# Challenges of Auditing: Variability in Outputs of Large Language Models for Health

arXiv: https://arxiv.org/abs/2609.16590 · HTML: https://mlsanity.com/paper/2609.16590/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 15 Sep 2026.

## Builds on (most cited first, 9 of 9)

- [HealthBench: Evaluating Large Language Models Towards Improved Human Health](https://mlsanity.com/paper/2505.08775/) (2025)
- [ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge](https://mlsanity.com/paper/2303.14070/) (2023)
- [Black-Box Access is Insufficient for Rigorous AI Audits](https://mlsanity.com/paper/2401.14446/) (2024)
- [Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries](https://mlsanity.com/paper/2310.13132/) (2023)
- [Large language models provide unsafe answers to patient-posed medical questions](https://mlsanity.com/paper/2507.18905/) (2025)
- [An Empirical Evaluation of Large Language Models on Consumer Health Questions](https://mlsanity.com/paper/2501.00208/) (2024)
- [LLM Spirals of Delusion: A Benchmarking Audit Study of AI Chatbot Interfaces](https://mlsanity.com/paper/2604.06188/) (2026)
- [Evaluating Search Engines and Large Language Models for Answering Health Questions](https://mlsanity.com/paper/2407.12468/) (2024)
- [What Current AI Benchmarks Leave Unmeasured: Modality, Search, Citations, and Implications (for Safety Evaluations)](https://mlsanity.com/paper/2608.06202/) (2026)

## Abstract (arXiv)

People increasingly use frontier AI models for health advice, but via different access modes (e.g., ChatGPT, ChatGPT Health, APIs) with varying settings. Here, we find systematic differences across access modes. Because evaluations typically rely on APIs while consumers interact through chatbot interfaces, these discrepancies limit evaluation validity. Our findings underscore an urgent need for model providers to enable faithful replication of consumer experiences and settings for rigorous audits.