ArticleApplied clinical informatics2024
A Survey of Clinicians' Views of the Utility of Large Language Models.
Article in Applied clinical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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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.
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Who cites it
32 citing papers in PubMed.
- Ability of Large Language Models to Answer Patients' Questions and Generate Educational Materials for Uncommon Retinal Conditions.Journal of vitreoretinal diseases · 2026Article
- Vocal Cord Dysfunction: Evaluating the Utility of AI Large Language Models for Patient Education.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- Clinician Use of a General-Purpose Large Language Model in Hospital Medicine: A Mixed-Methods Pilot Study.Cureus · 2026Article
- Development and Validation of the ATRAI Questionnaire to Assess Attitudes Toward Large Language Models in Clinical Setting (ATRAI-LLM).European journal of investigation in health, psychology and education · 2026Article
- A data-driven method for research trend analysis in a scientific discipline: Application to the journal of biomedical informatics.Journal of biomedical informatics · 2026Article
- Article
- Investigating expectations and needs regarding the use of large language models at Bavarian university clinics.Scientific reports · 2026Article
- Leveraging Large Language Models to Improve the Readability of German Online Medical Texts: Evaluation Study.JMIR AI · 2026Article
- Patient perspectives on life impact and unmet needs in giant cell arteritis and polymyalgia rheumatica: insights from social media.Rheumatology advances in practice · 2026Article
- Article
- Scalable scientific interest profiling using large language models.Journal of biomedical informatics · 2025Article
- Shifts in emergency physicians' attitudes toward large language model-based documentation: a pre- and post-implementation study.Scientific reports · 2025Article
- Large language model as a clinical decision support tool in the initial management of critically ill children: a pilot evaluation.European journal of pediatrics · 2025Article
- Large language models for clinical decision support in gastroenterology and hepatology.Nature reviews. Gastroenterology & hepatology · 2025Review
- Health Consumers' Use and Perceptions of Health Information from Generative Artificial Intelligence Chatbots: A Scoping Review.Applied clinical informatics · 2025Article
- Large Language Models in Healthcare and Medical Applications: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Article
- A guide to evade hallucinations and maintain reliability when using large language models for medical research: a narrative review.Annals of pediatric endocrinology & metabolism · 2025Article
- Leveraging long context in retrieval augmented language models for medical question answering.NPJ digital medicine · 2025Article
- Large Language Model GPT-4 Compared to Endocrinologist Responses on Initial Choice of Glucose-Lowering Medication Under Conditions of Clinical Uncertainty.Diabetes care · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectivesLarge language models (LLMs) like Generative pre-trained transformer (ChatGPT) are powerful algorithms that have been shown to produce human-like text from input data. Several potential clinical applications of this technology have been proposed and evaluated by biomedical informatics experts. However, few have surveyed health care providers for their opinions about whether the technology is fit for use.
methodsWe distributed a validated mixed-methods survey to gauge practicing clinicians' comfort with LLMs for a breadth of tasks in clinical practice, research, and education, which were selected from the literature.
resultsA total of 30 clinicians fully completed the survey. Of the 23 tasks, 16 were rated positively by more than 50% of the respondents. Based on our qualitative analysis, health care providers considered LLMs to have excellent synthesis skills and efficiency. However, our respondents had concerns that LLMs could generate false information and propagate training data bias.Our survey respondents were most comfortable with scenarios that allow LLMs to function in an assistive role, like a physician extender or trainee.
conclusionIn a mixed-methods survey of clinicians about LLM use, health care providers were encouraging of having LLMs in health care for many tasks, and especially in assistive roles. There is a need for continued human-centered development of both LLMs and artificial intelligence in general.
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Registered trials
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.