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RECAP framework enhances medical AI's emotional intelligence and transparency

By PulseAugur Editorial · [1 sources] ·

Researchers have developed RECAP, a novel framework designed to enhance the emotional intelligence and transparency of large language models in medical dialogue systems. This inference-time approach, based on cognitive appraisal theory, decomposes patient input into auditable stages without requiring model retraining. Evaluations across various models showed RECAP improves alignment with human judgments, particularly for smaller models, and revealed that models tend to underweight relational factors. In blinded trials, oncology fellows rated RECAP-enhanced responses significantly higher than baselines, indicating its potential for improving clinical trust in AI. AI

IMPACT Enhances emotional alignment and transparency in medical AI, potentially increasing clinical trust and adoption.

RANK_REASON The cluster contains a research paper detailing a new framework for improving AI in medical dialogue systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

RECAP framework enhances medical AI's emotional intelligence and transparency

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The cluster contains a research paper detailing a new framework for improving AI in medical dialogue systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Adarsh Srinivasan, Jacob Dineen, Muhammad Umar Afzal, Muhammad Uzair Sarfraz, Irbaz B. Riaz, Ben Zhou ·

    RECAP: Transparent Inference-Time Emotion Alignment for Medical Dialogue Systems

    arXiv:2509.10746v3 Announce Type: replace Abstract: Large language models in healthcare often produce emotionally flat or opaque responses, failing to provide the transparent reasoning required for clinical trust. We present RECAP (Reflect-Extract-Calibrate-Align-Produce), an inf…

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