Summary of Multimed: Massively Multimodal and Multitask Medical Understanding, by Shentong Mo et al.
MultiMed: Massively Multimodal and Multitask Medical Understanding
by Shentong Mo, Paul Pu Liang
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
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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 This paper presents a benchmark called MultiMed that aims to evaluate and enable large-scale learning across multiple medical modalities and tasks. The authors highlight the limitations of current biomedical AI approaches, which typically only train and evaluate with one or a few medical modalities and tasks. To address this challenge, they propose a comprehensive benchmark consisting of 2.56 million samples across ten medical modalities, including medical reports, pathology, genomics, and protein data, structured into eleven challenging tasks such as disease prognosis, protein structure prediction, and medical question answering. The authors conduct experiments using state-of-the-art unimodal, multimodal, and multitask models to benchmark their approach. They demonstrate the advantages of training large-scale medical models across many related modalities and tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special kind of AI tool that can help doctors make better decisions by looking at lots of different kinds of medical information together. Right now, AI tools are only good at using one or two types of information to make predictions. But in medicine, there’s often more than just one thing that matters – like what the doctor sees when they look at a patient, plus what tests show, and even things that patients wear on their bodies. To fix this problem, the authors created a big database called MultiMed that has lots of different kinds of medical information, like reports from doctors, pictures of organs, and genetic data. They then tested how well AI models could use all this information to make predictions, and they found that it really helps to be able to look at many different types of information together. |
Keywords
* Artificial intelligence * Question answering
