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

arXiv: https://arxiv.org/abs/2601.15558 · HTML: https://mlsanity.com/paper/2601.15558/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 59 references that are in the map.
- First posted on arXiv 22 Jan 2026.

## Builds on (most cited first, 19 of 19)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
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- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support](https://mlsanity.com/paper/2203.15144/) (2022)
- [A Computational Framework for Behavioral Assessment of LLM Therapists](https://mlsanity.com/paper/2401.00820/) (2024)
- [A Taxonomy of Empathetic Response Intents in Human Social Conversations](https://mlsanity.com/paper/2012.04080/) (2020)
- [Training language models to be warm and empathetic makes them less reliable and more sycophantic](https://mlsanity.com/paper/2507.21919/) (2025)
- [ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models](https://mlsanity.com/paper/2406.14952/) (2024)
- [Enhancing Empathetic Response Generation by Augmenting LLMs with Small-scale Empathetic Models](https://mlsanity.com/paper/2402.11801/) (2024)
- [Assessing Empathy in Large Language Models with Real-World Physician-Patient Interactions](https://mlsanity.com/paper/2405.16402/) (2024)
- [FactEHR: A Dataset for Evaluating Factuality in Clinical Notes Using LLMs](https://mlsanity.com/paper/2412.12422/) (2024)
- [A Critical Reflection and Forward Perspective on Empathy and Natural Language Processing](https://mlsanity.com/paper/2210.16604/) (2022)
- [Investigating Stylistic Profiles for the Task of Empathy Classification in Medical Narrative Essays](https://mlsanity.com/paper/2302.01839/) (2023)

## Abstract (arXiv)

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.