Michael William Sjoding

Michael William Sjoding, MD, MSc, is an Associate Professor of Internal Medicine at the University of Michigan Medical School. He holds Center Memberships at the Institute for Healthcare Policy and Innovation, Center for Computational Medicine and Bioinformatics, Precision Health Initiative, and e-Health and Artificial Intelligence Initiative. His research focuses on leveraging machine learning and artificial intelligence to address critical care challenges, particularly in acute respiratory distress syndrome (ARDS), sepsis, and healthcare equity. He investigates interventions to improve clinical decision-making, ventilator management practices, and reducing racial disparities in medical device accuracy (e.g., pulse oximetry). Recent work includes developing AI-driven diagnostic models for ARDS, analyzing practice variations in critical care, and understanding the impact of anti-anaerobic antibiotics on patient outcomes. Dr. Sjoding is actively involved in translational research, bridging computational methods with clinical practice to enhance patient care.

Education: MD and Master of Science degrees from University of Michigan.

Research interests include: 1) AI applications in critical care (e.g., chest X-ray analysis, predictive modeling), 2) Pulmonary and critical care medicine (ARDS pathophysiology, mechanical ventilation strategies), 3) Health equity (racial disparities in medical technology accuracy, algorithmic bias in healthcare AI), and 4) Clinical epidemiology (antibiotic stewardship, postoperative complications). His studies often involve large-scale data analysis and multicenter collaborations to ensure generalizability.

Recent publications highlight advancements in ARDS detection algorithms, delayed extubation practices, and racial disparities in medical testing patterns affecting AI training data. He collaborates with interdisciplinary teams to address urgent clinical questions through computational approaches.