At the "Medical Spring Festival," NVIDIA elaborated on "How to Implement AI in Healthcare"
JP Morgan stated that NVIDIA is making a "full-stack" layout in AI healthcare, building a closed loop from chips to tools to domain models, known as the "dry lab - wet lab" flywheel. At the same time, AI is transitioning from "pilot" to "employed," with the deployment speed of commercial AI in the U.S. healthcare industry being three times that of the overall economy. At the physical laboratory level, NVIDIA is collaborating with Thermo Fisher Scientific, and the industrialization process of drug discovery is also accelerating
At the recently concluded 44th JP Morgan Healthcare Conference, NVIDIA once again showcased how its computing power hegemony penetrates the deepest parts of the real economy.
According to Hard AI, the latest research report released by the JP Morgan analyst Harlan Sur's team on January 13, 2026, detailed the speech of NVIDIA's Vice President of Healthcare, Kimberly Powell. NVIDIA aims to transform the massive $4.9 trillion healthcare market into the next high-margin growth engine through a "full-stack" layout.
The report first pointed out the core logic of NVIDIA's business model: the profit explosion brought by full-stack vertical leverage. NVIDIA is building a closed loop from chips to tools to domain models, known as the "dry lab -> wet lab" flywheel. For Wall Street, the most attractive narrative is that the same R&D platform can be reused infinitely.
"Because the same core R&D platform can be reused horizontally (NVIDIA clearly categorizes sovereign AI and enterprise AI as one type and emphasizes the use of 'the same tools'), over time, incremental victories in vertical domains will bring highly attractive operational leverage."
Secondly, AI is moving from "pilot" to "employed." The year 2025 is seen as the year of the explosion of reasoning-capable AI agents (Agentic AI), and now, these digital employees are officially on the job. JP Morgan observed that the speed of commercial AI deployment in the healthcare industry is three times that of the overall U.S. economy, marking a structural acceleration in the adoption curve of enterprise-level AI in this field. With reasoning costs having dropped more than 100 times over the past four years, the ROI inflection point for large-scale adoption has arrived.
"NVIDIA is positioning itself as a platform layer, as spending shifts from pilot projects to paid deployments... Platforms like Abridge have already reclaimed over 30% of clinical physician time across more than 200 health systems worldwide."
At the physical laboratory level, NVIDIA is attempting to eliminate the "major data bottleneck" of humans through collaboration with Thermo Fisher. By deploying the "Three Computing Platforms" (COSMOS for simulation, Isaac for robot training, and edge computing for deployment), NVIDIA is driving the automation and intelligence of laboratories.
"By directly integrating agent intelligence into instruments to automate experimental design and quality control... these autonomous laboratories can achieve a 100-fold increase in throughput and reduce the production costs of complex drugs like cell therapies by 70%."
Finally, the industrialization process of drug discovery is accelerating. NVIDIA announced a landmark partnership with Eli Lilly, with both parties committing to invest $1 billion over five years. This is not just a collaboration but a signal: in the eyes of pharmaceutical giants, GPU clusters are no longer optional IT expenditures but essential production materials that determine life and death
"This marks that GPU clusters are now regarded as essential capital infrastructure—similar to wet labs—that directly determines the success of the R&D pipeline."
