ArticleThe journal of pathology. Clinical research2024
Large language models as a diagnostic support tool in neuropathology.
Article in The journal of pathology. Clinical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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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
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Improving large language model applications in biomedicine with retrieval-augmented generation: a systematic review, meta-analysis, and clinical development guidelines.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- A systematic review on the generative AI applications in human medical genetics.Frontiers in genetics · 2025Pooled it
- Assessing the Accuracy, Completeness, and Reference Quality of GPT-4 and Google for Gynecologic Cancer Information: Comparative Quantitative Study.JMIR cancer · 2026Article
- Evaluation Methods for Inference-Time Retrieval-Augmented and Graph Retrieval-Augmented Large Language Models in Health Care: Scoping Review.Journal of medical Internet research · 2026Article
- Assessing AI and Neurologist Diagnostic Reasoning Against Neuropathological Ground Truth.medRxiv : the preprint server for health sciences · 2026Article
- Evaluating Retrieval Augmented Generation-enhanced Large Language Models for Question Answering On German Neurovascular Guidelines.Clinical neuroradiology · 2026Article
- Large Language Models in Clinical Neurology: A Systematic Review.Research square · 2026Article
- From diagnostics to education: Multi-domain evaluation of LLM chatbots in neurology.Journal of Taibah University Medical Sciences · 2026Review
- Cautionary lessons from real-world testing of GPT-4.1 AI for pediatric foreign body aspiration.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- [Research progress of large language models in tumor diagnosis: applications in textual reports and medical imaging].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Review
- Harnessing generative artificial intelligence for periodontitis prediction: a machine learning approach integrating systemic health indicators for precision oral health in resource-limited settings.Frontiers in dental medicine · 2026Article
- Precision oncology: from large language models to multi-agent systems.Frontiers in oncology · 2026Review
- Retrieval-augmented generation for interpreting clinical laboratory regulations using large language models.Journal of pathology informatics · 2025Article
- Large Language Models in Neurological Practice: Real-World Study.Journal of medical Internet research · 2025Article
- Large Language Models in Neurology Treatment Decision-Making: a Scoping Review.Journal of medical systems · 2025Article
- Diagnostic Performance of ChatGPT-4.0 in Histopathological Analysis of Gliomas: A Single Institution Experience.Neuropathology : official journal of the Japanese Society of Neuropathology · 2025Article
- Large Language Models in Medical Diagnostics: Scoping Review With Bibliometric Analysis.Journal of medical Internet research · 2025Article
- Authors' reply: Re: Koga et al. Retrieval-augmented generation versus document-grounded generation: a key distinction in large language models.The journal of pathology. Clinical research · 2025Article
- Retrieval-augmented generation versus document-grounded generation: a key distinction in large language models.The journal of pathology. Clinical research · 2025Article
- Exploring the Potential of Claude 3 Opus in Renal Pathological Diagnosis: Performance Evaluation.JMIR medical informatics · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The WHO guidelines for classifying central nervous system (CNS) tumours are changing considerably with each release. The classification of CNS tumours is uniquely complex among most other solid tumours as it incorporates not just morphology, but also genetic and epigenetic features. Keeping current with these changes across medical fields can be challenging, even for clinical specialists. Large language models (LLMs) have demonstrated their ability to parse and process complex medical text, but their utility in neuro-oncology has not been systematically tested. We hypothesised that LLMs can effectively diagnose neuro-oncology cases from free-text histopathology reports according to the latest WHO guidelines. To test this hypothesis, we evaluated the performance of ChatGPT-4o, Claude-3.5-sonnet, and Llama3 across 30 challenging neuropathology cases, which each presented a complex mix of morphological and genetic information relevant to the diagnosis. Furthermore, we integrated these models with the latest WHO guidelines through Retrieval-Augmented Generation (RAG) and again assessed their diagnostic accuracy. Our data show that LLMs equipped with RAG, but not without RAG, can accurately diagnose the neuropathological tumour subtype in 90% of the tested cases. This study lays the groundwork for a new generation of computational tools that can assist neuropathologists in their daily reporting practice.
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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.