Program for Clinical AI

Advancing the science, adoption, and effective use of artificial intelligence in health care

We develop, validate, and implement artificial intelligence (AI) tools to improve healthcare delivery. Like other areas of our lives, the role of AI in healthcare delivery will only increase over time. A key objective of the Program for Clinical AI (P4AI) is to investigate AI-enabled tools in real-world settings including monitoring AI model performance for drift, equity, and fitness for answering questions across settings and subpopulations. The P4AI team includes experts with a wide range of expertise across the University of Minnesota including the Medical School, School of Public Health, Institute for Health Informatics, and the College of Science and Engineering. We foster both national and international collaborations in natural language processing (NLP) and computer vision including the Federated Computer Vision in Healthcare Collaborative across multiple U.S. academic health systems.

P4AI People

P4AI Leadership

  • Scientific co-Director, Christopher Tignanelli, MD, MS
  • Scientific co-Director, Gyorgy Simon, PhD

P4AI Full Members

  • Structured Healthcare Data
    •  Gyorgy Simon, PhD
  • Computer Vision
    • Ju Sun, PhD
  • Natural Language Processing
    • Rui Zhang, PhD
  • Robotics and Surgical Automation
    • Elliot Arsoniadis, MD, PhD
  • Project Manager
    • Molly Diethelm, PMP

USE CASES

Ability of Artificial Intelligence to Identify Self-Reported Race in Chest X-Ray Using Pixel Intensity Counts Re-Aiming Equity Evaluation in Clinical Decision Support: A Scoping Review of Equity Assessments in Surgical Decision Support Systems
Semi-automated Clinical Content Curation of COVID-19 Chatbot Remote Patient Monitoring Solution A Novel, Evidence-Based, Comprehensive Clinical Decision Support System Improves Outcomes for Patients with Traumatic Rib Fractures
Artificial Intelligence-enabled Decision Support in Surgery: State-of-the-art and Future Directions Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study
Patient Heterogeneity & the J-Curve Relationship – Time-to-Antibiotics & Outcomes of Patients Admitted with Bacterial Infection Improved Prediction of Older Adult Discharge After Trauma Using a Novel Machine Learning Paradigm
12-hospital prospective evaluation of CDS prognostic algorithm to facilitate decision making for patients with suspected COVID19 Need for Emergent Intervention within 6 Hours: A Novel Prediction Model for Hospital Trauma Triage Heterogeneity in COVID-19 Patients at Multiple Levels of Granularity: From Biclusters to Clinical Interventions
A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification NLP of prehospital EMS trauma records allows for automated characterization of treatment appropriateness
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