In a current webinar, two leaders — one from trade, one from well being system management — lower to the center of probably the most urgent challenges in healthcare AI at the moment: why so many deployments stall, and what it really takes to scale.
The issue isn’t AI. It’s fragmentation. Demetri Giannikopoulos, Aidoc’s former Chief Transformation Officer, mentioned:
“It’s incumbent on us as distributors to offer not only a single use case however a number of use instances — to take us out of the world of level options.”
That shift from level options to platforms isn’t simply semantic. As Giannikopoulos additional explains, true adoption hinges on delivering worth throughout dozens of workflows — 25, 30, even 100. As a result of when scientific groups see influence throughout the total spectrum of their work, they have interaction. When options run reliably within the background, adoption turns into sustainable and never simply tactical.
Nonetheless, it’s not solely about scale. It’s additionally about system influence.
Ashley Weber, Vice President of IS Ancillary Providers at Ochsner Well being, introduced the operational lens, describing the very actual penalties of bringing in too many one-off instruments — every with its personal software, communication channel and interface necessities.
It’s not solely an IT burden that may create legal responsibility, because it fragments clinician workflows, however it could actually additionally introduce communication dangers among the many care groups.
“We actually need to be certain that we’ve unified communications throughout our group… It’s a threat we simply don’t need to take.”
The takeaway for healthcare leaders? To scale AI efficiently, you want greater than a high-performing algorithm. You want infrastructure that consolidates fashions, limits duplication and aligns with system-wide requirements for governance, safety and communication.
In different phrases, AI technique isn’t about deploying extra AI. It’s about deploying AI higher.
Entry the total on-demand webinar, “From Promise to Apply: Driving System-Huge Effectivity with Scientific AI,” with insights from leaders at Foley & Lardner, LLP, Ochsner, Coalition for Well being AI (CHAI) and Aidoc.