%20(1200%20x%20400%20px).png)
CHARGE Current: Abridge's AI partnership with Nvidia surfaces the risk of 'recursive AI'
- Levi Miller
- 2 days ago
- 3 min read
At a sweeping keynote address on June 11, Abridge announced a strategic partnership with NVIDIA’s Nemotron to train a frontier artificial intelligence model designed specifically for clinical conversations. The new AI model will ostensibly be tailor designed for Abridge’s new suite of expanded operations, which it claims provides support for physicians at all stages of service, by surfacing and generating pre-visit notes with clinical context, suggesting discussion topics during clinical discussion, and coordinating post-visit summaries, claim adjudication, and billing. By domain adapting earlier in the development cycle, Abridge frames its access to 100 million de-identified clinical conversations annually as the “foundation model” for a generation of AI which “reasons clinically from its foundation.” Abridge’s ambitious new posture as the neutral health infrastructure also surfaces deep risk: partnership with Nemotron converts erroneous AI-generated notes from medical artifacts to the “foundation,” as Abridge puts it, of new AI.
Abridge's health infrastructure gambit
Though NVIDIA qualifies that they are “not a healthcare company,” they are an investor in Abridge through their venture capital wing NVentures, and are confident that Abridge provides the domain specific “clinical conversation foundation model” needed to navigate all of the “complexity of healthcare and all of the workflows.” Crucially, Abridge is building its proprietary model on top of NVIDIA’s Blackwell AI infrastructure and using NVIDIA’s open source model Nemotron which provides users full access to model training data and weights.
Consider the context: in August, Epic – Abridge’s largest partner and former shareholder – announced its plans to launch its own clinical scribe and sold off its shares in Abridge. Last summer, Abridge announced a prior-authorization feature, later countered by UnitedHealth Group’s Optum. Also last fall, the group began its foray into support for clinical decision making. That NVIDIA is bullish on Abridge’s newest movement against entrenched health giants indicates how confident NVIDIA is in Abridge as data custodians and in its own open-source Nemotron: if Abridge’s proprietary clinician supports AI functions “foundationally” as claimed, it is a portable, ostensibly neutral clinical infrastructure that could compete with the health giants.
The cost of doing business: the cost of data
NVIDIA is right: as stewards of raw clinical data, Abridge possesses a considerable comparative advantage compared to other health AI developers. Consider, first, the price of data within the current regulatory landscape: last October, General Catalyst purchased Summa Health, an Ohio based healthcare delivery system, for $515 million dollars, and transformed the health network into a data collection and testing ground for its new health venture, the Health Assurance Transformation Corporation. In Utah, startups Doctronic and Legion Health obtained regulatory approval for autonomous refills after building traditional telehealth networks providing native data access. Checkmark Abridge on both fronts: its data and networks are native.
Error artifacts become health infrastructure
While the data sources the confidence in Abridge, it should also surface uncertainty for health professionals across hospital networks. Ambient scribes occasionally generate incorrect or flawed transcriptions of clinical conversations – that’s a feature, not a bug, and certainly not an indictment of Abridge’s technology. Governance committees, CIOs, CAIOs and other AI leaders, of course, attempt to factor the rate of clinical accuracy before deployment of emergent health technologies. Under current deployment regimes, transcription errors are mere artifacts of deployment, factored into the risk-calculus of industry professionals. A domain-specific model trained on top of flawed medical data, however, transmutes those artifacts into the foundation of an entire health infrastructure.
To be sure, health innovators have always struggled with incomplete or inaccurate data sets and struggled to transition from limited sandbox data environments to operations with real, clinical research data. Here, though, Abridge and NVIDIA are essentially building AI on top of AI. At the same keynote, Abridge disclosed a strategic investment from Eli Lilly. That investment recalls Eli Lilly’s relationship with OpenEvidence, which partially trains its clinical models on top of Eli Lilly’s pharmaceutical trials and testing. OpenEvidence delivers targeted advertising for Eli Lilly and other pharmaceutical companies at the point of care; when sponsored data surfaces sponsored pharmaceuticals, OpenEvidence provides explanations of Eli Lilly products to HCPs.
As such, the risk multiplies. An addendum: Abridge, NVIDIA, and Eli Lilly are building an AI on top of an AI, perhaps on top of a sponsored data set. The point is not to criticize Abridge’s product, which over 300 hospital networks employ, nor is the risk of what we can coin recursive models Abridge specific. Rather, as domain specific open-source models proliferate, industry professionals should keep an eye out for the opaque AIs they are interacting with.
