Summary of Health Ai Developer Foundations, by Atilla P. Kiraly et al.
Health AI Developer Foundations
by Atilla P. Kiraly, Sebastien Baur, Kenneth Philbrick, Fereshteh Mahvar, Liron Yatziv, Tiffany Chen, Bram Sterling, Nick George, Fayaz Jamil, Jing Tang, Kai Bailey, Faruk Ahmed, Akshay Goel, Abbi Ward, Lin Yang, Andrew Sellergren, Yossi Matias, Avinatan Hassidim, Shravya Shetty, Daniel Golden, Shekoofeh Azizi, David F. Steiner, Yun Liu, Tim Thelin, Rory Pilgrim, Can Kirmizibayrak
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Image and Video Processing (eess.IV)
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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 research introduces Health AI Developer Foundations (HAI-DEF), a suite of pre-trained, domain-specific foundation models, tools, and recipes designed to accelerate the development of medical machine learning (ML) models for health applications. The models cover various modalities and domains, including radiology, histopathology, dermatological imaging, and audio, providing domain-specific embeddings that facilitate AI development with reduced labeled data, shorter training times, and lower computational costs. The paper presents model evaluations across various tasks, emphasizing the importance of ensuring efficacy, fairness, and equity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Robust medical machine learning models can revolutionize healthcare by accelerating clinical research and improving outcomes. To make this possible, researchers developed a set of pre-trained foundation models called Health AI Developer Foundations (HAI-DEF). These models are designed to help developers build better medical AI faster and more efficiently. The models cover different types of data, such as X-rays, images of skin, and audio recordings. They provide special connections that make it easier to develop new AI applications with less labeled data and shorter training times. |
Keywords
* Artificial intelligence * Machine learning
