Lessons Learned Applying Large Language Models in Healthcare
Large language models provide a leap in capabilities on understanding medical language and context – from passing the US medical licensing exam to summarizing clinical notes. They also suffer from a wide range of issues – hallucinations, robustness, privacy, bias – blocking many use cases.
This session shares currently deployed software, lessons learned, and best practices that John Snow Labs has learned while enabling academic medical centers, pharmaceuticals, and health IT companies to build LLM-based solutions, focused on answering these three questions:
- What generative AI use cases are healthcare organizations deploying today?
- Which solution architecture(s) are used to deliver these use cases?
- How do general-purpose LLMs perform versus healthcare-specific LLMs in these use cases?
David Talby
CTO at John Snow Labs
David Talby is the Chief Technology Officer at John Snow Labs, helping companies apply artificial intelligence to solve real-world problems in healthcare and life science.
David is the creator of Spark NLP – the world’s most widely used natural language processing library in the enterprise. He has extensive experience building and running web-scale software platforms and teams – in startups, for Microsoft’s Bing in the US and Europe, and to scale Amazon’s financial systems in Seattle and the UK.
David holds a Ph.D. in Computer Science and Master’s degrees in both Computer Science and Business Administration. He was named USA CTO of the Year by the Global 100 Awards and Game Changers Awards in 2022.
