ArticlePloS one2025
Evaluating the diagnostic performance of OpenBioLLM in neurology: A case-based assessment of a medical large language model.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Implicit bias in safety-aligned large language models: A multi-faceted evaluation of clinical decision-making and health equity.PloS one · 2026Article
- Large language models for neurology: a mini review.Frontiers in digital health · 2025Review
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
No grant is acknowledged in the PubMed record.
Abstract
In the evolving field of neurological healthcare, deep learning technologies are gaining recognition for their potential to enhance diagnostic accuracy. Transformer-based models, particularly large language models (LLMs) such as OpenBioLLM, have shown promise in processing large datasets typical of neurological assessments. This study evaluates the diagnostic capabilities of OpenBioLLM in the realm of neurological conditions. The primary aim of this research is to assess the diagnostic accuracy, comprehensiveness, supplementation, and fluency of OpenBioLLM when applied to complex neurological case studies. Twenty-five complex neurology cases were selected from "Clinical Cases in Neurology." OpenBioLLM was used to generate diagnoses and rationales for each case. Two independent medical doctors evaluated the responses based on accuracy, comprehensiveness, supplementation, and fluency, with discrepancies resolved by a third assessor. Statistical analyses included one-way ANOVA, Bartlett's test, and Spearman's rank correlation. OpenBioLLM achieved a mean accuracy score of 38%, a comprehensiveness score of 52%, a supplementation score of 24%, and a fluency score of 100%. The model could localize neurological lesions but often struggled with identifying the correct pathophysiological causes. Accuracy scores did not significantly vary by neurological disorder type. While OpenBioLLM shows potential in diagnosing neurological conditions, its performance metrics suggest it is not yet a reliable standalone tool. Future research should focus on fine-tuning the model and improving its reasoning capabilities to enhance diagnostic accuracy.
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.