ArticleBMC medical informatics and decision making2024
Analyzing evaluation methods for large language models in the medical field: a scoping review.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled 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.
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
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large language models in real-world clinical workflows: a systematic review of applications and implementation.Frontiers in digital health · 2025Pooled it
- Outcomes, Evaluation Metrics, and Measurement Tools for Large Language Model Applications Research in Nursing: A Scoping Review.International nursing review · 2026Article
- The Reliability of Human Evaluation of Large Language Models in Health Care Settings: Scoping Review.Journal of medical Internet research · 2026Article
- Methods of Evaluating Large Language Model-Based Health Care Applications Used by Nonprofessionals: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- Statistical Analysis of the Performance of Open-Access Language Models: A Tool in the Clinical Laboratory.EJIFCC · 2026Article
- Consistency in causal reasoning for large language models in scenarios of HIV antiretroviral treatment, drug interactions, and side effects.NPJ digital medicine · 2026Article
- Accuracy and Knowledge Base Evaluation of ChatGPT-4o, Gemini-2.0-Flash, and DeepSeek-V3 in Metabolic and Bariatric Surgery: an Expert-Rated Blinded Study.Obesity surgery · 2026Article
- Performance of large language models in endodontics: accuracy, consistency, and benchmarking with consensus guidelines.BMC oral health · 2026Article
- Structured taxonomy and framework for developing medical benchmark in large language models derived from scoping review.NPJ digital medicine · 2026Article
- A multicenter multifunctional assessment of large language models in pure-tone audiogram interpretation for patients.NPJ digital medicine · 2026Article
- Diagnostic Accuracy and Stability of Multimodal Large Language Models for Hand Fracture Detection: A Multi-Run Evaluation on Plain Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Plastic Surgery Education: A Global Multimodel Benchmark of Large Language Models on the Plastic Surgery In-Service Training Examination.Aesthetic surgery journal. Open forum · 2026Article
- Blinded two-phase evaluation of large language models in complex cardiac surgery: task-specific performance and human-AI collaboration.Frontiers in digital health · 2026Article
- Evaluating the potential of ChatGPT as an educational decision-support tool for hemodialysis decision-making in nephrology training.Frontiers in medicine · 2026Article
- Benchmarking five large language models in medical genetics: a bilingual comparative evaluation using published and novel expert-authored questions.Frontiers in medicine · 2026Article
- Performance of DeepSeek and ChatGPT on the Chinese Health Professional and Technical Examination: A comparative study.PloS one · 2026Article
- A critical juncture: Integrating large language models in biostatistical workflows.Journal of clinical and translational science · 2026Article
- A novel evaluation benchmark for medical LLMs illuminating safety and effectiveness in clinical domains.NPJ digital medicine · 2025Article
- Evaluation of ChatGPT-4o and DeepSeek as tools for orthodontic health literacy in public dental education.Scientific reports · 2025Article
- AI in primary care - a general practitioner's bucket list.The European journal of general practice · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundOwing to the rapid growth in the popularity of Large Language Models (LLMs), various performance evaluation studies have been conducted to confirm their applicability in the medical field. However, there is still no clear framework for evaluating LLMs.
objectiveThis study reviews studies on LLM evaluations in the medical field and analyzes the research methods used in these studies. It aims to provide a reference for future researchers designing LLM studies. METHODS & MATERIALS: We conducted a scoping review of three databases (PubMed, Embase, and MEDLINE) to identify LLM-related articles published between January 1, 2023, and September 30, 2023. We analyzed the types of methods, number of questions (queries), evaluators, repeat measurements, additional analysis methods, use of prompt engineering, and metrics other than accuracy.
resultsA total of 142 articles met the inclusion criteria. LLM evaluation was primarily categorized as either providing test examinations (n = 53, 37.3%) or being evaluated by a medical professional (n = 80, 56.3%), with some hybrid cases (n = 5, 3.5%) or a combination of the two (n = 4, 2.8%). Most studies had 100 or fewer questions (n = 18, 29.0%), 15 (24.2%) performed repeated measurements, 18 (29.0%) performed additional analyses, and 8 (12.9%) used prompt engineering. For medical assessment, most studies used 50 or fewer queries (n = 54, 64.3%), had two evaluators (n = 43, 48.3%), and 14 (14.7%) used prompt engineering.
conclusionsMore research is required regarding the application of LLMs in healthcare. Although previous studies have evaluated performance, future studies will likely focus on improving performance. A well-structured methodology is required for these studies to be conducted systematically.
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.