A recent JAMA study shows physicians who advertise AI use are seen as less competent and less trustworthy. The solution to this misperception lies in validation, transparency, and better communication.
Artificial intelligence tools are being introduced into healthcare workflows at an increasingly fast pace. Nearly 300 AI-enabled medical devices were approved by the FDA in 2025, a record-breaking year for such approvals. This quick cadence, combined with the novelty and uncertainty surrounding AI technology, is putting patients at a crossroads.
A recent survey published in JAMA Network Open found that Americans are less likely to trust physicians who advertise their use of AI, whether for administrative, diagnostic or therapeutic purposes, with their medical care. Patients perceived those physicians as less competent, trustworthy and less empathetic than those who made no mention of AI usage. Most strikingly, patients were less willing to book appointments with those physicians, with scores dropping from 3.61 (control) to as low as 3.15 when AI was mentioned in a therapeutic context.
This disconnect highlights a growing credibility gap: while health systems are investing in AI at a record pace, the public is growing wary of what it means for their care. That’s not just a perception issue. Trust directly shapes patient engagement, satisfaction, and outcomes. If left unaddressed, skepticism toward AI could undermine its potential to improve care at scale.
So how do we bridge the gap?
1. Distinguish clinical-grade AI from consumer tech
One reason for mistrust is that AI has become a blanket term covering everything from chatbot symptom checkers to FDA-cleared imaging software. Healthcare leaders need to draw clear distinctions: validated, clinical-grade AI that supports physicians is fundamentally different from consumer-facing tools designed for convenience.
Healthcare organizations need to establish a sharper public vocabulary around AI: clinical AI that has gone through regulatory review, validation, and deployment in high-stakes environments is not the same as consumer-grade tools optimized for speed or convenience. Making those differences explicit can reassure patients that what’s being used in their care meets regulated safety and quality standards..
2. Emphasize validation and outcomes
Patient mistrust is not irrational — it’s based on a history of overhyped health technology that had promised transformation but failed to deliver. AI must be presented differently. Clinical validation should be front and center, not buried in technical documentation. Peer-reviewed publications, real-world case studies, and transparent metrics, such as reductions in time-to-treatment or improved diagnostic sensitivity, should all be part of the patient-facing story.
For instance, imaging AI tools that are clinically validated in high-impact journals like NEJM are demonstrate measurable impact: faster identification of large vessel occlusions, quicker transfers to thrombectomy-capable centers, and ultimately improved patient recovery trajectories. Studies demonstrating meaningful improvements in clinical outcomes and workflow efficiencies help ground AI adoption in evidence rather than hype. Sharing outcomes like these, not just “AI is here” messaging, is what shifts patient perception from skepticism to cautious confidence.
3. Communicate the physician-AI partnership
The JAMA study suggests that some patients fear AI will replace human empathy. That perception isn’t unfounded — headlines often highlight AI “beating” doctors in tests, reinforcing the idea of competition rather than collaboration. In practice, the most successful deployments do the opposite: they allow doctors to spend more time with patients with the proper data to follow up on.
For example, administrative AI can reduce documentation burdens, giving clinicians more face-to-face interaction time. Diagnostic AI can serve as a second set of eyes, helping radiologists prioritize urgent cases without supplanting their judgment. These are not “AI versus physician” scenarios — they are “AI plus physician” solutions. Communicating that distinction is essential if patients are to see AI as a partner in their care rather than a substitute for human expertise.
4. Transparency as a trust-building strategy
Healthcare adoption of new technology has historically suffered from a lack of communication. Electronic health records are a classic example: introduced as a tool to improve coordination, they often left patients frustrated when physicians spent more time typing than listening. AI carries the same risk if its role remains opaque.
Transparency has to go beyond compliance checkboxes. Patients want clear, plain-language answers to questions such as: Why is AI being used in my case? How is my data protected? What benefits should I expect? Even a brief conversation, “this software helps me detect subtle changes on your scan that could help me decide if and when treatment is warranted,” reframes AI as a supportive tool rather than a black box.
The path forward
If implemented responsibly and communicated clearly, AI can become an invisible but essential layer of care, much like MRI or pulse oximetry once did. If health systems ignore the trust gap, adoption could stall, and patients could turn away from tools that are designed to help them.
The lesson from the latest JAMA study is clear: trust isn’t automatic. It has to be earned, deliberately and transparently. Healthcare leaders should treat trust-building as a core part of AI deployment, not an afterthought. The systems that succeed will be those that prove AI can make care not only faster and safer, but more human.
David Stoffel, M.D.
David Stoffel, M.D., has spent more than 20 years developing and commercializing innovative technologies and services in the medical device industry. He has an extensive track record of success in scaling healthcare businesses. Notably, he led marketing and corporate development at Intuitive Surgical, contributing significantly to establishing the Da Vinci surgical robotic system as a new surgical standard of care. He also helped launch and lead the Mobile Cardiac Telemetry business at iRhythm Technologies, one of the fastest growing digital health companies and was Chief Business Officer at Ceribell, maker of an innovative point-of-care EEG solution.
