Stanford AI in Medicine Imaging Center

Departmental Goals Become the leading program in data science applied to biomedical data Transform patient care in the UCLA Health System by leveraging advances in AI and Machine Learning

Our plan Expand research scope and educational programs Develop AI in Medicine @ UCLA Programs Leverage UCLA’s leadership in Data Sciences: Engineering School Affiliation

Expanded Scope New departmental faculty in Computational Genomics: Eleazar Eskin Jason Ernst Bogdan Pasaniuc Wei Wang Eran Halperin Jessica Li Noah Zaitlen Sriram Sankararaman Jointly appointed with: Statistics Anesthesiology Biological Chemistry Computer Science Human Genetics Neurology Pathology Growth Areas of Data Science in Biomedicine Clinical Machine Learning Computer Vision in Medicine Leverage existing strength in Mathematical Modeling

What is AI in Medicine? Impact care: Reduce costs. Identify high risk cases. Improve speed and accuracy of diagnosis. Predict treatment outcomes and side effects. Reduce costs. Requires research in AI and research in Medicine

What is NOT AI in Medicine? Analysis of small numbers of patients and/or small numbers of features Utilizing “Machine Learning Software” Hiring “Data Scientists” Information Technology

In Partnership with UCLA Health Electronic Health Records (2M+ records) Genomic Data (25,000 DNA genotypes + 750 week) Waveforms (30,000 patients per year) Images (1,500 imaging studies per day) more…

AI in Medicine @ UCLA Led by Eran Halperin CompMed/Anesthesiology Collaboration: Preoperative predictions of in-hospital mortality using electronic medical record data Effort led by Eran Halperin/Ira Hofer. Used EHR data from ~50,000 patients going through surgery. Applied machine learning techniques to predict preoperative risk for mortality (AUC = 0.92). Saves the time of ASA Physical Status Classification determined by the anesthesiologsits (AUC = 0.86) Next step: Improve care and reduce costs by saving anesthesiologists time. Creating a network of collaborations CompMed + Clinical Departments (OBGYN, Neurology, Anesthesiology)

AI in Medicine @ UCLA CROWDED AREA Prediction Insight Discovery Translation CROWDED AREA ENABLE CLINICAL SCIENCE WITH HEALTH DATA PARTNERSHIP WITH CLINICAL CHAIRS

Computational Medicine Programs Blockchain and Secure Computation in Medicine: Leverage transformative technology breakthroughs in cryptography Effort led by Sriram Sankararaman, Amit Sahai Develop technology for analysis of genomic and medical data while preserving privacy and data sharing policy. Builds upon foundational research @ UCLA: Secure Computation (Sahai), Differential Privacy in Genetics (Sankararaman), Private Genome Analysis (Sahai, Eskin) Computational Genomics in the Health System: Computational methods for combining genomic and health system data Effort led by Jessica Li, Jason Ernst, Bogdan Pasaniuc Builds upon foundational research @ UCLA in Computational Genomics Testbeds are UCLA Health System through AtLAs project and Depression Grand Challenge Tech Transfer to Industry: Commercialization and Software Licensing

UCLA Computational Medicine is a Hub in the Scientific Community Industry Partners UCLA Computational Medicine (UCLA Health Data) Academic Medical Centers Scientific Community Leadership

Industry Partners Industry Partners will translate discoveries beyond UCLA Health System Current Partnerships Amazon We are looking for industry partners to: Develop AI in Medicine Technology Answer Medical Questions using UCLA Health Data (e.g., stratifying patients receiving drug to reduce side effects)

Academic Medical Center Partnerships Goal: Establish Formal Collaborative Relationship with Health Systems which have Genomic Patient Data Replication across Health Systems Meta-Analysis (e.g. NIDA Opiod Consortium) Share expertise and collaborate Current Partnerships in Progress Vanderbilt (Nancy Cox) University of Michigan (Michael Bohenkhe) Mt. Sinai (Eimear Kenney)

Scientific Community Leadership Computational Genomics Summer Institute 50 Faculty and 150 participants spent 1 week to 1 month at UCLA in 2018 External Affiliate Member Program Incorporate Scientific Community Expertise in Analyzing UCLA Health Data Within UC Expertise Outside UC Expertise

UCLA’s Potential Best group of faculty in Computational Genomics + Machine Learning in the Country Strong Computer Science / Engineering International leaders in Machine Learning, Data Mining, Computer Vision and related areas. Strong Medical School Excellent and large Health System -> unique data. Single Campus A history of interdisciplinary activities.

UCLA Computational Medicine New DGSOM Department (formerly Biomathematics) Department Goals: Become the leading program in data science applied to biomedical data Transform patient care in the UCLA Health System by leveraging advances in AI and Machine Learning Department Scope: Computational Genomics Machine Learning on Clinical Data 8 Faculty joined Biomathematics in 2018 Ongoing FTE Faculty Search Provides bridge to Data Science Expertise on the Main Campus Computer Vision on Medical Images Mathematical Modeling