Healthcare is not short on information. It is overwhelmed by it. New studies are published every day, guidelines evolve, and treatment options continue to grow. For doctors, the real challenge is keeping up—quickly finding reliable answers while managing patients and limited time.
Artificial intelligence is beginning to address this imbalance. Its role is not to replace physicians, but to reduce friction in how medical knowledge is accessed and applied. The most effective systems act as filters: they process large volumes of research, identify what is relevant, and present it in a usable format. In a field where clinical decisions carry real consequences, fast access to reliable information is critical.
A strong example of this vertical, healthcare-focused approach is Open Evidence, often described as “the ChatGPT for doctors.” Built specifically for clinicians, it allows users to ask medical questions in natural language and receive answers grounded in peer-reviewed studies, clinical guidelines, and real-world data. What differentiates it from general AI tools is rigor: responses are linked to cited sources, allowing physicians to verify and evaluate the evidence themselves. The platform does not diagnose or prescribe; it supports clinical reasoning by reducing cognitive overload, particularly in complex cases.
Adoption has been rapid. By 2025, Open Evidence is used by more than 40% of U.S. physicians, making it one of the fastest-growing clinical decision-support platforms in the country. Usage has reached around 18 million queries per month, and the platform is deployed in more than 10,000 hospitals and medical centers across the United States (Source: Open Evidence). This augmentation-first model closely reflects how doctors already work, helping explain its rapid adoption.
While platforms like Open Evidence solve specific clinical workflows, they increasingly rely on a broader layer of AI infrastructure. Anthropic represents this foundational tier. Rather than building a single healthcare product, the company develops large language models—such as the Claude family—that can be integrated into applications ranging from clinical documentation and medical knowledge search to patient communication and internal decision-support systems. Designed with a strong focus on reliability, safety, and controllability, these models are well suited to regulated environments like healthcare. Through APIs and cloud platforms such as Amazon Bedrock, organizations can integrate advanced AI capabilities without developing their own core models.
This infrastructure approach is gaining momentum as adoption accelerates across the sector. More than 60% of healthcare organizations in the United States are already testing or deploying AI solutions, and many plan to increase investment in the coming years. Instead of building AI systems from scratch, hospitals, biotech companies, and digital health startups are increasingly layering specialized applications on top of foundation models.
The economic trajectory reflects this shift. According to Fortune Business Insights, The global market for artificial intelligence in healthcare was valued at $39.34 billion in 2025 and is expected to grow to $56.01 billion in 2026, reaching approximately $1.03 trillion by 2034, with a compound annual growth rate of nearly 44%. This is not incremental adoption—it is exponential expansion.
The pattern is becoming clear. Vertical platforms like Open Evidence address specific clinical challenges by translating complex medical knowledge into practical insights. Infrastructure providers such as Anthropic supply the scalable AI models that power a broader ecosystem of healthcare applications. Together, they signal a broader transition: AI is moving from experimental tool to an embedded layer of healthcare delivery, reshaping how medical knowledge is accessed and applied in everyday practice.
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