{"id":146746,"date":"2026-04-27T23:54:27","date_gmt":"2026-04-28T03:54:27","guid":{"rendered":"https:\/\/medcitynews.com\/?p=146746"},"modified":"2026-04-28T02:07:44","modified_gmt":"2026-04-28T06:07:44","slug":"ai-healthcare-nvidia-hoppr-tech","status":"publish","type":"post","link":"https:\/\/medcitynews.com\/2026\/04\/ai-healthcare-nvidia-hoppr-tech\/","title":{"rendered":"Why Healthcare AI Still Can\u2019t Scale \u2014 and How Nvidia & Hoppr Are Trying to Fix It"},"content":{"rendered":"\n

Issues with deployment and scaling are the real barriers holding back healthcare AI from delivering value, according to leaders at AI companies Nvidia<\/a> and Hoppr<\/a>.<\/p>\n\n\n\n

That\u2019s why they\u2019re shifting their focus away from building standalone models and zeroing in on the infrastructure needed for those models to actually be used in clinical practice. Hoppr has built an AI foundry that uses Nvidia\u2019s computing and foundation models \u2014 an offering the partners say gives developers access to tools for launching medical imaging AI more easily at scale.<\/p>\n\n\n\n

The foundry aims to help providers develop, validate and then deploy their own AI models without having to start from scratch, said Hoppr CEO Khan Siddiqui.<\/p>\n\n\n\n

\u201cWe’re providing the platform where health systems, radiology practices and med device companies can now build their fine-tuned models very quickly and deploy them very quickly in their practice or in their product,\u201d he explained.<\/p>\n\n\n\n

Hospitals no longer need huge amounts of data or infrastructure to create their own models because Hoppr and Nvidia pre-train their foundation models on massive datasets, he pointed out. In the past, providers needed to purchase massive datasets containing about 100,000 patient records to train AI models, but pre-trained foundation models allow hospitals to shape models using much smaller datasets, sometimes containing just hundreds of records, Siddiqui stated.<\/p>\n\n\n\n

The foundry\u2019s goal is to make custom, localized AI development more feasible for providers, he declared.<\/p>\n\n\n\n

The focus is on increasing imaging AI models so that providers can embed specialized tools directly into radiology and diagnostic workflows rather than relying on one-size-fits-all solutions, Siddiqui noted.<\/p>\n\n\n\n

David Niewolny, global head of business development at Nvidia, said that the AI foundry is a sign of a broader shift moving healthcare AI from isolated model development to a full ecosystem of tools that can be deployed directly into clinical workflows. <\/p>\n\n\n\n

He said Hoppr solves the \u201clast mile\u201d problem.<\/p>\n\n\n\n

\u201cNvidia is providing the tools and the raw performance. Hoppr is taking that, and through the use of open models and the fine-tuning that they’re doing, turns it into a much more turnkey clinical-grade AI that is designed to be run inside of hospitals,\u201d Niewolny remarked.<\/p>\n\n\n\n

The partners\u2019 effort reflects a push to turn healthcare AI into something closer to a software development ecosystem than a collection of point solutions. They\u2019re betting that as foundation models and deployment platforms mature, providers will move more and more from purchasing AI applications to building and iterating on them internally. <\/p>\n\n\n\n

Whether that shift will actually drive greater clinical adoption of tools \u2014 or simply add another  unnecessary layer of complexity \u2014 is still unknown. But this will likely help determine how quickly imaging AI can evolve from pilot projects to routine care.<\/p>\n\n\n\n

Photo: peterhowell, Getty Images<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Nvidia and Hoppr are betting that healthcare AI will scale not through standalone applications, but through a new infrastructure layer that lets providers develop and deploy their own imaging models. <\/p>\n","protected":false},"author":31620,"featured_media":146747,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"om_disable_all_campaigns":false,"featured_image_focal_point":[],"homepage_placement":"top","homepage_placements":{"top":true,"featured":true,"sidebar":false},"homepage_alternative_layout":false,"featured_categories":[48,117,105,51659],"hide_from_feed":false,"footnotes":""},"categories":[105,117,48,51659,60],"tags":[44258,44026,49328,37616,20670,52603,45891],"class_list":["post-146746","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-health-tech","category-hospitals-channel","category-providers","category-startup-channel","tag-ai-in-healthcare","tag-artificial-intelligence-in-healthcare","tag-hoppr","tag-jumpstart-foundry","tag-nvidia","tag-scalability","tag-scale"],"acf":[],"yoast_head":"\nWhy Healthcare AI Still Can\u2019t Scale \u2014 and How Nvidia & Hoppr Are Trying to Fix It - 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