Artificial Intelligence and Machine Learning in Health Care: From Model Development to Real-World Implementation
Artificial Intelligence (AI) and Deep Learning are transforming health care by enabling data-driven decision support in complex clinical systems. This presentation introduces two machine learning models developed at Missouri S&T: the Deceased Donor Organ Allocation (DDOA) model and the First Acceptance model. These systems augment the decision-making processes of Organ Procurement Organization (OPO) coordinators and transplant surgeons within the United States’ highly complex organ allocation framework. The models are discussed from the perspective of design, development, and validation in the machine learning lifecycle.
The second part of the presentation focuses on implementation outcomes. Five OPOs in the United States are currently using or evaluating these models through collaboration with a commercial partner licensed by Missouri S&T. Lessons learned from these deployments highlight both the opportunities and challenges of translating advanced AI models from conceptualization into operational health care practice.
Dr. Cihan H. Dagli is a Professor of Systems Engineering and Engineering Management. He is the founder of Missouri S&T’s Systems Engineering Graduate Program. He is also director of the Smart Engineering Systems Lab (SESL) at Missouri S&T. Dr. Dagli is a Fellow of International Council of Systems Engineering (INCOSE), Institute of Industrial Engineers (IIE), and International Foundation of Production Research. With over 490 publications and a strong international consulting portfolio, his research spans system-of-systems architecting, cyber‑physical systems, deep learning, machine learning and computational intelligence. In recent years, Dr. Dagli has applied this expertise to a pressing medical challenge: improving kidney transplant allocation with innovations that merge predictive analytics, intelligent architecture, and human-AI collaboration to improve life-saving outcomes in organ transplantation.