Articlenpj health systems2025
Explainable differential diagnosis with dual-inference large language models.
Article in npj health systems, 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.
- Artificial Intelligence-powered tiered early warning framework addressing high false alarm rates for in-hospital mortality prediction.NPJ digital medicine · 2026Article
- ChatGPT in precision medicine.APL bioengineering · 2026Review
- Large language models with retrieval-augmented generation enhance expert modelling of Bayesian network for clinical decision support.International journal of computer assisted radiology and surgery · 2026Article
- EPEE: towards efficient and effective foundation models in biomedicine.npj health systems · 2026Article
- Automating expert-level medical reasoning evaluation of large language models.NPJ digital medicine · 2025Article
- Uncertainty-aware large language models for explainable disease diagnosis.NPJ digital medicine · 2025Article
- Clinical insights: A comprehensive review of language models in medicine.PLOS digital health · 2025Review
- Large language models for disease diagnosis: a scoping review.NPJ artificial intelligence · 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
8 authors.
Funding
Abstract
Automatic differential diagnosis (DDx) involves identifying potential conditions that could explain a patient's symptoms and its accurate interpretation is of substantial significance. While large language models (LLMs) have demonstrated remarkable diagnostic accuracy, their capability to generate high-quality DDx explanations remains underexplored, largely due to the absence of specialized evaluation datasets and the inherent challenges of complex reasoning in LLMs. Therefore, building a tailored dataset and developing novel methods to elicit LLMs for generating precise DDx explanations are worth exploring. We developed the first publicly available DDx dataset, comprising expert-derived explanations for 570 clinical notes, to evaluate DDx explanations. Meanwhile, we proposed a novel framework, Dual-Inf, that could effectively harness LLMs to generate high-quality DDx explanations. To the best of our knowledge, it is the first study to tailor LLMs for DDx explanation and comprehensively evaluate their explainability. Overall, our study bridges a critical gap in DDx explanation, enhancing clinical decision-making.
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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.