Machine Learning in Clinical Laboratory and Point-of-Care Settings: Challenges, Opportunities, and Next Steps

• Nam Tran, PhD Dr. Tran is an internationally recognized clinical chemist and academic leader whose work bridges artificial intelligence, point-of-care testing (POCT), and translational laboratory medicine. At the University of Pittsburgh School of Medicine and UPMC, he serves as Associate Dean and Vice Chair for Biobanking as well as the medical director for enterprise-wide POCT. Dr Tran is also the Senior Technical Director for AI Operations at the Computational Pathology and AI Center of Excellence (CPACE). His leadership spans institution initiatives in diagnostic innovation, biobanking, and AI infrastructure, including the oversight of the CPACE AI Data Commons (ADC) Alliance—a platform designed to responsibly integrate laboratory and pathology data to power next-generation healthcare AI. Dr. Tran’s research has produced over 100 peer-reviewed publications and multiple patents in diagnostics and machine learning. He has led pioneering work in POCT, including multicenter studies that informed FDA guidance on glucose monitoring, rapid molecular testing for infectious diseases, and use of machine learning for detecting acute injury at the point of care. During the COVID-19 pandemic, during his time at UC Davis Health, he directed translational efforts that yielded several FDA-reviewed diagnostic platforms combining machine learning with mass spectrometry.

Reshaping the Future of Medical Care, Education and Research: The Pivotal Roles of Synthetic Data, Generative AI, and Auto-MLs

• Hooman H. Rashidi, MD, MS Dr. Rashidi combines his passion for patient care, research and education with his unique training in bioinformatics and machine learning (ML) to create innovative new tools (i.e. MILO, STNG, WSI Genie, Nubulon-GPT, Pitt-GPT+, etc.) and resources (Hematology Outlines, Cleveland Clinic's AI course, Pitt-AI-cademy, etc.) that improve clinical practice, research and education. His experience in AI/ML dates back to his graduate years at UCSD which subsequently allowed him to serve as the principal author and editor of several popular bioinformatics textbooks. This background has also enabled him to develop various novel AI-ML platforms. He is currently the Associate Dean of AI in Medicine, Professor & Endowed Chair of Lombardi-Shinozuka Experimental Pathology Research at the University of Pittsburgh School of Medicine, Executive Director of CPACE (Computational Pathology & AI Center of Excellence) and the Executive Vice Chair of the Computational Pathology & Informatics Division at UPMC. He is also Adjunct Professor of Engineering at the Carnegie Mellon University. Before joining U Pitt / UPMC, he served as the founding director of Cleveland Clinic’s PLMI Center for AI and Data Science & Vice Chair of Technology Innovation & Computational Pathology, and before that he served as the Director of AI for University of California Davis Medical Center and Professor & Vice chair of informatics, leading a large number of AI studies with numerous collaborators from various prominent institutions.

Learning Objectives:

1. Describe the current state of ML in in vitro diagnostic testing & Identify cases where ML has enhanced laboratory and point-of-care testing
2. Understand the concept of synthetic data (what it is and its subtypes, e.g. generative AI) and better appreciate the future roles of Generative AI within medicine (e.g. custom chatbots, etc.)
3. Identify barriers that limit ML implementation in the laboratory and point-of-care setting