Announcing the Establishment of the AI2D, Center for AI and Data Science for Integrated Diagnostics

We are pleased to announce the establishment of the Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) in the Perelman School of Medicine (PSOM), to be directed by Christos Davatzikos, PhD (Department of Radiology) and Li-San Wang, PhD (Department of Pathology and Laboratory Medicine). The AI2D is a highly collaborative effort led by faculty in several PSOM departments, with focus on AI-driven integrated diagnostics, and was seeded by the emerging Integrated Diagnostics Program co-led by the Department of Radiology and the Department of Pathology and Laboratory Medicine. The center will catalyze the use of advanced, but domain-specific AI and data science methods to leverage complex and large datasets, especially data produced by various laboratory tests offered by the Departments of Radiology and Pathology and Laboratory Medicine, eventually leading to a new generation of “in silico biomarkers” and personalized diagnostic and predictive models. The Center’s activities will dovetail with work in Pathology and Laboratory Medicine’s new Division of Diagnostic Innovation, as well as with translational AI work in both departments.

Areas of focus in AI2D include:

• On the clinical front, the emerging Integrated Diagnostics program, co-led by the departments of Radiology and Pathology and Laboratory Medicine. Multi-omic data science is evolving into one of the most effective catalysts of this program, since it is the place where biomarkers and measurements from various laboratory tests meet to assist in precision diagnosis, prognosis, and prediction of outcome.
• On the biological scientific front, the understanding and better description of disease heterogeneity, including spatial, temporal and inter-patient heterogeneity elucidated in part by imaging in its broad sense (in vivo and tissue-based).
• On the computational scientific front, methodologies for AI (including deep learning) and data science of big data, the latter defined in terms of size and complexity. To this end, AI2D is expected to further strengthen its relationships with the School of Engineering and Applied Science, and with the School for Arts and Sciences, focusing more on domain-specific implementation of AI and data science methods.

Christos Davatzikos is the Wallace T. Miller Sr. Professor of Radiology, with joint appointment in Electrical and Systems Engineering. He is also a member of the Bioengineering and the Applied Mathematics and Computational Science graduate groups. He leads the AI for Biomedical Imaging Laboratory (AIBIL) which focuses on image analysis and machine learning methods for clinical neuroscience studies, as well as on application of AI methods to large international consortia involving imaging and non-imaging data.

Li-San Wang is Professor of Pathology and Laboratory Medicine, with secondary appointment in the Department of Computer and Information Science.  He was Chair of the Genomics and Computational Biology (GCB) Graduate Group between 2014 and 2018.  Currently he is Vice Chair for Research in the Department of Pathology and Laboratory Medicine.  As founding Co-Director of Penn Neurodegeneration Genomics Center (PNGC), his research integrates bioinformatics, genomics and genetics in large cohorts such as Alzheimer’s Disease Sequencing Project (ADSP) to study biology of dementia and identify risk factors in underrepresented minority populations.

The center’s Senior Faculty Committee includes Drs. Despina Kontos (Radiology), Michael Feldman and Daniel Herman (Pathology and Laboratory Medicine), Jinbo Chen (Biostatistics), and Theodore Satterthwaite (Psychiatry), and the initial faculty list includes a growing number of over a dozen faculty representing multiple disciplines from several departments.

Please join us in congratulating this founding group of the AI2D Center, which will increase the footprint of Penn in interdisciplinary Biomedical Data Science research with impact on fundamental science, development of novel clinical diagnostics, and ultimately patient care.