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SUMMARY:IC Colloquium: Translating AI Research into Real-World Clinical Im
 pact
DTSTART:20251110T141500
DTEND:20251110T150000
DTSTAMP:20260403T211344Z
UID:46217492bf55c216aa411d0b750b16c585f1c8b77b78af89c68b444f
CATEGORIES:Conferences - Seminars
DESCRIPTION:By: Katherine Fairchild - Digital Diagnostics\nVideo of her 
 talk\n\nAbstract\nAI holds massive potential to transform disease diagnosi
 s and treatment at scale\, yet most AI research never makes it through the
  clinic door. Whereas academic funding and publications optimize for metri
 cs like novelty and benchmark accuracy\, successful adoption of AI in heal
 thcare is governed by regulation\, quality standards\, reimbursement\, wor
 kflow integration\, and real‑world performance. The result is a signific
 ant translational gap: academic prototypes that satisfy reviewers but neve
 r impact a patient\, or are used only by their creators.\n\nDrawing on my 
 experience as Senior Director of AI/ML at Digital Diagnostics\, the compan
 y that cleared the first autonomous AI device through the U.S. federal reg
 ulatory and reimbursement pathways\, I will chart the current global lands
 cape of AI in healthcare\, focusing on data – collection\, handling\, va
 luation\, and rights – as a primary obstacle to closing the translation 
 gap. I will then argue for the adoption of a data-driven reward architectu
 re that collaboratively guides academic and industry interests toward maxi
 mizing patient outcomes with AI\; practical mechanisms include pre‑compe
 titive consortia with clear IP scaffolding\, industry-led validation infra
 structure\, and shared outcome registries financed via value‑based reimb
 ursement.\n\nIn sum\, I will suggest that AI research has barely begun to 
 make a real-world impact in the field of healthcare\, and unlocking its fu
 ll potential requires restructuring traditional institutions around a new 
 understanding of data.\n\nBio\nKatherine Fairchild is Senior Director of A
 I/ML at Digital Diagnostics\, where she leads the research and development
  of AI for healthcare into safe\, effective\, and secure production system
 s. She previously served as Head of Engineering at the MIT Quest for Intel
 ligence\, managing teams of software engineers and research scientists dev
 eloping computational models and software applications at the boundary of 
 natural and machine intelligence. Katherine brings a multidisciplinary per
 spective to her work\, with past experience in cognitive science research 
 and program management at Harvard University\, machine learning for financ
 ial applications at State Street Corporation\, and earlier work in the leg
 al field. She holds a BA in Political Science from Dalhousie University an
 d an MLA in Software Engineering from Harvard Extension School.
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420 https://epfl.zoom.us/
 j/62121769269
STATUS:CONFIRMED
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