arXiv AI
Jul 28
RareLens: Towards End-to-End Rare Disease Care via Aligning Divergent Large Language Model Reasoning
arXiv:2607. 23290v1 Announce Type: new Abstract: Rare diseases collectively affect an estimated 3.
A specialized reasoning large language model for accelerating rare disease diagnosis: a randomized AI physician assistance trial
arXiv:2606. 24510v1 Announce Type: new Abstract: Rare diseases affect millions of individuals worldwide, yet timely diagnosis remains a major public health challenge due to scarcity of specialized clinical expertise.
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arXiv AI
Aug 18
DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models
arXiv:2505. 14107v5 Announce Type: replace-cross Abstract: The emergence of groundbreaking large language models capable of performing complex reasoning tasks holds significant promise for addressing various scientific challenges, including those arising in complex clinical scenarios.
arXiv AI
Jul 2
RareDxR1: Autonomous Medical Reasoning for Rare Disease Diagnosis Beyond Human Annotation
arXiv:2607. 00147v1 Announce Type: new Abstract: Rare disease differential diagnosis is a critical yet arduous clinical task, requiring physicians to identify precise phenotypes from complex, unstructured patient symptoms and execute intricate reasoning within a vast search space.
arXiv AI
Jun 16
LiteOdyssey: A Lightweight Reasoning AI Agent for Interpretable Rare-Disease Diagnosis
arXiv:2606. 16149v1 Announce Type: new Abstract: Most medical AI systems improve by scaling additional machinery: more fine-tuning data, more agents, and/or larger retrieval databases.
arXiv AI
Sep 10
Teaching agentic AI to generalize expert diagnostic reasoning in rare diseases
arXiv:2606.16149v5 Announce Type: replace Abstract: Rare disease diagnosis depends on expert reasoning that is scarce and difficult to transfer. Large language models rank the correct disease first i...
arXiv AI
Aug 13
Teaching agentic AI to learn expert reasoning for rare disease diagnosis
arXiv:2606. 16149v3 Announce Type: replace Abstract: Rare disease diagnosis depends on expert reasoning that is scarce and difficult to transfer; off-the-shelf large language models (LLMs) rank the correct disease first in only 35.
arXiv Computer Vision
Aug 25
Sparse Multi-Stage Expert-Agent Routing for Complex Clinical Reasoning
arXiv:2608.21948v1 Announce Type: new Abstract: Complex clinical reasoning requires models to update diagnostic hypotheses as new evidence emerges and to coordinate different medical specialities und...
arXiv AI
Aug 25
ClinicalGPT-R1: Pushing reasoning capability of generalist disease diagnosis with large language model
ClinicalGPT‑R1 is a reasoning‑enhanced generalist large language model designed for disease diagnosis. It was trained on 20,000 real‑world clinical records and uses diverse training strategies to improve diagnostic reasoning. In benchmarks, it outperforms GPT‑4o on Chinese diagnostic tasks and matches GPT‑4 performance in English, demonstrating superior disease‑diagnosis capabilities.
arXiv Machine Learning
Aug 7
Clinician input steers AI toward accurate and harmful recommendations
arXiv:2603. 14158v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are entering clinical workflows, yet evaluations rarely assess how clinician reasoning shapes model behavior during clinical interactions.
arXiv AI
Jul 10
Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning
arXiv:2607. 07761v1 Announce Type: new Abstract: Large language models (LLMs) have emerged as important tools in healthcare, showing growing potential for clinical reasoning and patient care.
arXiv AI
Jul 10
A safety-oriented hypothetico-deductive framework for AI-assisted differential diagnosis
arXiv:2607. 08038v1 Announce Type: new Abstract: Diagnostic error is a major threat to patient safety, yet current large language model (LLM) systems often treat diagnosis as a one-shot prediction task, lacking safeguards against missed high-risk alternatives or rigorous verification of their reasoning.
arXiv AI
Sep 1
SUP-MIMIC: A Multi-Task Clinical Diagnosis Benchmark for Evaluating LLMs' Robustness to Contradictory Evidence
arXiv:2608.29582v1 Announce Type: cross Abstract: Current evaluations of large language models (LLMs) primarily focus on factual knowledge retrieval, overlooking the fundamental challenge of navigati...