From Generation to Collaboration: Using LLMs to Edit for Empathy in Healthcare

2026-01-22

AI Summary

This work proposes a collaborative framework in which large language models (LLMs) serve as “empathy editors” to refine clinician-authored messages, enhancing empathetic expression while preserving medical accuracy. Recognizing that clinicians under pressure often struggle to balance factual precision with emotional attunement—leading to perceived empathy deficits in patient communication—the approach emphasizes human–AI collaboration rather than end-to-end generation. To evaluate performance, the study introduces two novel metrics: the Empathy Ranking Score, which assesses perceived empathy, and the MedFactChecking Score, which verifies clinical fidelity. Experimental results demonstrate that LLM-edited texts significantly improve patients’ perception of empathy without compromising medical correctness, outperforming fully LLM-generated responses.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Emotional Intelligence

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Clinical empathy is essential for patient care, but physicians need continually balance emotional warmth with factual precision under the cognitive and emotional constraints of clinical practice. This study investigates how large language models (LLMs) can function as empathy editors, refining physicians'written responses to enhance empathetic tone while preserving underlying medical information. More importantly, we introduce novel quantitative metrics, an Empathy Ranking Score and a MedFactChecking Score to systematically assess both emotional and factual quality of the responses. Experimental results show that LLM edited responses significantly increase perceived empathy while preserving factual accuracy compared with fully LLM generated outputs. These findings suggest that using LLMs as editorial assistants, rather than autonomous generators, offers a safer, more effective pathway to empathetic and trustworthy AI-assisted healthcare communication.

Problem

• clinical empathy
• empathy editing
• factual accuracy
• healthcare communication
• large language models

Innovation

• empathy editing
• large language models
• human-AI collaboration
• factual accuracy
• healthcare communication