Summary of Radflag: a Black-box Hallucination Detection Method For Medical Vision Language Models, by Serena Zhang et al.
RadFlag: A Black-Box Hallucination Detection Method for Medical Vision Language Models
by Serena Zhang, Sraavya Sambara, Oishi Banerjee, Julian Acosta, L. John Fahrner, Pranav Rajpurkar
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 approach to enhancing the accuracy of radiology report generation from medical images using Vision Language Models (VLMs) is proposed. The current VLM-based solutions are susceptible to generating hallucinations, which can have serious implications for patient care. RadFlag, a black-box method, is introduced to address this challenge. This method employs a sampling-based flagging technique to identify and remove hallucinatory generations. By sampling multiple reports at varying temperatures and leveraging a Large Language Model (LLM) to detect inconsistencies, the proposed approach achieves high precision in identifying both individual hallucinatory sentences and reports containing hallucinations. The RadFlag system is easy to use, requiring only access to a model’s temperature parameter, making it compatible with a wide range of radiology report generation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new method helps doctors write more accurate reports from medical images using artificial intelligence (AI). Right now, AI can generate fake information that could be harmful if not caught. This new approach, called RadFlag, finds and removes these mistakes by looking at many possible reports and identifying parts that don’t make sense. The system is easy to use and works with different types of AI models. |
Keywords
* Artificial intelligence * Large language model * Precision * Temperature
