# Med-MMHL: A Multi-Modal Dataset for Detecting Human- and LLM-Generated Misinformation in the Medical Domain

arXiv: https://arxiv.org/abs/2306.08871 · HTML: https://mlsanity.com/paper/2306.08871/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2023.

## Built on by (most cited first, 9 of 9)

- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challenges](https://mlsanity.com/paper/2403.18249/) (2024)
- [The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective](https://mlsanity.com/paper/2407.08583/) (2024)
- [From Deception to Detection: The Dual Roles of Large Language Models in Fake News](https://mlsanity.com/paper/2409.17416/) (2024)
- [Gen-Review: A Large-scale Dataset of AI-Generated (and Human-written) Peer Reviews](https://mlsanity.com/paper/2510.21192/) (2025)
- [Classifying Human-Generated and AI-Generated Election Claims in Social Media](https://mlsanity.com/paper/2404.16116/) (2024)
- [A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination](https://mlsanity.com/paper/2505.00008/) (2025)
- [Tailored untruths: How personalisation challenges LLM safeguards](https://mlsanity.com/paper/2510.12993/) (2025)
- [Securing Reliability: A Brief Overview on Enhancing In-Context Learning for Foundation Models](https://mlsanity.com/paper/2402.17671/) (2024)

## Abstract (arXiv)

The pervasive influence of misinformation has far-reaching and detrimental effects on both individuals and society. The COVID-19 pandemic has witnessed an alarming surge in the dissemination of medical misinformation. However, existing datasets pertaining to misinformation predominantly focus on textual information, neglecting the inclusion of visual elements, and tend to center solely on COVID-19-related misinformation, overlooking misinformation surrounding other diseases. Furthermore, the potential of Large Language Models (LLMs), such as the ChatGPT developed in late 2022, in generating misinformation has been overlooked in previous works. To overcome these limitations, we present Med-MMHL, a novel multi-modal misinformation detection dataset in a general medical domain encompassing multiple diseases. Med-MMHL not only incorporates human-generated misinformation but also includes misinformation generated by LLMs like ChatGPT. Our dataset aims to facilitate comprehensive research and development of methodologies for detecting misinformation across diverse diseases and various scenarios, including human and LLM-generated misinformation detection at the sentence, document, and multi-modal levels. To access our dataset and code, visit our GitHub repository: urlhttps://github.com/styxsys0927/Med-MMHL.