In the ever-evolving landscape of healthcare, doctors face an ongoing challenge: how to swiftly access vital medical information about their patients buried deep within databases. Traditional methods have proven time-consuming and often fall short of providing the comprehensive answers doctors need. But what if I told you that AI, SQL, and GraphQL have walked into fertility clinics, offering a groundbreaking solution?

In my presentation I explore the innovative use of Large Language Models (LLMs) in medical feature development. I delve into a novel approach that leverages LLMs to translate doctors' intricate questions into SQL and GraphQL queries, enabling prompt and accurate retrieval of patient data. The result? A revolution in the way doctors access and utilize critical information to make informed decisions.

Join me at the coding table as we uncover the objectives behind crafting the "chatting with my medical database" feature. Together, we'll unravel how LLM-based Python chains became integral to this feature and how GraphQL emerged as the superhero, leaving SQL in the dust. We will delve deep into the key development considerations that influenced our choices, encompassing security, cloud integration, flexibility to handle diverse inputs, and reliability in providing doctors with answers to their questions. Witness how concise and targeted Python code can efficiently achieve these objectives.

Shirli Di-Castro Shashua

Affiliation: Embie Clinic