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, MHA, PMP
Featured Use Cases
2023
Re-Aiming Equity Evaluation in Clinical Decision Support: A Scoping Review of Equity Assessments in Surgical Decision Support Systems
Nicholas Ingraham, Emma Jones, Samantha King, James Dries, Michael Phillips, Tyler Loftus, Heather Evans, Genevieve Melton, Chris Tignanelli
Artificial Intelligence-enabled Decision Support in Surgery: State-of-the-art and Future Directions
Tyler Loftus, Maria Altieri, Jeremy Balch, Kenneth Abbott, Jeff Choi, Jayson Marwaha, Daniel Hashimoto, Gabriel Brat, Yannis Raftopoulos, Heather Evans, Gretchen Jackson, Danielle Walsh, Chris Tignanelli
Validation of a Proprietary Deterioration Index Model and Performance in Hospitalized Adults
Thomas Byrd, Bronwyn Southwell, Adarsh Ravishankar, Travis Tran, Abhinab Kc, Tom Phelan, Genevieve Melton-Meaux, Michael Usher, Daren Scheppmann, Sean Switzer, Gyorgy Simon, Chris Tignanelli
A novel, evidence-based, comprehensive clinical decision support system improves outcomes for patients with traumatic rib fractures
Emma Jones, Ivana Ninkovic, Matthew Bahr, Sarah Dodge, Michael Doering, David Martin, Julie Ottosen, Tadashi Allen, Genevieve Melton, Chris Tignanelli
Ability of artificial intelligence to identify self-reported race in chest x-ray using pixel intensity counts
John Lee Burns, Zachary Zaiman, Jack Vanschaik, Gaoxiang Luo, Le Peng, Brandon Price, Garric Mathias, Vijay Mittal, Akshay Sagane, Chris Tignanelli, Sunandan Chakraborty, Judy W. Gichoya, Saptarshi Purkayastha
2022
A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19
Monica Lupei, Danni Li, Nicholas Ingraham, Karyn Baum, Bradley Benson, Michael Puskarich, David Milbrandt, Genevieve Melton, Daren Scheppmann, Michael Usher, Chris Tignanelli
Improved Prediction of Older Adult Discharge After Trauma Using a Novel Machine Learning Paradigm
Rachel Morris, Chris Tignanelli, Terri deRoon-Cassini, Purushottam Laud, Rodney Sparapani
Patient Heterogeneity and the J-Curve Relationship Between Time-to-Antibiotics and the Outcomes of Patients Admitted With Bacterial Infection
Michael Usher, Roshan Tourani, Ben Webber, Chris Tignanelli, Sisi Ma, Lisiane Pruinelli, Michael Rhodes, Nishant Sahni, Andrew Olson, Genevieve Melton, Gyorgy Simon
Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study
Ju Sun, Le Peng, Taihui Li, Dyah Adila, Zach Zaiman, Genevieve Melton-Meaux, Nicholas Ingraham, Eric Murray, Daniel Boley, Sean Switzer, John Burns, Kun Huang, Tadashi Allen, Scott Steenburg, Judy Wawira Gichoya, Erich Kummerfeld, Chris Tignanelli
Need for Emergent Intervention within 6 Hours: A Novel Prediction Model for Hospital Trauma Triage
Rachel Morris, Basil Karam, Emily Zolfaghari, Benjamin Chen, Thomas Kirsh, Roshan Tourani, David Milia, Lena Napolitano, Marc de Moya, Marc Conterato, Constantin Aliferis, Sisi Ma, Chris Tignanelli
Semi-automated Clinical Content Curation of COVID-19 Chatbot Remote Patient Monitoring Solution
Tanya Melnik, Joshua Thompson, Jake Vasilakes, Tucker Annis, Sicheng Zhou, Dalton Schutte, Genevieve Melton, Susan Pleasants, Rui Zhang
2021
A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification
Himanshu Sahoo, Greg Silverman, Nicholas Ingraham, Monica Lupei, Michael Puskarich, Raymond Finzel, John Sartori, Rui Zhang, Benjamin Knoll, Sijia Liu, Hongfang Liu, Genevieve Melton, Christopher Tignanelli, Serguei Pakhomov
Heterogeneity in COVID-19 Patients at Multiple Levels of Granularity: From Biclusters to Clinical Interventions
Suresh Bhavnani, Erich Kummerfeld, Weibin Zhang, Yong-Fang Kuo, Nisha Garg, Shyam Visweswaran, Mukaila Raji, Ravi Radhakrishnan, Georgiy Golvoko, Sandra Hatch, Michael Usher, Genevieve Melton-Meaux, Chris Tignanelli
2020
Natural language processing of prehospital emergency medical services trauma records allows for automated characterization of treatment appropriateness
Chris Tignanelli, Greg Silverman, Elizabeth Lindemann, Alexander Trembley, Jon Gipson, Gregory Beilman, John Lyng, Raymond Finzel, Reed McEwan, Benjamin Knoll, Serguei Pakhomov, Genevieve Melton
P4AI Recent News
CLHSS Team Creates Model That Effectively Predicts Sepsis. A team from CLHSS led by Drs. Mike Usher and Chris Tignanelli created a model that effectively predicts sepsis and identifies which patients would benefit from early antibiotics, leading to better patient outcomes.
Funded Projects
ENTRUST AI
This project is aimed at developing computational approaches to track the reliability of the clinical AI predictions and predict patient-specific harms and benefits from interventions.