ReviewCureus2025
Understanding Large Language Models in Healthcare: A Guide to Clinical Implementation and Interpreting Publications.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Nurse-Led Large Language Model Chatbot for Predicting and Preventing Complications After Coronary Artery Bypass Grafting: Protocol for a Randomized Controlled Trial.JMIR research protocols · 2026Article
- Development and Clinical Evaluation of a Large Language Model-Based System for Generating Patient-Friendly Echocardiography Reports: Two-Stage Retrospective Validation and Prospective Survey Study.Journal of medical Internet research · 2026Article
- Determinants of healthcare professionals' acceptance of DeepSeek-assisted clinical decision support applications: a cross-sectional study.BMC medical informatics and decision making · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) have generated excitement and interest in their capability to impact various facets of healthcare delivery. However, the rapidly expanding literature on LLMs presents challenges in understanding recent work, associated terminology, and potential applications for healthcare professionals. In this review, we discuss the development and evolution of LLMs, especially in healthcare. We provide a description of the key terminologies associated with LLMs to improve the understanding of these terms and their application context in healthcare. Evaluation of the experiments and research related to LLMs is fundamentally important for clinicians. Thus, we provide a description of evaluation methodologies used in LLM research. Lastly, through illustrative examples of research in application of LLMs in healthcare, we showcase the opportunities to leverage this state-of-the-art artificial intelligence (AI) technique with considerations for clinical and administrative adoption both by the patients and healthcare professionals. Through this review, we hope to equip the healthcare professionals with the knowledge they need to understand LLM in healthcare research.
Indexed as
Identifiers
What OpenQuestion holds
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.