Generative AI in Healthcare
Springer International Publishing (Verlag)
978-3-032-11998-8 (ISBN)
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This volume brings together cutting-edge research at the intersection of artificial intelligence, clinical care, and public health. While it highlights the impact of generative AI, including large language models, it also delves into broader challenges such as fairness, robustness, scalability, and explainability.
Chapters explore:
Applications of Generative AI in healthcare and medicine
Strategies to reduce bias and improve equity in clinical AI
Dr. Arash Shaban-Nejad is a leading expert in population and precision health informatics. He is currently an Associate Professor and Director of Population and Precision Health at the UTHSC-Oak Ridge National Laboratory Center for Biomedical Informatics, Department of Pediatrics, University of Tennessee Health Science Center. Dr. Shaban-Nejad received his PhD and MSc in Computer Science from Concordia University, Canada, and an MPH from UC Berkeley, along with post-doctoral training at McGill University and additional training at Harvard School of Public Health. His interdisciplinary research focuses on health AI, explainable AI, large language models, predictive modeling, epidemiologic surveillance, and causal reasoning. He has published extensively, and his research has been funded by several federal, state, and international funding agencies. Dr. Shaban-Nejad is a founding chair of the AAAI International Workshop on Health Intelligence and the lead editor of multiple book volumes on the applications of AI in healthcare and medicine.
Dr. Martin Michalowski is a Foundation Research Professor in the School of Nursing at the University of Minnesota, where he co-directs the Center for Nursing Informatics and the Digital Health Lab. With a PhD in Computer Science from the University of Southern California, he applies advanced AI techniques, ranging from heuristic planning to large language models, in nursing informatics and personalized medicine. He co-leads the Nursing and AI Leadership Collaborative and the Mobile Emergency Triage (MET) research group, and has been recognized as a Fellow of the American Medical Informatics Association and the International Academy of Health Sciences Informatics. With more than 100 publications and as a founding chair of the International Workshop on Health Intelligence and co-chair of the AIME society, Dr. Michalowski has helped shape global dialogue on digital health innovation. His work is funded by NIH, NSF, DARPA, and DoD, and has led to award-winning papers and startup ventures.
1. Robustness, Equity, Scalability, and the Challenge of Explainability in the Use of Foundation Models in Medicine.- 2. Uncertainty Quantification of Deep Learning Models for Audio-driven Disease Diagnosis.- 3. Swin fMRI Transformer Predicts Early Neurodevelopmental Outcomes from Neonatal fMRI.- 4. Probabilistic Forecasting of U.S. County-Level Suicide Mortality Rates (2005 2020): Assessing the Impact of Social Determinants of Health.- 5. AI-SAM: Automatic and Interactive Segment Anything Model.
| Erscheint lt. Verlag | 1.3.2026 |
|---|---|
| Reihe/Serie | Studies in Computational Intelligence |
| Zusatzinfo | X, 415 p. 99 illus., 89 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Medizin / Pharmazie ► Physiotherapie / Ergotherapie ► Orthopädie | |
| Technik | |
| Schlagworte | AI and Health Equity • Big Data • Clinical Intelligence • Digital Medicine • generative AI • Health Informatics • Health Intelligence • Large Language Models (LLMs) • Medical Informatics • Precision Health • Precision medicine • predictive analytics |
| ISBN-10 | 3-032-11998-7 / 3032119987 |
| ISBN-13 | 978-3-032-11998-8 / 9783032119988 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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