ArticleNPJ digital medicine2025
Uncertainty-aware large language models for explainable disease diagnosis.
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.
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
12 citing papers in PubMed.
- Mechanism-Informed Language Modeling and Oxygenated 3D Screening Identify Berberine-Enzalutamide Synergy in Prostate Cancer Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- On-premise medical AI agents for reliable clinical decision-making.Nature medicine · 2026Article
- Navigating uncertainty matters: Evaluating large language models for drug-drug interaction identification.Journal of managed care & specialty pharmacy · 2026Article
- Performance of DeepSeek-R1 and ChatGPT-5 in the Generation of North American Spine Society Clinical Guidelines for Adult Vertebral Compression Fractures: Comparative Study.Journal of medical Internet research · 2026Article
- Benchmarking General Purpose Artificial Intelligence for Accessory Pathway Localisation on 12-Lead Electrocardiograms: A Proof-of-Concept Study.Journal of clinical medicine · 2026Article
- Article
- Recent advances in machine learning-enhanced extracellular vesicle omics for oncology.Journal of nanobiotechnology · 2026Review
- Engineering framework for curiosity-driven and humble AI in clinical decision support.BMJ health & care informatics · 2026Article
- The value of doubt: training LLMs to consider diagnostic uncertainty may improve clinical utility.NPJ digital medicine · 2026Article
- Mechanistic Language Modeling and Oxygenated 3D Screening Reveal Berberine and Enzalutamide Synergy in Resistant Prostate Cancer.bioRxiv : the preprint server for biology · 2026Article
- CLIN-LLM: A safety-constrained hybrid framework for clinical diagnosis and treatment generation.PloS one · 2026Article
- Automating expert-level medical reasoning evaluation of large language models.NPJ digital medicine · 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
14 authors.
Funding
Abstract
Explainable disease diagnosis, which leverages patient information (e.g., symptoms) and computational models to generate probable diagnoses and reasoning, holds strong clinical promise. Yet, when clinical notes lack sufficient evidence for a definitive diagnosis, such as the absence of definitive symptoms, diagnostic uncertainty commonly arises, increasing the risk of misdiagnosis. Despite its importance, the explicit identification and explanation of diagnostic uncertainty remain under-explored in artificial intelligence-driven systems. To fill this gap, we introduce ConfiDx, an uncertainty-aware large language model fine-tuned with diagnostic criteria. We formalized the task of uncertainty-aware diagnosis and curated richly annotated datasets that reflect varying degrees of diagnostic ambiguity. Evaluating on real-world datasets demonstrated that ConfiDx excelled in identifying diagnostic uncertainties, achieving superior diagnostic performance, and generating trustworthy explanations for diagnoses and uncertainties. Moreover, ConfiDx-assisted experts outperformed standalone experts by 10.7% in uncertainty recognition and 26% in uncertainty explanation, underscoring its substantial potential to improve clinical decision-making.
Identifiers
What Socratic 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 Socratic graph.