A complex field like healthcare provides many opportunities for machine learning and generative AI applications. In practical terms, this often involves the art of building bridges between the possible and the feasible – while technical tools exist that make fancy applications possible, only a subset of these are feasible at a non-profit healthcare center within regulatory and resource constraints. In this talk, we will discuss healthcare applications using a multi-faceted approach, including domain context and practical limitations along with the technical set-up.

Adaptability is key, with tools and problems judiciously chosen for meaningful and successful applications. Simplest natural language processing methods can still add value, projects that do not involve protected health information can serve as a sandbox while meeting important business needs, and sophisticated generative artificial intelligence techniques can be combined with traditional machine learning methods for maximal impact.

She will present examples of each of these scenarios, and also describe the challenges encountered. Her hope is to provide one snapshot of the current state of realistic generative AI and Machine Learning applications at a healthcare delivery organization.