Summary of Assessing Empathy in Large Language Models with Real-world Physician-patient Interactions, by Man Luo et al.
Assessing Empathy in Large Language Models with Real-World Physician-Patient Interactions
by Man Luo, Christopher J. Warren, Lu Cheng, Haidar M. Abdul-Muhsin, Imon Banerjee
First submitted to arxiv on: 26 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel study investigates whether ChatGPT can provide more empathetic responses than physicians’ typically do. To answer this question, researchers collected de-identified patient messages and physician replies from Mayo Clinic, generating alternative responses using ChatGPT. The team developed a novel empathy ranking evaluation (EMRank) combining automated metrics and human assessments to gauge response empathy levels. Findings indicate that LLM-powered chatbots may surpass human physicians in delivering empathetic communication, suggesting a promising avenue for enhancing patient care and reducing professional burnout. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ChatGPT is a new way for healthcare to help patients feel better understood. Researchers compared ChatGPT’s answers with what doctors say to see if the computer can be more caring. They looked at messages from real patients and what doctors said in response, then had ChatGPT respond too. To figure out how good each answer was, they came up with a new way to measure empathy (EMRank). The study shows that these chatbots might be better at being understanding than doctors are! This could make it easier for people to get help and feel less stressed. |
