Evidence map›Paper›PMID 36269641›Full record

ArticleJMIR medical education2022

Perceptions of US Medical Students on Artificial Intelligence in Medicine: Mixed Methods Survey Study.

David Shalom Liu, Jake Sawyer, Alexander Luna, Jihad Aoun, Janet Wang, Lord Boachie, Safwan Halabi, Bina Joe

Abstract read
In one paragraph

Article in JMIR medical education, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 3 pooled it
–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

51 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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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

8 authors.

David Shalom LiuCollege of Medicine and Life Sciences, University of Toledo, Toledo, OH, United States.ORCID https://orcid.org/0000-0002-1279-9580
Jake SawyerCollege of Medicine and Life Sciences, University of Toledo, Toledo, OH, United States.ORCID https://orcid.org/0000-0003-2921-526X
Alexander LunaCollege of Medicine and Life Sciences, University of Toledo, Toledo, OH, United States.ORCID https://orcid.org/0000-0002-8335-7915
Jihad AounCollege of Medicine and Life Sciences, University of Toledo, Toledo, OH, United States.ORCID https://orcid.org/0000-0003-2403-7791
Janet WangCollege of Medicine and Life Sciences, University of Toledo, Toledo, OH, United States.ORCID https://orcid.org/0000-0002-4411-0143
Lord BoachieCollege of Medicine and Life Sciences, University of Toledo, Toledo, OH, United States.ORCID https://orcid.org/0000-0003-4799-9352
Safwan HalabiPediatric Radiology, Ann & Robert H Lurie Children's Hospital of Chicago, Chicago, IL, United States.ORCID https://orcid.org/0000-0003-1317-984X
Bina JoeDepartment of Physiology and Pharmacology, College of Medicine and Life Sciences, University of Toledo, Toledo, OH, United States.ORCID https://orcid.org/0000-0002-2385-7061

Funding

Genetic, Epigenetic and Dietary Salt effects on Microbiota and HypertensionR01HL143082 · NHLBI · UNIVERSITY OF TOLEDO HEALTH SCI CAMPUS · PI JOE, BINA · 2018 to 2021
$2.6M
NHLBI NIH HHS R01 HL143082
6 · The paper itself

Abstract

backgroundGiven the rapidity with which artificial intelligence is gaining momentum in clinical medicine, current physician leaders have called for more incorporation of artificial intelligence topics into undergraduate medical education. This is to prepare future physicians to better work together with artificial intelligence technology. However, the first step in curriculum development is to survey the needs of end users. There has not been a study to determine which media and which topics are most preferred by US medical students to learn about the topic of artificial intelligence in medicine.

objectiveWe aimed to survey US medical students on the need to incorporate artificial intelligence in undergraduate medical education and their preferred means to do so to assist with future education initiatives.

methodsA mixed methods survey comprising both specific questions and a write-in response section was sent through Qualtrics to US medical students in May 2021. Likert scale questions were used to first assess various perceptions of artificial intelligence in medicine. Specific questions were posed regarding learning format and topics in artificial intelligence.

resultsWe surveyed 390 US medical students with an average age of 26 (SD 3) years from 17 different medical programs (the estimated response rate was 3.5%). A majority (355/388, 91.5%) of respondents agreed that training in artificial intelligence concepts during medical school would be useful for their future. While 79.4% (308/388) were excited to use artificial intelligence technologies, 91.2% (353/387) either reported that their medical schools did not offer resources or were unsure if they did so. Short lectures (264/378, 69.8%), formal electives (180/378, 47.6%), and Q and A panels (167/378, 44.2%) were identified as preferred formats, while fundamental concepts of artificial intelligence (247/379, 65.2%), when to use artificial intelligence in medicine (227/379, 59.9%), and pros and cons of using artificial intelligence (224/379, 59.1%) were the most preferred topics for enhancing their training.

conclusionsThe results of this study indicate that current US medical students recognize the importance of artificial intelligence in medicine and acknowledge that current formal education and resources to study artificial intelligence-related topics are limited in most US medical schools. Respondents also indicated that a hybrid formal/flexible format would be most appropriate for incorporating artificial intelligence as a topic in US medical schools. Based on these data, we conclude that there is a definitive knowledge gap in artificial intelligence education within current medical education in the US. Further, the results suggest there is a disparity in opinions on the specific format and topics to be introduced.

Indexed as

artificial intelligencedigital healtheducationeHealthelective courseintegrationmedical curriculummedical educationmedical schoolmedical student

Identifiers

PMID36269641
PMCPMC9636531

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.