Conference talk · AI in healthcare · VivaTech 2019
VivaTech 2019 · Microsoft Health & Life Sciences · English
Past — The trajectory of AI in medicine
From early diagnostic algorithms to deep learning: a structured retrospective on the milestones that made AI clinically relevant. Expert systems, medical imaging, genomics, and the turning point of large labelled datasets. Why medicine lagged behind other sectors, and what changed.
Present — What AI can and cannot do in 2019
State of the art in 2019: AI-assisted radiology, sepsis prediction, drug discovery acceleration, population health analytics. The Microsoft Health & Life Sciences perspective on deployment patterns across European health systems. The gap between proof-of-concept and operational production — the data quality problem, interoperability barriers, and governance constraints.
Perspectives — What needs to happen next
Conditions for industrial-scale AI deployment in healthcare: standardised, ambient, high-quality data; regulatory frameworks adapted to AI as a medical device; organisational transformation of clinical workflows; and the shift from AI as a tool to AI as infrastructure. The framing that would directly shape PREDICARE and TweenMe.
The three conditions identified in 2019 — data quality, regulatory readiness, organisational transformation — are precisely the three problems that PREDICARE addresses at territorial scale and TweenMe solves technically. The HDLSS challenge (High-Dimensional Low-Sample-Size), the heterogeneity of clinical data sources, the need for standardised digital twin generation: all of this was named here, six years before the operational solution.
VivaTech 2019 is one of the earliest public formulations of the problem space that the Twingital Institute now works to resolve.