ArticleNPJ digital medicine2025
Preliminary analysis of the impact of lab results on large language model generated differential diagnoses.
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 12 papers.
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Who cites it
12 citing papers in PubMed.
- Assessing AI and Neurologist Diagnostic Reasoning Against Neuropathological Ground Truth.medRxiv : the preprint server for health sciences · 2026Article
- Generating Question Prompt Lists From Electronic Health Record Data Using Large Language Models: Iterative Evaluation Study.Journal of medical Internet research · 2026Article
- Evaluation of causal reasoning for large language models in contextualized clinical scenarios of laboratory test interpretation.NPJ digital medicine · 2026Article
- Generalist Foundation Models Are Not Clinical Enough for Hospital Operations.Research square · 2026Article
- The value of doubt: training LLMs to consider diagnostic uncertainty may improve clinical utility.NPJ digital medicine · 2026Article
- Independent and collaborative performance of large language models and healthcare professionals in diagnosis and triage.NPJ digital medicine · 2026Article
- Enhanced Atrial Fibrillation Prediction in ESUS Patients with Hypergraph-based Pre-training.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Utilizing Large Language Models for Zero-Shot Medical Ontology Extension from Clinical Notes.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2025Article
- Uncertainty-aware large language models for explainable disease diagnosis.NPJ digital medicine · 2025Article
- Large language models and their performance for the diagnosis of histoplasmosis.PLoS neglected tropical diseases · 2025Article
- Large language models for disease diagnosis: a scoping review.NPJ artificial intelligence · 2025Review
- KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
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10 authors.
Funding
Abstract
Differential diagnosis (DDx) is crucial for medicine as it helps healthcare providers systematically distinguish between conditions that share similar symptoms. This study evaluates the influence of lab test results on DDx accuracy generated by large language models (LLMs). Clinical vignettes from 50 randomly selected case reports from PMC-Patients were created, incorporating demographics, symptoms, and lab data. Five LLMs-GPT-4, GPT-3.5, Llama-2-70b, Claude-2, and Mixtral-8x7B-were tested to generate Top 10, Top 5, and Top 1 DDx with and without lab data. Results show that incorporating lab data enhances accuracy by up to 30% across models. GPT-4 achieved the highest performance, with Top 1 accuracy of 55% (0.41-0.69) and lenient accuracy reaching 79% (0.68-0.90). Statistically significant improvements (Holm-adjusted p values < 0.05) were observed, with GPT-4 and Mixtral excelling. Lab tests, including liver function, metabolic/toxicology panels, and serology, were generally interpreted correctly by LLMs for DDx.
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