The fields of clinical informatics and computational pathology are changing the way medicine is practiced in every specialty.

Our department is at the forefront of this change – we are nationally recognized leaders in education and translational research in informatics and computational pathology, including machine learning/artificial intelligence applications and new digital imaging technologies, including whole-slide scanning of clinical cases that are linked to our extensive pathology case database through PathSearch.

Educational programs include our Clinical Informatics Fellowship program which brings together the unique research, clinical and educational experiences at UC Davis Health to create the next-generation of physician-informaticians. Our clinical informatics fellowship is ACMGE-approved and currently accepts two fellows, one from pathology, and one from clinical specialties. 

New technologies developed by department faculty include:

• MILO (Machine Learning Intelligence Optimizer), an automated machine learning platform.
• FIBI (Fluorescence Imitating Brightfield Imaging): a new method for rapid slide-free histology.
• MUSE (Microscopy with Ultraviolet Surface Excitation), recipient of the 2018 Astellas C3 Technology Prize.

Check out some of our recent publications to learn more about our research and creative work in informatics and computational pathology. Our work involves faculty in all subspecialties and research areas of the department:

• Prediction of Tuberculosis using an Automated Machine Learning Platform for Models Trained on Synthetic Data.
• Advances in Deep Neuropathological Phenotyping of Alzheimer Disease: Past, Present, and Future.
• Artificial Intelligence in Pathology.
• Novel Application of Automated Machine Learning with MALDI-TOF-MS for Rapid High-Throughput Screening of COVID-19: A Proof of Concept.
• Automated Machine-Learning for Endemic Active Tuberculosis Prediction from Multiplex Serologic Data.
• Enhancing Military Burn- and Trauma-Related Acute Kidney Injury Prediction Through an Automated Machine Learning Platform and Point-of-Care Testing.
• Automated Computational Detection of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis.