Evidence map›Paper›PMID 39564056›Full record

ArticleCureus2024

A Pilot Study of Medical Student Opinions on Large Language Models.

Alan Y Xu, Vincent S Piranio, Skye Speakman, Chelsea D Rosen, Sally Lu, Chris Lamprecht, Robert E Medina, Maisha Corrielus, Ian T Griffin, Corinne E Chatham and 6 more

Abstract read
In one paragraph

Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Observational
  3. Article
  4. Article
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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

16 authors.

Alan Y XuDepartment of Medicine, University of Florida College of Medicine, Gainesville, USA.
Vincent S PiranioDepartment of Medicine, University of Florida College of Medicine, Gainesville, USA.
Skye SpeakmanDepartment of Medicine, University of Florida College of Medicine, Gainesville, USA.
Chelsea D RosenDepartment of Medicine, University of Florida College of Medicine, Gainesville, USA.
Sally LuDepartment of Medicine, University of Florida College of Medicine, Gainesville, USA.
Chris LamprechtDepartment of Medicine, University of Florida College of Medicine, Gainesville, USA.
Robert E MedinaDepartment of Medicine, University of Florida College of Medicine, Gainesville, USA.
Maisha CorrielusDepartment of Medicine, University of Florida College of Medicine, Gainesville, USA.
Ian T GriffinDepartment of Medicine, University of Florida College of Medicine, Gainesville, USA.
Corinne E ChathamDepartment of Medicine, University of Florida College of Medicine, Gainesville, USA.
Nicolas J AbcheeDepartment of Medicine, University of Florida College of Medicine, Gainesville, USA.
Daniel StriblingDepartment of Medicine, University of Florida College of Medicine, Gainesville, USA.
Phuong B HuynhDepartment of Medicine, University of Florida College of Medicine, Gainesville, USA.
Heather HarrellDepartment of Medicine, University of Florida College of Medicine, Gainesville, USA.
Benjamin ShickelDepartment of Medicine, University of Florida College of Medicine, Gainesville, USA.
Meghan BrennanDepartment of Anesthesiology, University of Florida College of Medicine, Gainesville, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction Artificial intelligence (AI) has long garnered significant interest in the medical field. Large language models (LLMs) have popularized the use of AI for the public through chatbots such as ChatGPT and have become an easily accessible and recognizable medical resource for medical students. Here, we investigate how medical students are currently utilizing LLM-based tools throughout medical education and examine medical student perception of these tools. Methods A cross-sectional survey was administered to current medical students at the University of Florida College of Medicine (UFCOM) in January 2024 discussing the utilization of AI and LLM tools and perspectives on the current and future role of AI in medicine. Results All 102 respondents reported having heard of LLM-based chatbots such as ChatGPT, Bard, Bing Chat, and Claude. Sixty-nine percent (69%; 70/102) of respondents reported having used them for medical-related purposes at least once a month. Seventy-seven point one percent (77.1%; 54/70) reported the information provided by them to be very accurate or somewhat accurate, and 80% (55/70) reported that they were likely to continue using them in their future medical practice. Those with some baseline understanding of and exposure to AI were 3.26 (p=0.020) and 4.30 (p=0.002) times more likely to have used an LLM-based chatbot, respectively, and 5.06 (p=0.021) and 3.38 (p=0.039) times more likely to cross-check information obtained from them, respectively, compared to those with little to no baseline understanding or exposure. Furthermore, those with some exposure to AI in medical school were 2.70 (p=0.039) and 4.61 (p=0.0004) times more likely to trust AI with clinical decision-making currently and in the next 5 years, respectively, than those with little to no exposure. Those who had used an LLM-based chatbot were 4.31 (p=0.019) times more likely to trust AI with clinical decision-making currently compared to those who had not used one. Conclusion LLM-based chatbots, such as ChatGPT, are not only making their way into the medical student repertoire of study resources but are also being utilized in the setting of patient care and research. Medical students who participated in the survey generally had a positive perception of LLM-based chatbots and reported they were likely to continue using them in the future. Previous AI knowledge and exposure correlated with more conscientious use of these tools such as cross-checking information. Combined with our finding that all respondents believed AI should be taught in the medical curriculum, our study highlights a key opportunity in medical education to acclimate medical students to AI now.

Indexed as

artificial intelligencechatgptlarge language modelmedical educationsurvey

Identifiers

PMID39564056
PMCPMC11575919

What OpenQuestion holds

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LicenceCC BY
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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.