What Will It Take to Revolutionize Healthcare?
Every day, patients and clinicians grapple with complex diseases that defy simple solutions. We’re reshaping healthcare by using data-driven models to deliver precise diagnoses, personalized treatments and faster cures–moving beyond traditional care to transform outcomes for patients everywhere.
Driving Discovery: Projects in Computational and Systems Medicine
How AI Is Creating “Smart” Skin Images to Advance Early Detection of Skin Cancer
A new artificial intelligence technique is helping fill a critical gap in skin health: not enough images of different skin conditions to train machine learning systems. Their method uses powerful new “diffusion models”—the same technology behind realistic AI-generated art—to automatically create and refine synthetic skin lesion images when real images are scarce or hard to obtain. By smartly “inpainting” and blending realistic details into existing images, this approach teaches diagnostic algorithms to better recognize skin cancers and other diseases, even for rare cases. With improved accuracy and fairness, this technology could lead to more reliable, faster, and equitable skin disease diagnosis for everyone.
Learn MoreFaculty List
Ashutosh Agrawal, PhD
Texas A&M School of Engineering Medicine
Chandler Benjamin, PhD
Texas A&M School of Engineering Medicine
Yanmin Gong, PhD
Texas A&M School of Engineering Medicine
Raksha Raghunathan, PhD
Houston Methodist
Gregory Reeves, PhD
Texas A&M College of EngineeringPublications and Grants
Our interdisciplinary research combines clinical knowledge with computational and systems-based approaches to advance medicine. We are committed to developing impactful solutions for patients and contributing to progress across the healthcare community.
Staying in treatment is key for people recovering from opioid addiction, but many patients leave their medication programs too soon—putting them at risk for relapse and overdose. This research project uses advanced, explainable machine learning to predict which patients are most likely to drop out, tailoring predictions to each state’s unique challenges. By analyzing patterns in healthcare data and highlighting important risk factors for each region, the project aims to help doctors and clinics better support patients, improve retention rates, and guide policies to address the opioid crisis in local communities.
Bioinspired Shells Offer Next-Level Strength for Lightweight Structures
Ashutosh Agrawal, PhDInspired by the protective envelope around our cells’ DNA, scientists have designed a new type of shell structure called “torene shells.” These structures use a unique pattern of curved layers connected by donut-shaped holes, mimicking nature’s design. Computer simulations show that torene shells can be much stiffer and stronger than traditional shells, even when using the same amount of material. This breakthrough could lead to safer, lighter materials for buildings, vehicles, and equipment used in extreme environments.
Unlocking a Better Way to Predict Aortic Rupture: How Tissue Density Reveals Strength
Chandler Benjamin, PhDcientists have found that the density of aortic tissue directly affects its stiffness and strength. In this study, they treated aortic samples with enzymes that break down key proteins, then measured how the tissue’s density and ability to withstand stretching changed. The less dense the tissue became, the weaker and less stiff it was. This breakthrough suggests that doctors could one day use tissue density measurements to non-invasively estimate the strength of a patient’s aorta, potentially leading to better ways to monitor and prevent life-threatening aortic ruptures and dissections.