Sepsis remains one of the leading causes of death in intensive care units worldwide — and modern medicine still lacks effective molecular therapies to treat the underlying immune dysfunction. In this talk, Dr. Gary An presents a National Academies of Sciences, Engineering, and Medicine (NASEM)-compliant digital twin designed specifically to control and reverse critical illness through adaptive, personalized immune intervention.

• real-time biological sensing • adaptive cyber-physical systems • deep reinforcement learning • closed-loop decision and intervention frameworks This Critical Illness Digital Twin (CIDT) is not built to merely predict outcomes — it is designed to actively steer patient biology back toward health, using continuous model updating, uncertainty-aware control, and personalized immune modulation strategies. The talk explores why traditional AI, clinical data analytics, and population-based precision medicine approaches fail in critical illness, and why only a true NASEM-compliant digital twin architecture can enable real-time, patient-specific therapy in diseases defined by heterogeneity, uncertainty, and biological complexity. This is a vision of medicine that moves beyond prediction — toward control, adaptation, and cure. If you found this video useful, please check out our other videos on computational modeling, infection and immunology: Please consider joining our IMAG/MSM WG on Multiscale Modeling and Viral Pandemics: https://www.imagwiki.nibib.nih.gov/content/msm-viral-pandemics-meetings Please also consider joining the Global Alliance for Immune Prediction and Intervention: http://glimprint.org/ #digitaltwins , #SepsisResearch, #criticalcare , #computationalimmunology, #precisionmedicine, #medicalai , #systemsbiology , #healthcareinnovation , #aiinmedicine