Full Description
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
Tools for making model predictions more explainable and accountable
Approaches for real-world deployment at scale
Human-centered and governance frameworks for responsible AI
Rather than focusing on isolated use cases or technical performance alone, this book offers a systems-level perspective, bridging computational innovation with clinical and ethical relevance.
Designed for researchers, healthcare professionals, and innovators, this collection provides critical insights for anyone aiming to responsibly develop or implement AI in health contexts.
Contents
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.
