ArticleNPJ digital medicine2025
Evaluation of performance of generative large language models for stroke care.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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.
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Who cites it
8 citing papers in PubMed.
- Development of a Framework for Evaluating Large Language Model Safety and Reliability: a Proof-of-Concept Evaluation.Journal of medical systems · 2026Article
- Review
- A Multiagent Large Language Model Framework for Emergency Treatment Recommendation in Acute Ischemic Stroke: Development and Validation Study.Journal of medical Internet research · 2026Article
- AI-based diagnostic evaluation of GPT-4o for crown-fracture detection on maxillary periapical radiographs: effects of prompt detail and customization.BMC oral health · 2026Article
- Identification of quality and safety concerns in AI chatbot responses to procedural sedation: a comparative evaluation of ChatGPT, Gemini, and Copilot.Frontiers in health services · 2026Article
- Comparative performance of ChatGPT-4o, ChatGPT-5, and gemini 2.5 flash on Persian internal medicine subspecialty board exams.Scientific reports · 2025Article
- Diagnostic performance of large language models on the NEJM image challenge: a comparative study with human evaluators and the impact of prompt engineering.Frontiers in medicine · 2025Article
- A two-stage evaluation of large language models for guideline-based Q&A and draft rehabilitation planning in stroke care.Digital healthArticle
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Stroke is a leading cause of global morbidity and mortality, disproportionately impacting lower socioeconomic groups. In this study, we evaluated three generative LLMs-GPT, Claude, and Gemini-across four stages of stroke care: prevention, diagnosis, treatment, and rehabilitation. Using three prompt engineering techniques-Zero-Shot Learning (ZSL), Chain of Thought (COT), and Talking Out Your Thoughts (TOT)-we applied each to realistic stroke scenarios. Clinical experts assessed the outputs across five domains: (1) accuracy; (2) hallucinations; (3) specificity; (4) empathy; and (5) actionability, based on clinical competency benchmarks. Overall, the LLMs demonstrated suboptimal performance with inconsistent scores across domains. Each prompt engineering method showed strengths in specific areas: TOT does well in empathy and actionability, COT was strong in structured reasoning during diagnosis, and ZSL provided concise, accurate responses with fewer hallucinations, especially in the Treatment stage. However, none consistently met high clinical standards across all stroke care stages.
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.