IC Colloquium: Translating AI Research into Real-World Clinical Impact

By: Katherine Fairchild - Digital Diagnostics

Abstract
AI holds massive potential to transform disease diagnosis and treatment at scale, yet most AI research never makes it through the clinic door. Whereas academic funding and publications optimize for metrics like novelty and benchmark accuracy, successful adoption of AI in healthcare is governed by regulation, quality standards, reimbursement, workflow integration, and real‑world performance. The result is a significant translational gap: academic prototypes that satisfy reviewers but never impact a patient, or are used only by their creators.

Drawing on my experience as Senior Director of AI/ML at Digital Diagnostics, the company that cleared the first autonomous AI device through the U.S. federal regulatory and reimbursement pathways, I will chart the current global landscape of AI in healthcare, focusing on data – collection, handling, valuation, and rights – as a primary obstacle to closing the translation gap. I will then argue for the adoption of a data-driven reward architecture that collaboratively guides academic and industry interests toward maximizing patient outcomes with AI; practical mechanisms include pre‑competitive consortia with clear IP scaffolding, industry-led validation infrastructure, and shared outcome registries financed via value‑based reimbursement.

In sum, I will suggest that AI research has barely begun to make a real-world impact in the field of healthcare, and unlocking its full potential requires restructuring traditional institutions around a new understanding of data.

Bio
Katherine Fairchild is Senior Director of AI/ML at Digital Diagnostics, where she leads the research and development of AI for healthcare into safe, effective, and secure production systems. She previously served as Head of Engineering at the MIT Quest for Intelligence, managing teams of software engineers and research scientists developing computational models and software applications at the boundary of natural and machine intelligence. Katherine brings a multidisciplinary perspective to her work, with past experience in cognitive science research and program management at Harvard University, machine learning for financial applications at State Street Corporation, and earlier work in the legal field. She holds a BA in Political Science from Dalhousie University and an MLA in Software Engineering from Harvard Extension School.