Evidence map›Paper›PMID 40385858›Full record

ReviewCureus2025

Understanding Large Language Models in Healthcare: A Guide to Clinical Implementation and Interpreting Publications.

Julia Maslinski, Rachel Grasfield, Raghav Awasthi, Shreya Mishra, Dwarikanath Mahapatra, Piyush Mathur

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

6 authors.

Julia MaslinskiArtificial Intelligence, BrainXAI ReSearch, BrainX LLC, Cleveland, USA.
Rachel GrasfieldMedicine and Health Sciences, University of Iowa, Iowa City, USA.
Raghav AwasthiPopulation and Quantitative Health Sciences, Case Western Reserve University, Cleveland, USA.
Shreya MishraData Science, Lerner Research Institute, Cleveland Clinic, Cleveland, USA.
Dwarikanath MahapatraArtificial Intelligence, BrainXAI ReSearch, BrainX LLC, Cleveland, USA.
Piyush MathurAnesthesiology and Perioperative Medicine, Cleveland Clinic, Cleveland, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

artificial intelligence in medicinedeep learning artificial intelligencehealth professional's educationlarge language models (llms)large language models (llms) in medicineresearch

Identifiers

PMID40385858
PMCPMC12084119

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.