Physician use of artificial intelligence in the United States has more than doubled in three years. The American Medical Association’s 2026 Physician Survey on Augmented Intelligence, fielded among 1,692 doctors in January and February, found that 81% now use AI in a professional capacity, up from 38% when the AMA first polled physicians on the technology in 2023. The average respondent reported 2.3 separate use cases.
Institutional adoption has followed the same curve. A survey of executives at 120 U.S. health systems by research firm Eliciting Insights found that 75% have implemented or plan to implement at least one AI solution, and half said their organization already runs three or more applications. Clinical note-taking leads the field at 68% adoption, with AI-based clinical documentation improvement close behind at an impressive 43%.
The question facing health system leaders is no longer if but when to deploy gen AI. All stakeholders need to make sure that the governance, validation, and workforce capability exist to run it at scale without introducing new categories of risk.
Where AI has already made real progress
Ambient documentation was the bottleneck unlocked. The Permanente Medical Group reported that its ambient AI scribes saved physicians roughly 15,791 hours of documentation time, with 84% of physicians reporting a positive effect on communication and 82% reporting improved work satisfaction.
Controlled research, though, paints a more measured outcome. A study of 1,800 clinicians across five academic medical centers from 2023 to 2025 found AI scribe users saved about 16 minutes of documentation time and spent 13 fewer minutes in the medical record for every eight hours of patient care, with adopters seeing roughly one additional patient every two weeks. Modest per-encounter savings still compound across a large ambulatory footprint, which is why systems keep on buying.
Patient communication is the second front, and the volume problem is significant. A large cross-sectional analysis found patient-authored portal messages rose 153% between 2020 and 2025, with messaging intensity among senders climbing from 2.2 to 5.4 messages per year. Drafting assistants embedded in the EHR now generate first-pass replies for clinicians to review.
Revenue cycle has become the clearest commercial case. An HFMA survey of 519 CFOs and revenue cycle leaders found 80% of health systems exploring, piloting, or implementing generative AI for RCM in 2025, a 38-point jump in under two years. More recent data from a second HFMA-partnered survey shows 37% of health systems using generative AI inside the revenue cycle, rising to 48% among large systems, with 45% applying AI to denial-related workflows.
The gap between a pilot and production
Deployment breadth remains thin. Deloitte’s 2026 Global Health Care Outlook reports that about 30% of surveyed health systems operate generative AI at scale in select areas, while just 2% have deployed it across the entire enterprise. Executives expect generative and agentic AI to consume 19% of technology budgets in the year ahead.
Four constraints explain the gap.
The first constraint is data privacy. Protected health information moving through model providers, gateways, and retrieval pipelines expands the attack surface for security teams. Physicians have noticed the same. Data privacy assurances were named critical to broader adoption by 86% of AMA respondents, and 88% pointed to robust safety validation.
There is regulatory and accreditation pressure as well. In June 2026, the Joint Commission launched its Responsible Use of AI in Healthcare certification, the first program of its kind built specifically for U.S. healthcare organizations. The standards are organized around five areas: governance, effective data management, risk and bias reduction, monitoring and validation of safety performance, and transparency, education and training. It follows initial guidance issued in 2025 and aligns with governance playbooks published by the Coalition for Health AI. Certification is voluntary today, which has an obvious impact on AI adoption.
Accuracy concerns in such a critical field are also slowing the pace of adoption. AI-generated drafts frequently introduced errors and extraneous details and often failed to ask relevant follow-up questions, tested across six commercial models. “We find that AI can sound like a doctor but not think like one,” said co-corresponding author Sarah Preum, an assistant professor of computer science at Dartmouth, in the study’s announcement. Co-author Tim Burdick, a family medicine physician, put the operational math plainly: heavy editing can cost more time than writing from scratch.
Finally, about 85% of AMA respondents said they want to be consulted or directly involved in AI adoption decisions, and 88% expressed concern about erosion of skills, particularly among physicians with fewer than 10 years of clinical experience. Oversight only works when the person doing it retains the expertise to catch what the model got wrong. This is a critical debate that has engulfed all major professions, including healthcare.
Training is a real bottleneck.
The most striking data on AI in healthcare this year is not about models. It is about people.
Incredible Health’s 2026 State of Nursing Report, drawing on a survey of 2,240 U.S. nurses, found that AI adoption among nurses nearly tripled in a single year while almost half of those using AI reported little or no time saved. The differentiator was preparation. Among nurses who received thoughtful AI training from employers, 24% saved over an hour a day, compared with 16% among those without training. Only 8% of nurses reported a clear AI strategy from their employer.
This is a clear training problem wearing a technology costume. Systems are buying tools faster than they are building the capability to use them, and the returns show it.
Accreditors have reached the same conclusion. Education and training are one of the five pillars of the Joint Commission’s certification standards, which require organizations to demonstrate education and training for staff on the health AI tools in use. Hospitals will need clinicians who can evaluate a model’s output, recognize where it is likely to fail, understand what happens to patient data downstream, and escalate appropriately when something looks wrong.
As generative AI continues to reshape healthcare delivery across the United States, institutions like Keuka College are playing an important role in preparing healthcare professionals with the digital literacy they need to navigate this rapidly evolving landscape. The demand signal is unambiguous: AI fluency is migrating from a specialty informatics skill toward a baseline expectation for nurses, allied health staff, and clinical managers alike.
What comes next
The numbers frame the problem without a forecast. Three-quarters of U.S. health systems have deployed or plan to deploy AI, while about 30% run generative AI at scale in any part of the organization and 2% run it enterprise-wide. The Joint Commission now asks certifying organizations to demonstrate role-specific education and training on the AI tools in use. Eight percent of nurses report a clear AI strategy from their employer, and 85% of physicians say they want to be consulted on adoption decisions.
Executives expect generative and agentic AI to take 19% of technology budgets in the year ahead. Whether a proportionate share goes to the people operating the tools is the open question.
