The paper introduces P4-DT, a personalized patient preference predictor that uses dilemma training to elicit context‑dependent decision reasoning. In a study of 12 patient‑surrogate pairs, P4‑DT achieved 81.7% accuracy in predicting patient treatment choices, outperforming unassisted surrogates (55.0%) and surrogates aided by a simpler P4 model (61.7%). The authors show that incorporating contextual scenarios and open‑ended text into prompts improves accuracy by 15 percentage points over static value ratings.
In serious illness, human surrogates often struggle to accurately predict patient preferences (68% accuracy), causing decision conflict. Personalized Patient Preference Predictor (P4) agents offer a p...
arXiv:2601. 16529v4 Announce Type: replace Abstract: Large language models (LLMs) deployed in clinical decision support may acquiesce to patient requests for care that conflicts with evidence-based guidelines.
arXiv:2609.24480v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) in healthcare requires robust performance across two complementary dimensions - diagnostic reasoning: the conv...
arXiv:2609.39566v1 Announce Type: new Abstract: Foundation models can serve as clinical agents through tool-use harnesses. However, conventional medical benchmarks assess reasoning over preselected e...
arXiv:2608.21864v1 Announce Type: cross Abstract: The current progress of Clinical Vision Large Language Models (C-VLLMs) has substantially improved digital diagnostics, still these frameworks often...
The paper introduces DeToxR, a reinforcement‑learning‑enhanced large language model designed to support decision making in acute toxicology cases. It fuses unstructured narratives from paramedics and patients with structured vital‑sign data to predict co‑ingested substances across 14 classes. In preliminary validation, DeToxR outperforms baseline models, achieving higher micro‑F1 and recall scores for poison identification.
arXiv:2608.21721v1 Announce Type: new Abstract: Correcting health misinformation in dialogue requires more than producing a factual rebuttal: users differ in what they know, what they believe, and wh...
CARE: Causally-Aligned Reasoning Exploration for Medical Large Language Models proposes a new framework to improve medical reasoning in LLMs. It introduces two key conditions—Causal Sufficiency and Proximal Learnability—to curate high-quality training trajectories, using agreement-based self-verification and dynamic entropy bounds. Experiments on medical multimodal and text-only benchmarks show that CARE outperforms competitors, reducing incorrect reasoning and enhancing training stability.
arXiv:2607. 25369v1 Announce Type: new Abstract: Agentic systems have rapidly advanced in their ability to interact with real-world environments, leverage external tools, and provide services for users.
arXiv:2512. 01241v4 Announce Type: replace-cross Abstract: Large language models (LLMs) and medical AI tools are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized.
arXiv:2610.34878v1 Announce Type: new Abstract: Disease diagnosis is a key application of large language model (LLM) agents in healthcare. However, existing medical agents are primarily trained and e...
arXiv:2604. 06684v2 Announce Type: replace Abstract: Clinical reasoning over electronic health records (EHRs) is a fundamental yet challenging task in modern healthcare.