CompHealth Corner May 2026
Computational and digital health ideas evolve quickly. Our monthly research roundup highlights the latest publications — from our Center faculty and other leaders in the field. Here’s what you should be reading this month!
Digital Twins and Digital Models of the Human Circulatory System
Wu, Runxin, Guinevere Ferreira, Nusrat Sadia Khan, Samreen T. Mahmud, Jorik Stoop, Lydia L. Sohn, Jane A. Leopold, and Amanda Randles. “Digital twins and digital models of the human circulatory system.” Nature Reviews Bioengineering(2026): 1-20.
Summary
How can we better understand and treat complex cardiovascular diseases?
A new review in Nature Portfolio Nature Reviews Bioengineering explores the emerging role of digital twins in modeling the human circulatory system, highlighting both the promise and the challenges of translating digital twins into precision medicine tools.
These dynamic, data-driven models integrate clinical data, physics-based simulations, and AI to represent biological processes across scales, from whole-body blood flow to cellular interactions.
Digital twins have the potential to support disease diagnosis, risk prediction, surgical planning, and personalized treatment. As advances in sensors, imaging, and high performance computing continue, these models are moving closer to real-time clinical applications.
State and Diffusion of National Institutes of Health Funding of AI in Radiology
Jabal, Mohamed Sobhi, Miriam Chisholm, Vikash Gupta, Barbaros Selnur Erdal, David Kallmes, Waleed Brinjikji, Mustafa Bashir, Evan Calabrese, and Kirti Magudia. “State and Diffusion of National Institutes of Health Funding of AI in Radiology.” Journal of Imaging Informatics in Medicine (2026): 1-13.
Summary
New research from CCDHI member Evan Calabrese and colleagues, published in the Journal of Imaging Informatics in Medicine, examined how NIH funding for AI in radiology has evolved over the past decade. NIH support increased from $46.4M in 2015 to $633.5M in 2024, with AI projects now accounting for more than 30% of radiology-related funding.
Growth followed an exponential trajectory, doubling approximately every three years. Deep learning applications, MRI, neurology, and oncology emerged as leading areas of focus, highlighting how AI is becoming a foundational component of radiology research rather than a niche area of innovation.
Impact of Daylight Saving Time on Physical Activity Patterns
Jeong, Hayoung, Srikar Katta, Will Ke Wang, Alexander Volfovsky, and Jessilyn Dunn. “Impact of daylight saving time on physical activity patterns.” Nature Health (2026): 1-8.
Summary
Does daylight saving time actually change our behavior?
A new study from the lab of Jessilyn Dunn, Associate Director for Wearables at the Center for Computational and Digital Health Innovation, used large-scale Fitbit data from the All of Us Research Program to examine how daylight saving time affects physical activity patterns was recently published in Nature Portfolio‘s Nature Health.
Interestingly, the study found no overall change in total daily steps. Instead, activity shifted within the day. Fall transitions increased morning activity and decreased evening activity, while spring transitions showed the opposite pattern.
The effects also varied across individuals and demographic groups, suggesting that work schedules and daily routines shape how people adapt to time changes.
Beyond daylight saving time itself, the study highlights how wearable data can help us better understand the impact of public policies and environmental changes on health and behavior.
