Digital twins as global learning health and disease models for preventive and personalized medicine

Genome Medicine, Journal Year: 2025, Volume and Issue: 17(1)

Published: Feb. 7, 2025

Abstract Ineffective medication is a major healthcare problem causing significant patient suffering and economic costs. This issue stems from the complex nature of diseases, which involve altered interactions among thousands genes across multiple cell types organs. Disease progression can vary between patients over time, influenced by genetic environmental factors. To address this challenge, digital twins have emerged as promising approach, led to international initiatives aiming at clinical implementations. Digital are virtual representations health disease processes that integrate real-time data simulations predict, prevent, personalize treatments. Early applications DTs shown potential in areas like artificial organs, cancer, cardiology, hospital workflow optimization. However, widespread implementation faces several challenges: (1) characterizing dynamic molecular changes biological scales; (2) developing computational methods into DTs; (3) prioritizing mechanisms therapeutic targets; (4) creating interoperable DT systems learn each other; (5) designing user-friendly interfaces for clinicians; (6) scaling technology globally equitable access; (7) addressing ethical, regulatory, financial considerations. Overcoming these hurdles could pave way more predictive, preventive, personalized medicine, potentially transforming delivery improving outcomes.

Language: Английский

From screens to scenes: A survey of embodied AI in healthcare

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103033 - 103033

Published: Feb. 1, 2025

Language: Английский

Intelligent control of robotic X-ray devices using a language-promptable digital twin

International Journal of Computer Assisted Radiology and Surgery, Journal Year: 2025, Volume and Issue: unknown

Published: April 9, 2025

Language: Английский