AI PHI Affinity Group - Presentation by Jason Johnson and Renato Umeton September 2024

Topic

Unlocking the Potential of Large Language Models in Healthcare: A Case Study from Dana-Farber Cancer Institute

In this talk, we will share our experience of implementing large language models (LLMs) in a private, secure, and HIPAA-compliant way to support Dana-Farber Cancer Institute’s workforce and mission. We will explain how we adapted the GPT family of models (i.e., GPT-3.5 Turbo, GPT-4 Turbo, GPT-4o) to our specific needs and goals, and how we addressed some of the ethical, legal, and technical challenges along the way. We will also discuss why we excluded direct clinical use of LLMs (i.e., to treat, diagnose, or drive/inform clinical management) and limited clinical explorations to clinical research studies and institutionally sanctioned IT pilots. This decision played a pivotal role in unlocking lower-risk use cases in research and operations. We will also discuss ancillary activities required for broad AI deployment, like policy, ethics, training, monitoring, user support, and working with external companies for safe and impactful AI adoption. As the industry grapples with the concurrent imperatives of innovation, cost-effectiveness, and patient safety, we hope our insights and lessons learned might be beneficial to other organizations considering similar deployments of AI.

Speakers

Jason Johnson, PhD
Chief Data and Analytics Officer
Dana-Farber Cancer Institute

Jason Johnson is Chief Data and Analytics Officer and SVP of Informatics and Analytics at the Dana-Farber Cancer Institute in Boston, where he has served for 8 years. Jason’s team at DFCI includes research informatics, business intelligence, AI operations and data science services, bioinformatics and molecular data, data governance and architecture, data warehousing, informatics support for clinical trials, scientific computing, and software engineering. Prior to joining Dana-Farber, Jason was Head of R&D at PatientsLikeMe, a patient-focused research company in Cambridge, MA. He came to that position after 16 years in the biotech and pharmaceutical industries in various leadership roles in computational sciences, informatics, IT, and genomics. His last role at Merck & Co., Inc. was Associate VP, Scientific Informatics. Jason has undergraduate degrees in Philosophy and Physics from Stanford University, a Master’s degree in Physics from the University of Cambridge (UK), and a PhD in Biophysics from Harvard University.

Renato Umeton, PhD
Director of Artificial Intelligence Operations and Data Science Services
Dana-Farber Cancer Institute

Renato studied computer science for both Master’s and Bachelor’s, later he earned a Ph.D. in Mathematics and Informatics defending a thesis on Optimization and Ontology for Computational and Systems Biology, which brought him to work first at Microsoft and then at MIT. Additionally, he has pursued Executive Education at Harvard to complement his existing leadership skills.

Currently Renato serves as Director of Artificial Intelligence Operations and Data Science Services in the Informatics & Analytics department of Dana-Farber Cancer Institute, a teaching affiliate of Harvard Medical School. In this position, where he reports to the Chief Data and Analytics Officer, Renato created the departmental AI & data science horizontal, counting about 40 people (including temps) as of 2024. He accrued 15 years of experience in artificial intelligence, data science, and big data working across hospitals, academia, consulting, and in industry, where he operated in roles spanning from postdoc to director. In those contexts, he worked on several scientific publications and patents, some of which were leveraged in clinical trials or licensed. As of 2024, Renato co-authored 140+ scientific publications, 6+ patent applications, and he is currently affiliated also with MIT, Harvard School of Public Health, and Weill Cornell Medicine. Renato also participates in various data science academic-industry-government collaborations that aim at democratizing and expanding the reach of artificial intelligence and machine learning in healthcare, regulatory science, and healthcare financing.

In addition to his main responsibilities, he is/has been: (a) reviewer for various journals by NEJM Group, Nature Publishing Group, Cell Press, IEEE, ACM, and Oxford Press among the others, (b) invited speaker at top venues (e.g., US Federal Government Departments; Fortune 20 companies; top academic medical centers), (c) led several ML and AI communities and conferences (e.g., medperf.org and lod2024.icas.events), (d) mentor to 50+ mentees through programs varying from bootcamp to MD-PhD degree, and (e) manager for an online ML community counting 80,000+ members world-wide.

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