# Medical Hallucinations in Foundation Models and Their Impact on Healthcare

**Authors:** Yubin Kim, Hyewon Jeong, Shan Chen, Shuyue Stella Li, Mingyu Lu, Kumail Alhamoud, Jimin Mun, Cristina Grau, Minseok Jung, Rodrigo Gameiro, Lizhou Fan, Eugene Park, Tristan Lin, Joonsik Yoon, Wonjin Yoon, Maarten Sap, Yulia Tsvetkov, Paul Liang, Xuhai Xu, Xin Liu  
**arXiv:** [2503.05777](https://arxiv.org/abs/2503.05777)  
**Date:** 2025-02-26  
**Summary page:** https://www.summarizepaper.com/en/paper/2503.05777v1/

## Key points

- Foundation Models have revolutionized AI in medicine, impacting the healthcare landscape
- Medical hallucinations occur when models produce inaccurate or fabricated information, potentially compromising patient safety
- Medical hallucination is defined as any instance where a model generates misleading medical content
- Research delves into characteristics, causes, and implications of medical hallucinations in real-world clinical scenarios
- Contributions include developing a taxonomy to understand and address medical hallucinations and benchmarking models using specialized datasets
- Inference techniques like Chain-of-Thought (CoT) and Search Augmented Generation can reduce occurrence of medical hallucinations
- Despite advancements, significant levels of hallucination still persist within AI models
- Urgent need for robust detection and mitigation strategies to address medical hallucinations effectively
- Establishment of regulatory policies prioritizing patient safety as AI integrates into healthcare systems

## Summary

In the realm of artificial intelligence (AI) in medicine, Foundation Models have revolutionized the processing and generation of multi-modal data, significantly impacting the healthcare landscape. However, a critical challenge that hinders their reliability is the occurrence of medical hallucinations. These are instances where these models produce inaccurate or fabricated information that can potentially influence clinical decisions and compromise patient safety. Medical hallucination is defined as any instance in which a model generates misleading medical content. This paper delves into an in-depth exploration of the unique characteristics, causes, and implications of medical hallucinations. It specifically focuses on how these errors manifest in real-world clinical scenarios. The contributions of this research include the development of a taxonomy aimed at understanding and addressing medical hallucinations. It also benchmarks models using a specialized dataset on medical hallucinations and physician-annotated responses to real medical cases to provide direct insights into their clinical impact. Additionally, a multi-national clinician survey was conducted to gather experiences and perspectives on medical hallucinations from healthcare professionals. The findings reveal that employing inference techniques such as Chain-of-Thought (CoT) and Search Augmented Generation can effectively reduce their occurrence. However, despite these advancements, significant levels of hallucination still persist within AI models. These results underscore the ethical and practical urgency for implementing robust detection and mitigation strategies to address medical hallucinations adequately. This imperative establishes a foundation for regulatory policies that prioritize patient safety and uphold clinical integrity as AI continues its integration into healthcare systems. Feedback from clinicians further emphasizes the pressing need for not only technical enhancements but also clearer ethical guidelines and regulatory frameworks to ensure patient safety amidst the growing presence of AI technologies in healthcare settings. A repository containing resources related to this paper, including summaries and additional information, is accessible at https://github.com/mitmedialab/medicalhallucination.