arXiv:2609.27987v1 Announce Type: new Abstract: Large language models (LLMs) have made substantial progress on medical question-answering, yet effective medical dialogue also requires learning to ask...
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.
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:2608. 09080v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance in medical question answering and clinical reasoning tasks.
MedConceal is a new benchmark for evaluating medical dialogue systems on hidden‑concern reasoning under partial observability. It features 300 curated cases and 600 clinician‑LLM interactions, using an interactive patient simulator that hides latent concerns and tracks their revelation and resolution through theory‑grounded communication signals. The benchmark assesses both confirmation (surfacing hidden concerns) and intervention (addressing the primary concern), revealing that current models excel on different metrics while human clinicians still outperform them on intervention success.
arXiv:2601. 09853v3 Announce Type: replace-cross Abstract: Real-world health questions from patients often unintentionally embed false assumptions or premises.
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...
The paper introduces SelfDx, a benchmark that tests whether AI models appropriately respond to health queries by either cautioning against self-diagnosis or agreeing when evidence rules out serious alternatives. It evaluates 27 frontier models on 100 paired cases, finding that most models inappropriately agree in caution-required scenarios, achieving only a 77.0% appropriate caution rate. The study shows that prompt engineering can raise caution but at the cost of agreement, while supervised fine‑tuning on balanced examples improves both metrics.
The paper introduces PRISM, a framework that separates belief estimation from action selection to handle irregular state transitions in online medical consultations. By maintaining a posterior belief over consultation states, PRISM improves clinical decision accuracy by 22.40 percentage points and response quality by 5.51 BERTScore_F1 points compared to leading LLMs and clinical agents. Ablation studies confirm that oracle state information further enhances response quality across baselines.
arXiv:2610.09209v1 Announce Type: new Abstract: Substance Use Disorder (SUD) counseling requires patient responses that reflect underlying cognitive states such as beliefs, coping strategies, and rea...
arXiv:2608. 20331v1 Announce Type: cross Abstract: Personalized interpretation of medical reports has emerged as an increasingly important need among patients.
The paper introduces Expected‑Severity‑Risk (ESR), a new objective for selecting questions in proactive medical dialogue that prioritizes reducing the expected severity of diagnostic errors rather than merely uncertainty. ESR uses population statistics to marginalize over possible answers and distills its rankings into a prefix‑only language policy, enabling deployment without teacher‑side risk computation. Experiments on DDxPlus show ESR cuts high‑severity diagnostic misses by 29.5% and boosts accuracy while adding only 0.14 extra questions per dialogue.