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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.01520 |
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| _version_ | 1866929333965160448 |
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| author | Chen, Jihua Yuan, Yue Ziabari, Amir Koushyar Xu, Xuan Zhang, Honghai Christakopoulos, Panagiotis Bonnesen, Peter V. Ivanov, Ilia N. Ganesh, Panchapakesan Wang, Chen Jaimes, Karen Patino Yang, Guang Kumar, Rajeev Sumpter, Bobby G. Advincula, Rigoberto |
| author_facet | Chen, Jihua Yuan, Yue Ziabari, Amir Koushyar Xu, Xuan Zhang, Honghai Christakopoulos, Panagiotis Bonnesen, Peter V. Ivanov, Ilia N. Ganesh, Panchapakesan Wang, Chen Jaimes, Karen Patino Yang, Guang Kumar, Rajeev Sumpter, Bobby G. Advincula, Rigoberto |
| contents | Artificial Intelligence (AI) approaches are increasingly being applied to more and more domains of Science, Engineering, Chemistry, and Industries to not only improve efficiencies and enhance productivity, but also enable new capabilities. The new opportunities range from automated molecule design and screening, properties prediction, gaining insights of chemical reactions, to computer-aided design, predictive maintenance of systems, robotics, and autonomous vehicles. This review focuses on the new applications of AI in manufacturing and healthcare. For the Manufacturing Industries, we focus on AI and algorithms for (1) Battery, (2) Flow Chemistry, (3) Additive Manufacturing, (4) Sensors, and (5) Machine Vision. For Healthcare applications, we focus on: (1) Medical Vision (2) Diagnosis, (3) Protein Design, and (4) Drug Discovery. In the end, related topics are discussed, including physics integrated machine learning, model explainability, security, and governance during model deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01520 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI for Manufacturing and Healthcare: a chemistry and engineering perspective Chen, Jihua Yuan, Yue Ziabari, Amir Koushyar Xu, Xuan Zhang, Honghai Christakopoulos, Panagiotis Bonnesen, Peter V. Ivanov, Ilia N. Ganesh, Panchapakesan Wang, Chen Jaimes, Karen Patino Yang, Guang Kumar, Rajeev Sumpter, Bobby G. Advincula, Rigoberto Materials Science Artificial Intelligence (AI) approaches are increasingly being applied to more and more domains of Science, Engineering, Chemistry, and Industries to not only improve efficiencies and enhance productivity, but also enable new capabilities. The new opportunities range from automated molecule design and screening, properties prediction, gaining insights of chemical reactions, to computer-aided design, predictive maintenance of systems, robotics, and autonomous vehicles. This review focuses on the new applications of AI in manufacturing and healthcare. For the Manufacturing Industries, we focus on AI and algorithms for (1) Battery, (2) Flow Chemistry, (3) Additive Manufacturing, (4) Sensors, and (5) Machine Vision. For Healthcare applications, we focus on: (1) Medical Vision (2) Diagnosis, (3) Protein Design, and (4) Drug Discovery. In the end, related topics are discussed, including physics integrated machine learning, model explainability, security, and governance during model deployment. |
| title | AI for Manufacturing and Healthcare: a chemistry and engineering perspective |
| topic | Materials Science |
| url | https://arxiv.org/abs/2405.01520 |