By leveraging specialized medical language models to evaluate RAG outputs, organizations can implement more rigorous quality control at scale without the time and expense of human evaluation. The John Snow Labs solution scores aspects of the RAG pipelines with transparent rubrics and natural language explanations, making assessment results interpretable for both technical and non‑technical stakeholders. This approach shortens feedback loops, accelerates improvement cycles, and provides greater confidence when deploying healthcare chatbots in production environments where accuracy is paramount.
The presentation will showcase practical implementation strategies and real‑world results that demonstrate how this evaluation framework enables healthcare organizations to build more reliable, trustworthy AI‑powered applications while maintaining compliance with healthcare information standards.
About the speaker
Chris Haddad
Machine Learning Solutions Architect
at Amazon
Chris Haddad is a results-driven and passionate machine learning specialist with over 9 years of experience in the healthcare and life science industries. He has worked on a variety of projects, including predictive analytics, payer life science work, full-stack data science development, and generative AI. He began his career as a machine learning engineer at Allscripts, a large electronic health records company. He developed algorithms to predict patient risk of readmission, identify high-cost patients, and optimize clinical workflows. He then moved to a community-based health services provider where he led a team of data scientists in developing a full-stack data science solution to improve patient outcomes. In his most recent role at McKinsey & Company, he led a team of machine learning engineers in developing and deploying predictive models that have saved healthcare payers millions of dollars. Chris is now a Senior Health ML Solutions Architect at Amazon Web Services, where he helps healthcare and life science companies adopt generative AI. Generative AI can be used to generate new drug molecules, design clinical trials, or create personalized patient experiences.
When
Sessions: April 2nd – 3rd 2024
Trainings: April 15th – 19th 2024
Contact
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