Large language models in healthcare often miss critical emotional cues, delivering medically sound but emotionally flat advice. Such responses are insufficient in clinical encounters, where distressed or vulnerable patients rely on empathic communication to support safety, adherence, and trust. We present RECAP (Reflect-Extract-Calibrate-Align-Produce), an inference-time framework that guides models through structured emotional reasoning without retraining. RECAP decomposes patient input into appraisal-theoretic stages, identifies psychological factors, and assigns Likert-based emotion likelihoods that clinicians can inspect or override, producing nuanced and auditable responses. Across EmoBench, SECEU, and EQ-Bench, RECAP improves emotional reasoning by 22-28% on 8B models and 10-13% on larger models over zero-shot baselines. In blinded evaluations, oncology clinicians rated RECAP's responses as more empathetic, supportive, and context-appropriate than prompting baselines. These findings demonstrate that modular, principled prompting can enhance emotional intelligence in medical AI while maintaining transparency and accountability for clinical deployment.

翻译：医疗领域的大型语言模型常忽略关键情感线索，提供医学上正确但情感平淡的建议。此类回应在临床接触中不足，因为痛苦或脆弱的患者依赖共情沟通来获得安全感、依从性与信任。我们提出RECAP（反思-提取-校准-对齐-生成），一种无需重新训练的推理时框架，通过结构化情感推理引导模型。RECAP将患者输入解构为评价理论阶段，识别心理因素，并分配临床医生可检查或调整的李克特式情感可能性评分，生成细致且可审计的回应。在EmoBench、SECEU和EQ-Bench测试中，RECAP在80亿参数模型上将情感推理能力提升22-28%，在更大模型上较零样本基线提升10-13%。在盲法评估中，肿瘤科临床医生认为RECAP的回应比提示基线更具共情力、支持性且更符合情境。这些发现表明，模块化、原则性的提示方法可在保持临床部署透明度与问责制的同时，增强医疗人工智能的情感智能。