# Can "AI" Be a Doctor? A Study of Empathy, Readability, and Alignment in Clinical LLMs

arXiv: https://arxiv.org/abs/2604.20791 · HTML: https://mlsanity.com/paper/2604.20791/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 51 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [A Question-Entailment Approach to Question Answering](https://mlsanity.com/paper/1901.08079/) (2019)
- [SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Model Safety](https://mlsanity.com/paper/2404.05399/) (2024)
- [A Framework for Human Evaluation of Large Language Models in Healthcare Derived from Literature Review](https://mlsanity.com/paper/2405.02559/) (2024)
- [What are human values, and how do we align AI to them?](https://mlsanity.com/paper/2404.10636/) (2024)
- [A Novel Evaluation Benchmark for Medical LLMs: Illuminating Safety and Effectiveness in Clinical Domains](https://mlsanity.com/paper/2507.23486/) (2025)
- [Assessing Empathy in Large Language Models with Real-World Physician-Patient Interactions](https://mlsanity.com/paper/2405.16402/) (2024)
- [Investigating Large Language Models and Control Mechanisms to Improve Text Readability of Biomedical Abstracts](https://mlsanity.com/paper/2309.13202/) (2023)
- [An Empirical Evaluation of Large Language Models on Consumer Health Questions](https://mlsanity.com/paper/2501.00208/) (2024)
- [Trustworthy AI for Medicine: Continuous Hallucination Detection and Elimination with CHECK](https://mlsanity.com/paper/2506.11129/) (2025)
- [Aligning Large Language Models with Healthcare Stakeholders: A Pathway to Trustworthy AI Integration](https://mlsanity.com/paper/2505.02848/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) are increasingly deployed in healthcare, yet their communicative alignment with clinical standards remains insufficiently quantified. We conduct a multidimensional evaluation of general-purpose and domain-specialized LLMs across structured medical explanations and real-world physician-patient interactions, analyzing semantic fidelity, readability, and affective resonance. Baseline models amplify affective polarity relative to physicians (Very Negative: 43.14-45.10% vs. 37.25%) and, in larger architectures such as GPT-5 and Claude, produce substantially higher linguistic complexity (FKGL up to 16.91-17.60 vs. 11.47-12.50 in physician-authored responses). Empathy-oriented prompting reduces extreme negativity and lowers grade-level complexity (up to -6.87 FKGL points for GPT-5) but does not significantly increase semantic fidelity. Collaborative rewriting yields the strongest overall alignment. Rephrase configurations achieve the highest semantic similarity to physician answers (up to mean = 0.93) while consistently improving readability and reducing affective extremity. Dual stakeholder evaluation shows that no model surpasses physicians on epistemic criteria, whereas patients consistently prefer rewritten variants for clarity and emotional tone. These findings suggest that LLMs function most effectively as collaborative communication enhancers rather than replacements for clinical expertise.