Evidence map›Paper›PMID 40354644›Full record

ArticleJMIR medical education2025

Global Health care Professionals' Perceptions of Large Language Model Use In Practice: Cross-Sectional Survey Study.

Ecem Ozkan, Aysun Tekin, Mahmut Can Ozkan, Daniel Cabrera, Alexander Niven, Yue Dong

Abstract read
In one paragraph

Article in JMIR medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 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

6 authors.

Ecem OzkanDepartment of Medicine, Jersey Shore University Medical Center, 1945 NJ-33, Neptune, NJ, 07753, United States, 1 5078843064.ORCID 0000-0003-4380-2364
Aysun TekinDepartment of Anesthesiology, Mayo Clinic College of Medicine, Rochester, MN, United States.ORCID 0000-0002-1891-2118
Mahmut Can OzkanDepartment of Medicine, Jersey Shore University Medical Center, 1945 NJ-33, Neptune, NJ, 07753, United States, 1 5078843064.ORCID 0000-0001-5905-3498
Daniel CabreraDepartment of Emergency Medicine, Mayo Clinic College of Medicine, Rochester, MN, United States.ORCID 0000-0002-5891-0459
Alexander NivenDepartment of Pulmonary and Critical Care Medicine, Mayo Clinic College of Medicine, Rochester, MN, United States.ORCID 0000-0003-0891-9495
Yue DongDepartment of Anesthesiology, Mayo Clinic College of Medicine, Rochester, MN, United States.ORCID 0000-0002-1737-6536

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: ChatGPT is a large language model-based chatbot developed by OpenAI. ChatGPT has many potential applications to health care, including enhanced diagnostic accuracy and efficiency, improved treatment planning, and better patient outcomes. However, health care professionals' perceptions of ChatGPT and similar artificial intelligence tools are not well known. Understanding these attitudes is important to inform the best approaches to exploring their use in medicine. Objective: Our aim was to evaluate the health care professionals' awareness and perceptions regarding potential applications of ChatGPT in the medical field, including potential benefits and challenges of adoption. Methods: We designed a 33-question online survey that was distributed among health care professionals via targeted emails and professional Twitter and LinkedIn accounts. The survey included a range of questions to define respondents' demographic characteristics, familiarity with ChatGPT, perceptions of this tool's usefulness and reliability, and opinions on its potential to improve patient care, research, and education efforts. Results: One hundred and fifteen health care professionals from 21 countries responded to the survey, including physicians, nurses, researchers, and educators. Of these, 101 (87.8%) had heard of ChatGPT, mainly from peers, social media, and news, and 77 (76.2%) had used ChatGPT at least once. Participants found ChatGPT to be helpful for writing manuscripts (n=31, 45.6%), emails (n=25, 36.8%), and grants (n=12, 17.6%); accessing the latest research and evidence-based guidelines (n=21, 30.9%); providing suggestions on diagnosis or treatment (n=15, 22.1%); and improving patient communication (n=12, 17.6%). Respondents also felt that the ability of ChatGPT to access and summarize research articles (n=22, 46.8%), provide quick answers to clinical questions (n=15, 31.9%), and generate patient education materials (n=10, 21.3%) was helpful. However, there are concerns regarding the use of ChatGPT, for example, the accuracy of responses (n=14, 29.8%), limited applicability in specific practices (n=18, 38.3%), and legal and ethical considerations (n=6, 12.8%), mainly related to plagiarism or copyright violations. Participants stated that safety protocols such as data encryption (n=63, 62.4%) and access control (n=52, 51.5%) could assist in ensuring patient privacy and data security. Conclusions: Our findings show that ChatGPT use is widespread among health care professionals in daily clinical, research, and educational activities. The majority of our participants found ChatGPT to be useful; however, there are concerns about patient privacy, data security, and its legal and ethical issues as well as the accuracy of its information. Further studies are required to understand the impact of ChatGPT and other large language models on clinical, educational, and research outcomes, and the concerns regarding its use must be addressed systematically and through appropriate methods.

Indexed as

Attitude of Health PersonnelGlobal HealthHealth PersonnelAdultCross-Sectional StudiesFemaleHumansLarge Language ModelsMalePerceptionSurveys and QuestionnairesAIartificial intelligencechatbotChatGPTclinicaldiagnostic accuracyeducationaleducatorsefficiencyglobalhealth care professionalslanguage modellarge language modelLLMnursespatient carepatient communicationpatient outcomephysicianssurveytreatment planningutilization

Identifiers

PMID40354644
PMCPMC12088617

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.