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.
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This chapter explores the potential of applying AI and data analytics
to transform manufacturing. It provides an overview of new research
trends in smart manufacturing, including the use of IoT, big data, and
advanced AI technologies like machine learning and digital twins. The
conceptual background of relevant AI approaches is discussed,
including deep learning, reinforcement learning, unsupervised
learning, and state-of-the-art models. A key focus is examining the
role of AI in predictive maintenance through data-driven techniques
for remaining useful life estimation, anomaly detection, prognostics,
and optimizing maintenance strategies. Challenges and limitations such
as noisy data, imbalanced datasets, and high computational
requirements are addressed. The opportunities enabled by AI in
manufacturing are highlighted, spanning synthetic data generation,
real-time prediction, and enhancing asset utilization. The chapter
concludes that transformative gains in productivity, sustainability,
and resilience will arise from thoughtfully leveraging AI and data to
inform decision-making in industrial settings. Adoption remains in the
early stages, and realizing the full potential will require
interdisciplinary collaboration and purposeful innovation.
Digital twin and large language model technologies have been
increasingly applied in precision healthcare and patient applications
in recent years. This publication fills the research gap by providing
an overview of the recent advances, applications, and challenges of
digital twins and large language models in precision healthcare. It
also proposes a state-of-the-art technology that combines a large
language model and a digital twin that can be used to create models
specific to patients to help with diagnosis, treatment planning,
therapy planning, checking the effectiveness of drugs on individuals,
and many other cases. And with this proposed technology, the
healthcare and pharmaceutical industries can be revolutionized.