AI for Health provides data-driven tools for healthcare:

  • Phenotype complex diseases.
  • Predict clinical outcomes and treatment effects.
  • Discover risk factors associated with clinical outcomes.
  • Support clinical decisions and precision medicine.

Research underway at Washington University in St. Louis

$5 million NIH grant to find causes of chronic pain after surgery

$5 million NIH grant to find causes of chronic pain after surgery

Study may lead to personalized prevention, management tools for post-operative pain.

Analyzing multiple mammograms improves breast cancer risk prediction

Analyzing multiple mammograms improves breast cancer risk prediction

AI method spots subtle changes over time, enhances accuracy of determining five-year risk

Researchers define new subtypes of common brain disorder

Researchers define new subtypes of common brain disorder

Artificial intelligence identified 3 subtypes of Chiari type-1 malformations, could improve medical decision making

Deep learning models can be trained with limited data

Deep learning models can be trained with limited data

Ulugbek Kamilov, graduate students, develop method that could reduce errors in computational imaging

DEMIST artificial intelligence tool may enhance usability of medical images

DEMIST artificial intelligence tool may enhance usability of medical images

A deep-learning-based image denoising method developed by Abhinav Jha may improve detection of myocardial defects in low-count SPECT scans

New machine learning method can better predict spine surgery outcomes

New machine learning method can better predict spine surgery outcomes

Researchers who had been using Fitbit data to help predict surgical outcomes have a new method to more accurately gauge how patients may recover from spine surgery.

Talk: AI for Health with Wearables

Talk: AI for Health with Wearables

Chenyang Lu to deliver keynote speech at CPS-IoT Week, the premier event on cyber-physical systems and Internet of Things.

AI-assisted breast-cancer screening may reduce unnecessary testing

AI-assisted breast-cancer screening may reduce unnecessary testing

Using AI to help doctors read mammograms reduces follow-up testing without missing cancer cases, simulation shows

AI-based algorithms need to be evaluated for their clinical applications

AI-based algorithms need to be evaluated for their clinical applications

Abhinav Jha-led research focuses on developing algorithms to evaluate nuclear medicine images for clinical applications, such as cancer diagnosis and treatment

Recent talks