Jonathan Chun; Yindalon Aphinyanaphongs
Journal:  J Gen Intern Med       Date:  2022-06-16       Impact factor: 6.473

  1 in total

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