Artificial intelligence in health care is no longer a distant idea. It is becoming part of the way patients in the U.S. book appointments, refill prescriptions, and even get early guidance on symptoms, often without realizing that an algorithm is involved. As this shift unfolds, it is creating a large and fast-growing market that now attracts hospitals, insurers, start ups, and the largest technology companies in the world.
Analysts estimate that spending on AI in U.S. health care is already in the tens of billions of dollars each year and could grow severalfold over the coming decade, helped by rising digital health adoption and pressure to control costs. Although different firms publish different figures, they broadly agree on two ideas. First, the U.S. is one of the largest and most active markets for AI health tools today. Second, much of the growth is expected to come from software that supports clinical decisions, administrative workflows, and patient engagement rather than from futuristic robots or sci fi style applications.
At a global level, AI in health care is also expanding quickly, with estimates suggesting that worldwide revenue could reach well into the hundreds of billions of dollars by the early 2030s. The U.S. accounts for a significant share of this global activity because of its large health spending base, relatively advanced digital infrastructure, and concentration of leading technology and pharmaceutical companies. This means that many of the models tested in U.S. health systems are likely to influence how AI health tools are rolled out in other regions over time.
Within this landscape, a small group of technology and cloud companies has become especially influential. Industry research frequently cites Alphabet, Microsoft, and Amazon as three of the most important players in AI health infrastructure, since they provide the cloud platforms, data tools, and machine learning services that many hospitals and software vendors build on. These firms also invest directly in health specific products, from imaging analysis and clinical note summarization to patient facing chat tools. Their role is not only to sell software, but also to set technical standards for security, reliability, and integration that smaller companies must often follow.¹²
Amazon.com, Inc. (NASDAQ: AMZN) has recently taken a more visible step into this space through its primary care business One Medical. The company is rolling out an artificial intelligence health care service for One Medical members called Health AI, which is designed to sit between the patient and the rest of the care team as a first point of digital contact. In practical terms, the service uses large language models, together with information from a patient’s medical record, to answer questions, help manage medications, and handle routine tasks such as booking appointments.
The idea behind tools like Health AI is not to replace physicians, but to triage basic requests and provide clearer information before and after visits. Patients might use it to ask follow up questions that feel too minor for a phone call, to confirm instructions about a new drug, or to prepare for an upcoming consultation by organizing their symptoms in plain language. For clinicians, the hope is that these systems can reduce time spent on administrative work and repeated explanations, which are often cited as major sources of burnout in U.S. health care.
Amazon’s move arrives at a moment when model developers are racing to show that their systems can handle health related tasks safely. Earlier this month, both OpenAI and Anthropic announced new health care-oriented features, which are aimed at helping doctors and health organizations experiment with chat based tools under controlled conditions. These announcements show that the competition is no longer only about raw model capability. It is also about who can demonstrate strong safeguards, reliable performance on medical benchmarks, and compliance with privacy and health data rules.
For the broader industry, several questions remain open. Hospitals and insurers must decide where AI adds real value compared with simpler software, how to measure outcomes, and how to manage legal responsibility when a model contributes to a decision. Regulators in the U.S. are still refining how they evaluate learning systems that can change over time, while investors are trying to distinguish durable business models from short lived experiments.
The business of AI in health care is likely to grow more visible in the coming years, not only through large deals and product launches, but through small changes in how patients experience the system. A person who books appointments through a conversational interface, receives draft visit notes right after an exam, or gets reminders tailored to their medical history may not think of these as AI products, yet they are part of the same shift. For companies like Amazon, Alphabet, and Microsoft, the next phase will be about turning technical capability into trust, repeat use, and eventually, into a stable share of health care spending.
