Summary of A Survey on Trustworthiness in Foundation Models For Medical Image Analysis, by Congzhen Shi et al.
A Survey on Trustworthiness in Foundation Models for Medical Image Analysis
by Congzhen Shi, Ryan Rezai, Jiaxi Yang, Qi Dou, Xiaoxiao Li
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); 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 The rapid advancement of foundation models in medical imaging enables enhanced diagnostic accuracy and personalized treatment. However, deploying these models in healthcare requires rigorous examination of their trustworthiness, encompassing privacy, robustness, reliability, explainability, and fairness. This survey aims to fill a gap by presenting a novel taxonomy of foundation models used in medical imaging and analyzing key motivations for ensuring their trustworthiness. We review current research on foundation models in major medical imaging applications, focusing on segmentation, medical report generation, Q&A, and disease diagnosis. Our analysis underscores the imperative for advancing towards trustworthy AI in medical image analysis, advocating for a balanced approach that fosters innovation while ensuring ethical healthcare delivery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation models are revolutionizing medical imaging by improving diagnostic accuracy and personalization. But before we can trust these models, we need to make sure they’re reliable, explainable, and fair. This survey looks at the current state of foundation models in medical imaging and what makes them trustworthy or not. We examine specific applications like segmentation, report generation, Q&A, and disease diagnosis. The goal is to create trustworthy AI that helps patients get better care. |
