Summary of Specialized Curricula For Training Vision-language Models in Retinal Image Analysis, by Robbie Holland et al.
Specialized curricula for training vision-language models in retinal image analysis
by Robbie Holland, Thomas R. P. Taylor, Christopher Holmes, Sophie Riedl, Julia Mai, Maria Patsiamanidi, Dimitra Mitsopoulou, Paul Hager, Philip Müller, Hendrik P. N. Scholl, Hrvoje Bogunović, Ursula Schmidt-Erfurth, Daniel Rueckert, Sobha Sivaprasad, Andrew J. Lotery, Martin J. Menten
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 abstract discusses the potential of vision-language models (VLMs) to alleviate clinical workloads and improve patient care by automatically interpreting medical images and summarizing findings in text. While foundational models have shown promise, it is unclear if they can be applied to real-world clinical tasks. The study compares the performance of OpenAI’s ChatGPT-4o model and two foundation VLMs designed for medical use with practicing ophthalmologists on specialist tasks related to age-related macular degeneration (AMD). The results show that these models underperform compared to ophthalmologists, highlighting the need for specialized training. A curriculum-based approach is developed to selectively train VLMs in image-based clinical decision-making skills, resulting in a model called RetinaVLM. This model outperforms leading foundation medical VLMs and ChatGPT-4o on disease staging and patient referral tasks, approaching the diagnostic performance of junior ophthalmologists. Senior ophthalmologists also found RetinaVLM’s reports to be more accurate than those by ChatGPT-4o. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study shows that vision-language models (VLMs) can’t replace experienced ophthalmologists just yet. Researchers compared these AI models with expert doctors on certain tasks and found they didn’t do as well. To fix this, the team created a special training program for VLMs to help them make better decisions about medical images. This new model, called RetinaVLM, did much better than before. It was able to write reports that were just as good as those written by junior doctors and almost as good as those written by senior ophthalmologists. |
