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Introducing Artificial Intelligence Training in Medical Education

2019· article· en· 614 citations· W2981296841 on OpenAlex· 10.2196/16048

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian venueIt was published in a Canadian venue.

No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.

Machine scores (provisional)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Opus teacher head0.091
GPT teacher head0.464
Teacher spread
0.373 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

Health care is evolving and with it the need to reform medical education. As the practice of medicine enters the age of artificial intelligence (AI), the use of data to improve clinical decision making will grow, pushing the need for skillful medicine-machine interaction. As the rate of medical knowledge grows, technologies such as AI are needed to enable health care professionals to effectively use this knowledge to practice medicine. Medical professionals need to be adequately trained in this new technology, its advantages to improve cost, quality, and access to health care, and its shortfalls such as transparency and liability. AI needs to be seamlessly integrated across different aspects of the curriculum. In this paper, we have addressed the state of medical education at present and have recommended a framework on how to evolve the medical education curriculum to include AI.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

The record

Venue
JMIR Medical Education
Topic
Artificial Intelligence in Healthcare and Education
Field
Medicine
Canadian institutions
Funders
Keywords
CurriculumTransparency (behavior)Health careMedical educationLiabilityMedical knowledgeQuality (philosophy)Knowledge managementTraining (meteorology)Computer scienceArtificial intelligenceMedicinePsychologyBusinessPolitical sciencePedagogy
Has abstract in OpenAlex
yes