Explainable AI in Healthcare and Medicine: Building a Culture of Transparency and Accountability
his book highlights the latest advances in the application of artificial intelligence and data science in health care and medicine. Featuring selected papers from the 2020 Health Intelligence Workshop, held as part of the Association for the Advancement of Artificial Intelligence (AAAI) Annual Confe...
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Switzerland
Springer
2021
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Ychwanegu Tag
Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!
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MARC
| LEADER | 00000nam a22000007a 4500 | ||
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| 008 | 241021b |||||||| |||| 00| 0 eng d | ||
| 020 | |a 9783030533519 | ||
| 082 | |a 610 |b NEJ | ||
| 100 | |a Nejad, Arash Shaban |9 10994 | ||
| 245 | |a Explainable AI in Healthcare and Medicine: Building a Culture of Transparency and Accountability | ||
| 260 | |a Switzerland |b Springer |c 2021 | ||
| 300 | |a 344p | ||
| 520 | |a his book highlights the latest advances in the application of artificial intelligence and data science in health care and medicine. Featuring selected papers from the 2020 Health Intelligence Workshop, held as part of the Association for the Advancement of Artificial Intelligence (AAAI) Annual Conference, it offers an overview of the issues, challenges, and opportunities in the field, along with the latest research findings. Discussing a wide range of practical applications, it makes the emerging topics of digital health and explainable AI in health care and medicine accessible to a broad readership. The availability of explainable and interpretable models is a first step toward building a culture of transparency and accountability in health care. As such, this book provides information for scientists, researchers, students, industry professionals, public health agencies, and NGOs interested in the theory and practice of computational models of public and personalized health intelligence. | ||
| 650 | |a Artificial intelligence |a health care |9 10995 | ||
| 700 | |a Michalowski,Martin |a Buckeridge,David L. |9 10996 | ||
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| 999 | |c 8860 |d 8860 | ||