Evidence map›Paper›PMID 36543111›Full record

ArticleCell reports. Medicine2022

Teaching artificial intelligence as a fundamental toolset of medicine.

Erkin Ötleş, Cornelius A James, Kimberly D Lomis, James O Woolliscroft

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports. Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Erkin ÖtleşMedical Scientist Training Program, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA. Electronic address: eotles@umich.edu.
Cornelius A JamesDepartment of Pediatrics, University of Michigan, Ann Arbor, MI, USA; Departments of Internal Medicine and Learning Health Sciences, University of Michigan, Ann Arbor, MI, USA.
Kimberly D LomisAmerican Medical Association, Chicago, IL, USA.
James O WoolliscroftDepartments of Internal Medicine and Learning Health Sciences, University of Michigan, Ann Arbor, MI, USA.

Funding

MICHIGAN MEDICAL SCIENTIST TRAINING PROGRAMT32GM007863 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI COLLINS, KATHLEEN L. · 1985 to 2024
$38.3M
NIGMS NIH HHS T32 GM007863
6 · The paper itself

Abstract

Artificial intelligence (AI) is transforming the practice of medicine. Systems assessing chest radiographs, pathology slides, and early warning systems embedded in electronic health records (EHRs) are becoming ubiquitous in medical practice. Despite this, medical students have minimal exposure to the concepts necessary to utilize and evaluate AI systems, leaving them under prepared for future clinical practice. We must work quickly to bolster undergraduate medical education around AI to remedy this. In this commentary, we propose that medical educators treat AI as a critical component of medical practice that is introduced early and integrated with the other core components of medical school curricula. Equipping graduating medical students with this knowledge will ensure they have the skills to solve challenges arising at the confluence of AI and medicine.

Indexed as

MedicineStudents, MedicalArtificial IntelligenceCurriculumElectronic Health RecordsHumans

Identifiers

PMID36543111
PMCPMC9797941

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.