Document Type
Editorial
Publication Date
12-23-2024
Abstract
With rapid developments in medical sensing and imaging, we now live in an era of data explosion in which large amounts of data are readily available in clinical environments. The fast-growing biomedical and healthcare data provide unprecedented opportunities for data-driven scientific knowledge discovery and clinical decision support. Our Research Topic aims to catalyze synergies among biomedical informatics, machine learning, computer simulation, operations research, systems engineering, and other related fields with three specific goals: (1) develop cutting-edge data-driven models to accelerate scientific knowledge discovery in biomedicine using healthcare data collected from laboratory systems, imaging systems, and medical and sensing devices; (2) develop advanced simulation and calibration algorithms to build personalized digital twins by effectively assimilating patient-specific medical data with population-level computer models, facilitating precision medical planning; (3) develop innovative optimization algorithms for optimal medical decision making in the face of uncertainty factors, conflicting objectives, and complex trade-offs. This Research Topic, containing 10 articles, will offer a timely collection of information to benefit researchers and practitioners working in the broad fields of biomedical informatics, healthcare data analytics, medical image processing, and health-related AI.
DOI
10.3389/fphys.2024.1541057
Source Publication
Frontiers in Physiology
Recommended Citation
Yao B, Gaw N and Lee HK (2024) Editorial: Artificial intelligence for smart health: learning, simulation, and optimization. Front. Physiol. 15:1541057. doi: 10.3389/fphys.2024.1541057
Comments
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