Health AI Developer Foundations
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arXiv
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| Format: | Preprint |
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2024
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| author | Kiraly, Atilla P. Baur, Sebastien Philbrick, Kenneth Mahvar, Fereshteh Yatziv, Liron Chen, Tiffany Sterling, Bram George, Nick Jamil, Fayaz Tang, Jing Bailey, Kai Ahmed, Faruk Goel, Akshay Ward, Abbi Yang, Lin Sellergren, Andrew Matias, Yossi Hassidim, Avinatan Shetty, Shravya Golden, Daniel Azizi, Shekoofeh Steiner, David F. Liu, Yun Thelin, Tim Pilgrim, Rory Kirmizibayrak, Can |
| author_facet | Kiraly, Atilla P. Baur, Sebastien Philbrick, Kenneth Mahvar, Fereshteh Yatziv, Liron Chen, Tiffany Sterling, Bram George, Nick Jamil, Fayaz Tang, Jing Bailey, Kai Ahmed, Faruk Goel, Akshay Ward, Abbi Yang, Lin Sellergren, Andrew Matias, Yossi Hassidim, Avinatan Shetty, Shravya Golden, Daniel Azizi, Shekoofeh Steiner, David F. Liu, Yun Thelin, Tim Pilgrim, Rory Kirmizibayrak, Can |
| contents | Robust medical Machine Learning (ML) models have the potential to revolutionize healthcare by accelerating clinical research, improving workflows and outcomes, and producing novel insights or capabilities. Developing such ML models from scratch is cost prohibitive and requires substantial compute, data, and time (e.g., expert labeling). To address these challenges, we introduce Health AI Developer Foundations (HAI-DEF), a suite of pre-trained, domain-specific foundation models, tools, and recipes to accelerate building ML for health applications. The models cover various modalities and domains, including radiology (X-rays and computed tomography), histopathology, dermatological imaging, and audio. These models provide domain specific embeddings that facilitate AI development with less labeled data, shorter training times, and reduced computational costs compared to traditional approaches. In addition, we utilize a common interface and style across these models, and prioritize usability to enable developers to integrate HAI-DEF efficiently. We present model evaluations across various tasks and conclude with a discussion of their application and evaluation, covering the importance of ensuring efficacy, fairness, and equity. Finally, while HAI-DEF and specifically the foundation models lower the barrier to entry for ML in healthcare, we emphasize the importance of validation with problem- and population-specific data for each desired usage setting. This technical report will be updated over time as more modalities and features are added. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15128 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Health AI Developer Foundations Kiraly, Atilla P. Baur, Sebastien Philbrick, Kenneth Mahvar, Fereshteh Yatziv, Liron Chen, Tiffany Sterling, Bram George, Nick Jamil, Fayaz Tang, Jing Bailey, Kai Ahmed, Faruk Goel, Akshay Ward, Abbi Yang, Lin Sellergren, Andrew Matias, Yossi Hassidim, Avinatan Shetty, Shravya Golden, Daniel Azizi, Shekoofeh Steiner, David F. Liu, Yun Thelin, Tim Pilgrim, Rory Kirmizibayrak, Can Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Multimedia Image and Video Processing Robust medical Machine Learning (ML) models have the potential to revolutionize healthcare by accelerating clinical research, improving workflows and outcomes, and producing novel insights or capabilities. Developing such ML models from scratch is cost prohibitive and requires substantial compute, data, and time (e.g., expert labeling). To address these challenges, we introduce Health AI Developer Foundations (HAI-DEF), a suite of pre-trained, domain-specific foundation models, tools, and recipes to accelerate building ML for health applications. The models cover various modalities and domains, including radiology (X-rays and computed tomography), histopathology, dermatological imaging, and audio. These models provide domain specific embeddings that facilitate AI development with less labeled data, shorter training times, and reduced computational costs compared to traditional approaches. In addition, we utilize a common interface and style across these models, and prioritize usability to enable developers to integrate HAI-DEF efficiently. We present model evaluations across various tasks and conclude with a discussion of their application and evaluation, covering the importance of ensuring efficacy, fairness, and equity. Finally, while HAI-DEF and specifically the foundation models lower the barrier to entry for ML in healthcare, we emphasize the importance of validation with problem- and population-specific data for each desired usage setting. This technical report will be updated over time as more modalities and features are added. |
| title | Health AI Developer Foundations |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Multimedia Image and Video Processing |
| url | https://arxiv.org/abs/2411.15128 |