A Comprehensive Review of Generative AI in Healthcare
The advancement of Artificial Intelligence (AI) has catalyzed revolutionary changes across various sectors, notably in healthcare. Among the significant developments in this field are the applications of generative AI models, specifically transformers and diffusion models. These models have played a...
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| creator | Shokrollahi, Yasin Yarmohammadtoosky, Sahar Nikahd, Matthew M Dong, Pengfei Li, Xianqi Gu, Linxia |
| description | The advancement of Artificial Intelligence (AI) has catalyzed revolutionary
changes across various sectors, notably in healthcare. Among the significant
developments in this field are the applications of generative AI models,
specifically transformers and diffusion models. These models have played a
crucial role in analyzing diverse forms of data, including medical imaging
(encompassing image reconstruction, image-to-image translation, image
generation, and image 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. Such applications have enhanced clinical diagnosis,
data reconstruction, and drug synthesis. This review paper aims to offer a
thorough overview of the generative AI applications in healthcare, focusing on
transformers and diffusion models. Additionally, we propose potential
directions for future research to tackle the existing limitations and meet the
evolving demands of the healthcare sector. Intended to serve as a comprehensive
guide for researchers and practitioners interested in the healthcare
applications of generative AI, this review provides valuable insights into the
current state of the art, challenges faced, and prospective future directions. |
| doi_str_mv | 10.48550/arxiv.2310.00795 |
| format | Article |
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changes across various sectors, notably in healthcare. Among the significant
developments in this field are the applications of generative AI models,
specifically transformers and diffusion models. These models have played a
crucial role in analyzing diverse forms of data, including medical imaging
(encompassing image reconstruction, image-to-image translation, image
generation, and image 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. Such applications have enhanced clinical diagnosis,
data reconstruction, and drug synthesis. This review paper aims to offer a
thorough overview of the generative AI applications in healthcare, focusing on
transformers and diffusion models. Additionally, we propose potential
directions for future research to tackle the existing limitations and meet the
evolving demands of the healthcare sector. Intended to serve as a comprehensive
guide for researchers and practitioners interested in the healthcare
applications of generative AI, this review provides valuable insights into the
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changes across various sectors, notably in healthcare. Among the significant
developments in this field are the applications of generative AI models,
specifically transformers and diffusion models. These models have played a
crucial role in analyzing diverse forms of data, including medical imaging
(encompassing image reconstruction, image-to-image translation, image
generation, and image 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. Such applications have enhanced clinical diagnosis,
data reconstruction, and drug synthesis. This review paper aims to offer a
thorough overview of the generative AI applications in healthcare, focusing on
transformers and diffusion models. Additionally, we propose potential
directions for future research to tackle the existing limitations and meet the
evolving demands of the healthcare sector. Intended to serve as a comprehensive
guide for researchers and practitioners interested in the healthcare
applications of generative AI, this review provides valuable insights into the
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changes across various sectors, notably in healthcare. Among the significant
developments in this field are the applications of generative AI models,
specifically transformers and diffusion models. These models have played a
crucial role in analyzing diverse forms of data, including medical imaging
(encompassing image reconstruction, image-to-image translation, image
generation, and image 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. Such applications have enhanced clinical diagnosis,
data reconstruction, and drug synthesis. This review paper aims to offer a
thorough overview of the generative AI applications in healthcare, focusing on
transformers and diffusion models. Additionally, we propose potential
directions for future research to tackle the existing limitations and meet the
evolving demands of the healthcare sector. Intended to serve as a comprehensive
guide for researchers and practitioners interested in the healthcare
applications of generative AI, this review provides valuable insights into the
current state of the art, challenges faced, and prospective future directions.</abstract><doi>10.48550/arxiv.2310.00795</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Learning |
| title | A Comprehensive Review of Generative AI in Healthcare |
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