Evidence map›Paper›PMID 40672122›Full record

ArticleJournal of medical education and curricular development

Medical Students' Perceptions of Large Language Models in Healthcare: A Multinational Cross-Sectional Study.

Faiza Ejas, Sameer Asim Khan, Amina Mujahid, Fatma AlJoker, Hans Mautong, Geovanny Alvarado-Villa, Abhishek Kashyap, Muhammad Umer Yasir, Kindie Woubshet Nigatu, Nethra Jain and 13 more

Abstract read
In one paragraph

Article in Journal of medical education and curricular development. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. From Novelty to Necessity: Artificial Intelligence in Medical Education.Journal of medical education and curricular development
    Article
  5. Article
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

23 authors.

Faiza EjasCollege of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates.
Sameer Asim KhanCollege of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates.ORCID https://orcid.org/0009-0005-7173-8672
Amina MujahidCollege of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates.
Fatma AlJokerCollege of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates.
Hans MautongGlobal Remote Research Scholars Program, Princeton Junction, NJ, USA.
Geovanny Alvarado-VillaUniversidad de Especialidades Espiritu Santo, Samborondon, Ecuador.
Abhishek KashyapGlobal Remote Research Scholars Program, Princeton Junction, NJ, USA.
Muhammad Umer YasirShifa College of Medicine (SCM), Islamabad, Pakistan.
Kindie Woubshet NigatuHawassa University School of Medicine & Health Science, Hawassa, Sidama, Ethiopia.
Nethra JainGrant Medical College and Sir JJ Group of Hospitals, Mumbai, India.
Nandhini IyerGrant Medical College and Sir JJ Group of Hospitals, Mumbai, India.
Aman SandhuDrexel University College of Medicine, Philadelphia, PA, USA.
Shahab SharafatCollege of Medicine, University of Sharjah, Sharjah, United Arab Emirates.
Sara YahyaCollege of Medicine, University of Sharjah, Sharjah, United Arab Emirates.ORCID https://orcid.org/0009-0004-1241-6895
Mohamd Mahmoud GhalyCollege of Medicine, University of Sharjah, Sharjah, United Arab Emirates.ORCID https://orcid.org/0009-0001-7032-4305
Ibrahim IbrarCollege of Medicine, Gulf Medical University, Ajman, United Arab Emirates.
Aakanksha SinghGlobal Remote Research Scholars Program, Princeton Junction, NJ, USA.ORCID https://orcid.org/0000-0002-0700-9441
Harpeet GrewalGlobal Remote Research Scholars Program, Princeton Junction, NJ, USA.ORCID https://orcid.org/0009-0004-4811-0337
Ivan Alfredo HuespeHospital Italiano de Buenos Aires, Buenos Aires, Argentina.
Priyal MehtaGlobal Remote Research Scholars Program, Princeton Junction, NJ, USA.
Zara ArshadGlobal Remote Research Scholars Program, Princeton Junction, NJ, USA.ORCID https://orcid.org/0000-0002-5888-6663
Rahul KashyapGlobal Remote Research Scholars Program, Princeton Junction, NJ, USA.
Faisal A NawazGlobal Remote Research Scholars Program, Princeton Junction, NJ, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) and large language models (LLMs), are potential tools for enhancing healthcare delivery and clinical research. Presently, there is a scarcity of research regarding the viewpoints of medical students toward LLMs. This study aims to explore the perceptions of LLMs and their applications among this demographic. Methods: A cross-sectional study was done using an online survey. It was designed using Google Forms and circulated from July 2023 to August 2023. The target population included medical students from Ecuador, Ethiopia, India, Mauritius, Pakistan, United Arab Emirates (UAE), and the United States (USA). Results: A total of 1180 responses were collected from 10 medical colleges across 7 countries. The UAE had the largest number of responses (31.7%), followed by Ecuador (17.6%), and Mauritius (13.7%). And 77.4% respondents were already aware about LLMs before attempting the survey, most popularly, ChatGPT. Participants were most familiar with the specific use of LLMs in research (46%), and the lowest familiarity was seen in the use of LLMs in healthcare (37%). More than half of the participants (52%) support the use of LLMs healthcare, yet a significant number of them remained neutral (37%) or disagreed (31%) on LLMs being safe in this context. Conclusion: While most of the medical students are aware of LLMs and their applications, the multinational survey demonstrated hesitancy among medical students in fully adopting LLMs into healthcare practice and clinical research. This study prompts further exploration of medical students' attitudes and acceptance concerning the integration of LLMs for research methodologies, ethical considerations, and healthcare practice.

Indexed as

artificial intelligencehealthcarelarge language modelsmedical educationmedical students

Identifiers

PMID40672122
PMCPMC12266358

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.