Pitt Health + Explainable AI (Pitt HexAI) Research Laboratory

Building Explainable, Safe, Fair, and Responsible AI in Healthcare Systems

Making Healthcare Better Through the Power of Explainable AI

Augmenting AI Explainability, Interpretability, and Accountability in Healthcare

Exploring How Explainable AI Impacts Our Healthcare Community

Understanding How Explainable AI Shapes Novel Healthcare Systems

Examining How to Handle Bias Issues in AI-Powered Healthcare

Sharing Best Practices in Explainable AI and Interpretable Models

Evaluating and Generalizing Interpretable AI Models in Healthcare

Ahmad P. Tafti, PhD


Ahmad P. Tafti, PhD, FAMIA

Director, Pitt HexAI Research Laboratory
Interim Director of Scientific Affairs, CPACE
Director, HexAI Podcast
AI Lead, Youki GmbH

The Pitt Health + Explainable AI (Pitt HexAI) Research Laboratory at the University of Pittsburgh, led by Ahmad P. Tafti, has a simple goal: to make healthcare better through the power of explainable artificial intelligence (AI). From computational perspective, our research focuses on engineering, implementing, validating, and deploying cutting-edge fundamental and applied explainable AI algorithms, and promote their applications in healthcare problems. We attempt to answer three main questions: 1) how advanced explainable AI strategies are developing in healthcare, 2) how they shape healthcare systems, and 3) how they impact the healthcare community. From clinical perspective, our research agenda spans musculoskeletal diseases and disorders (e.g., TJA), computational orthopedics, and computational pathology.

The Pitt HexAI Research Laboratory is functioning in the Department of Health Information Management at the School of Health and Rehabilitation Sciences. Our group at Pitt HexAI is a multidisciplinary team, and it benefits from several extramural collaborations with other academic institutions, nationwide and worldwide. Moreover, our group aims to provide highest quality education and research experience in AI-powered health informatics at all levels from K-12 to undergraduate to graduate and postdoctoral levels.

Research Interests

Technical

Explainable and Accountable AI

Safe and Resposnsible AI

AI Fairness

Clinical

Musculoskeletal Diseases and Disorders

Computational Orthopedics

Computational Pathology

News

Click here to see more news from 2022-2023!

2022-23

Supports | Partners | Collaborators

Awards