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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.00795 |
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| _version_ | 1866913089580957696 |
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| author | Shokrollahi, Yasin Colmenarez, Jose Liu, Wenxi Yarmohammadtoosky, Sahar Nikahd, Matthew M. Dong, Pengfei Li, Xianqi Gu, Linxia |
| author_facet | Shokrollahi, Yasin Colmenarez, Jose Liu, Wenxi Yarmohammadtoosky, Sahar Nikahd, Matthew M. Dong, Pengfei Li, Xianqi Gu, Linxia |
| contents | The rapid advancement of Artificial Intelligence (AI) has catalyzed revolutionary changes across various sectors, notably in healthcare. In particular, generative AI-led by diffusion models and transformer architectures-has enabled significant breakthroughs in medical imaging (including image reconstruction, image-to-image translation, generation, and classification), protein structure prediction, clinical documentation, diagnostic assistance, radiology interpretation, clinical decision support, medical coding, and billing, as well as drug design and molecular representation. These innovations have enhanced clinical diagnosis, data reconstruction, and drug synthesis. This review paper aims to offer a comprehensive synthesis of recent advances in healthcare applications of generative AI, with an emphasis on diffusion and transformer models. Moreover, we discuss current capabilities, highlight existing limitations, and outline promising research directions to address emerging challenges. Serving as both a reference for researchers and a guide for practitioners, this work offers an integrated view of the state of the art, its impact on healthcare, and its future potential. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_00795 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Recent Advances in Generative AI for Healthcare Applications Shokrollahi, Yasin Colmenarez, Jose Liu, Wenxi Yarmohammadtoosky, Sahar Nikahd, Matthew M. Dong, Pengfei Li, Xianqi Gu, Linxia Machine Learning Artificial Intelligence The rapid advancement of Artificial Intelligence (AI) has catalyzed revolutionary changes across various sectors, notably in healthcare. In particular, generative AI-led by diffusion models and transformer architectures-has enabled significant breakthroughs in medical imaging (including image reconstruction, image-to-image translation, generation, and classification), protein structure prediction, clinical documentation, diagnostic assistance, radiology interpretation, clinical decision support, medical coding, and billing, as well as drug design and molecular representation. These innovations have enhanced clinical diagnosis, data reconstruction, and drug synthesis. This review paper aims to offer a comprehensive synthesis of recent advances in healthcare applications of generative AI, with an emphasis on diffusion and transformer models. Moreover, we discuss current capabilities, highlight existing limitations, and outline promising research directions to address emerging challenges. Serving as both a reference for researchers and a guide for practitioners, this work offers an integrated view of the state of the art, its impact on healthcare, and its future potential. |
| title | Recent Advances in Generative AI for Healthcare Applications |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2310.00795 |