Machine Learning–Enabled Digital Twins for Diagnostic and Therapeutic Purposes

Abstract

Digital twins offer virtual representations of patients by integrating diverse data modalities to enable personalized diagnostics and treatments. This chapter explores augmenting patient digital twins with machine learning for enhanced clinical decision support. Beginning with the fundamental concepts around digital twin technology and machine learning techniques, the discussion ranges to the discussion of state-of-the-art digital twins and machine learning models used in the field of diagnostic and therapeutic. Fusing high-fidelity digital profiling with complex pattern recognition using machine neural networks establishes a powerful platform for data-driven precision medicine. This synergistic approach allows for gaining a comprehensive understanding of individual patients for granular risk assessment. Personalized digital twins equipped with machine learning additionally enable the recommendation of optimal therapeutic interventions tailored to the specific needs of each patient. Combining multipartite patient simulations with artificial intelligence offers the next paradigm for preventative and participatory medicine centered around the individual. The immense promise along with challenges and opportunities are covered to provide a holistic perspective on this emerging interdisciplinary technology converging human medicine, virtual modeling, and artificial intelligence.

Ähnliche Arbeiten