arXiv AI
Ask or Answer: A Decision Framework for Multi-Turn Health Misinformation Intervention
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Large Language Model Few-Shot Prompting with Dilemma Training Outperforms Human Surrogates in Predicting Patient Preferences
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 AI
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When Confidence Fails: Overconfidence in LLMs under Uncertainty and Missing Clinical Information
arXiv:2608. 09080v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance in medical question answering and clinical reasoning tasks.
arXiv Computation and Language
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MedConceal: A Benchmark for Clinical Hidden-Concern Reasoning Under Partial Observability
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arXiv AI
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MedRedFlag: Investigating how LLMs Redirect Misconceptions in Real-World Health Communication
arXiv:2601. 09853v3 Announce Type: replace-cross Abstract: Real-world health questions from patients often unintentionally embed false assumptions or premises.
Hugging Face Trending Papers
Aug 26
Large Language Model Few-Shot Prompting with Dilemma Training Outperforms Human Surrogates in Predicting Patient Preferences
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 AI
Aug 21
G-CARL: Grounded Checklist-Aligned Reward Learning for Patient-Oriented Medical Report Interpretation
arXiv:2608. 20331v1 Announce Type: cross Abstract: Personalized interpretation of medical reports has emerged as an increasingly important need among patients.