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Stanford AI in Medicine Imaging Center

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

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Stanford AI in Medicine Imaging Center

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  1. Stanford AI in Medicine Imaging Center

  2. 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

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

  4. Expanded Scope Eleazar EskinJason ErnstBogdan PasaniucWei Wang Eran HalperinJessica LiNoah ZaitlenSriram Sankararaman New departmental faculty in Computational Genomics: Jointly appointed with: StatisticsAnesthesiologyBiological ChemistryComputer Science Human GeneticsNeurologyPathology Growth Areas of Data Science in Biomedicine • Clinical Machine Learning • Computer Vision in Medicine Leverage existing strength in Mathematical Modeling

  5. What is AI in Medicine? • Impact care: • 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

  6. 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

  7. 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…

  8. AI in Medicine @ UCLALed by EranHalperin • 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)

  9. AI in Medicine @ UCLA CROWDED AREA ENABLE CLINICAL SCIENCE WITH HEALTH DATA PARTNERSHIPWITHCLINICAL CHAIRS

  10. Computational Medicine Programs • Blockchain and Secure Computation in Medicine: Leverage transformative technology breakthroughs in cryptography • Effort led by SriramSankararaman, 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, BogdanPasaniuc • 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

  11. UCLA Computational Medicine is a Hub in the Scientific Community

  12. 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)

  13. 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)

  14. 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

  15. 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.

  16. 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

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