For years, artificial intelligence in healthcare lived in slide decks, pilot programs, and press releases. Promises were big, results were narrow, and adoption was slow. That phase is ending. In the past year, the largest AI companies in the world have made something clear through their product launches and infrastructure investments:
Healthcare will be built on AI.
Not as a feature. Not as an add-on. As a foundational layer across consumer experiences, clinical workflows, life sciences, and drug development. What makes this moment different is not hype. It is architecture. We are watching the health AI stack form in real time, spanning personalized consumer tools, enterprise-grade healthcare platforms, and specialized medical models that handle text, images, and speech. OpenAI, Anthropic, and Google are approaching the problem from different angles, but they are converging on the same outcome. This is no longer about experimentation. It is about ownership of the future healthcare operating system.
Healthcare represents nearly 20 percent of GDP in the United States alone. It is data-rich, operationally complex, and burdened by inefficiencies that directly affect cost, access, and outcomes.
It is also uniquely suited for AI transformation because:
Until recently, AI systems were not reliable, multimodal, or secure enough to operate in regulated healthcare environments. That constraint has changed.
Large language models now reason across complex domains. Vision models understand medical imaging. Speech models handle clinical dictation. Privacy and compliance tooling is maturing.
As a result, every serious AI company is making a strategic bet on healthcare.
OpenAI’s strategy centers on the consumer experience first, then extends into healthcare organizations.
ChatGPT Health is a dedicated experience inside ChatGPT designed specifically for health-related use cases. Instead of generic medical information, users can securely connect personal health data from medical records and wellness platforms like Apple Health, Function, and MyFitnessPal.
This allows conversations to be grounded in individual data, not population averages.
Key use cases include:
Privacy is central to the design. Health conversations are not used to train foundation models, which addresses one of the biggest barriers to consumer trust in AI-driven healthcare.
On the provider side, OpenAI is positioning ChatGPT for Healthcare as an enterprise platform rather than a chatbot.
The emphasis is on:
This includes documentation assistance, patient communication support, and clinical knowledge synthesis. The value proposition is not replacing clinicians but giving them leverage in a system overwhelmed by complexity and paperwork.
OpenAI’s long-term play is clear. Become the default interface for health information at the consumer level, then expand into healthcare organizations once trust and familiarity are established.
Anthropic is taking a more infrastructure-first approach that reflects a deep understanding of healthcare’s core problem.
Healthcare is fragmented.
Data lives in disconnected systems. Standards are inconsistent. Workflows vary across providers, payers, and regulators. Anthropic is building Claude for Healthcare with this reality as the starting point.
Claude for Healthcare is explicitly framed as HIPAA-ready and suitable for providers, payers, and healthcare-adjacent organizations.
The differentiator is not just safety. It is connectivity.
Anthropic is investing heavily in connectors that allow AI agents to work across existing healthcare systems, including:
These connectors enable real operational use cases such as:
Anthropic is also developing agent skills around FHIR, which is critical for interoperability in modern healthcare systems.
Beyond care delivery, Anthropic is pushing into life sciences and pharmaceutical workflows.
This includes support for:
By integrating with platforms like Medidata and ClinicalTrials.gov, Anthropic is positioning Claude as a tool that supports the entire lifecycle of medical innovation, from research to regulation.
Anthropic’s thesis is that whoever controls healthcare workflows and data connectivity becomes indispensable. Applications are secondary to infrastructure.
Google’s approach is less about interfaces and more about foundational capability.
Rather than focusing on end-user products, Google is investing in open medical models that developers and healthcare organizations can adapt and deploy.
MedGemma 1.5 is an open medical model designed to handle complex clinical data across modalities.
Its capabilities include:
These capabilities are essential for diagnostic support, clinical research, and medical education. The emphasis on open access allows hospitals, startups, and researchers to fine-tune and deploy models without relying on centralized cloud services.
Google also released MedASR, an open speech-to-text model fine-tuned for medical dictation.
Clinical environments depend heavily on voice workflows. Accurate medical transcription is foundational for documentation, coding, and clinical reasoning. MedASR is designed to integrate with MedGemma for downstream reasoning and decision support.
The most important aspect of Google’s strategy is that these models can run locally and offline. This enables:
Google is building the raw materials for health AI at scale, not just products for hospitals.
Across these approaches, a clear pattern is emerging.
The health AI ecosystem is organizing into layers:
Each company is targeting a different layer, but the layers reinforce each other. Together, they form an end-to-end AI-driven healthcare system.
This is why the current moment feels different from past waves of healthcare technology.
Once platform layers are established, the next phase is inevitable. AI moves from supporting healthcare to reshaping how medicine is developed and delivered.
This is why the partnership between NVIDIA and Eli Lilly matters. Their co-innovation AI lab is focused on drug discovery and development, using large-scale compute and an “AI factory” model to accelerate research timelines and reduce costs.
Drug discovery is one of the most expensive and failure-prone processes in healthcare. AI-driven simulation, molecule design, and trial optimization have the potential to compress years into months.
Importantly, regulators are not ignoring these shifts. Cross-Atlantic efforts are emerging to define principles for responsible AI use in drug development and clinical research. Rather than blocking progress, regulators are increasingly focused on governance frameworks that allow innovation while protecting patients. This co-evolution of technology and regulation is critical. Healthcare adoption depends on trust as much as performance.
Every major AI company wants to win healthcare for a simple reason. Healthcare is the ultimate proving ground for AI.
Winning healthcare means becoming the underlying operating system for medicine. It means shaping how data flows, how decisions are made, and how care is delivered at scale. This is no longer about chatbots answering medical questions. It is about rebuilding healthcare around intelligent systems that can reason, see, listen, and act across the entire ecosystem. The race is already underway. And this time, it is real.
