AI Is Becoming a New Gatekeeper in Physician Choice
We know that AI is revolutionizing almost everything. Including how patients are searching for and interpreting physicians and their practices.
It’s a given that many patients search online to find a physician and learn about them before scheduling an appointment. They might visit the practice website. Hopefully find recent, authored articles about their condition there. Read reviews. Compare credentials. Look at photos. Watch videos. Check whether the physician accepts their insurance. Search the physician’s name on Google…
But now another influence is entering that decision. You guessed it! Patients today are asking artificial intelligence which physician they should see. They are turning to ChatGPT, Claude, Google AI Overviews, and other AI-powered tools to identify providers, compare options, interpret reputations, and decide whom to consider.
According to rater8’s 2026 Patient Choice Report, 47% of surveyed patients had used AI to research healthcare providers. That’s up from 31% in 2025. Clearly, AI-assisted physician discovery is no longer a distant possibility. It’s rapidly becoming, and will continue to be, a big part of how patients navigate care.
Today’s modern physician practice needs to proactively plan for this new reality.
But this growing reliance on AI introduces what I believe is a more complicated and critical question than whether a physician simply appears in a Google search:
What happens when an AI system becomes one of the gatekeepers between a patient’s healthcare need and the physician ultimately considered?
An AI Recommendation Is Not a Simple Search Result
Traditional search results usually present patients with a list of links, profiles, maps, reviews, and advertisements. The patient can open those sources, compare information, and form their conclusion. However, AI systems often do something different.
They synthesize information and present an answer. They may identify a physician, summarize the physician’s qualifications, characterize the practice, explain why the physician may be a good fit, or recommend several providers. And these answers can feel authoritative, even when the system has incomplete, outdated, conflicting, or unverified information.
This creates three separate very important challenges for physician practices today:
AI visibility: Does the system mention the physician?
AI accuracy: Are the physician’s name, specialty, location, practice affiliation, and other details correct?
AI verifiability: Can the system corroborate its recommendation through credible, current sources?
Note that these are related but they are not interchangeable. For example, a physician can be visible but described inaccurately. The information can be accurate but difficult for the system to verify. A highly qualified physician can also be omitted entirely because the AI system cannot confidently connect that physician to the patient’s request.
You see the challenge: Appearing in an AI-generated answer is not enough. The physician must be identifiable, understandable, accurate, and verifiable.
Let’s break these three areas down bit.
AI Visibility: Will the Physician Be Considered?
The first challenge is the probably the most basic and straightforward. When a patient asks for a physician in a particular specialty, condition area, or geographic market, does the AI system mention the practice at all? Recent research suggests that inclusion should not be assumed.
An International Journal of Medical Informatics study tested ChatGPT with 40,500 prompts requesting recommendations for orthopedic surgeons. ChatGPT declined to provide a recommendation in 52.8% of its responses.
The likelihood of receiving a recommendation also varied based on the patient persona included in the prompt. Income, insurance status, and location were among the strongest predictors of whether ChatGPT provided a surgeon recommendation. Prompts involving higher-income patients and patients with insurance were more likely to receive recommendations, while results also differed by city.
That finding matters for practices because it shows that AI visibility is not a simple reflection of clinical quality. A physician may be highly qualified, respected by peers, and well suited to a patient’s needs, yet still be absent from an AI response. The system may lack enough accessible information, fail to connect the physician with the requested specialty or location, or decide not to provide a recommendation at all.
As you can see, clinical excellence does not automatically create AI visibility. And a physician’s longevity, word-of-mouth recognition, or a strong reputation within the local medical community matter nothing to AI. AI systems can only work with the information they retrieve, recognize, connect, and trust.
AI Accuracy: Is the Physician Being Represented Correctly?
Being mentioned creates a second, more complicated challenge:
Is the physician identified correctly?
In the orthopedic-surgeon study, researchers manually coded a sample of 1,000 responses. Only 44.6% of the recommended surgeons were determined to be valid. A separate September 2026 audit examined AI-generated provider recommendations across the 100 largest metropolitan areas in the United States and compared those recommendations with official registries.
Without web search, only 4% of doctors recommended by an open-weight model matched a registered clinician in the requested city. For a proprietary model operating without web search, the match rate was only 11%. Wow!
Those numbers do not merely describe an obscure technical limitation. They demonstrate a serious problem for patients and physicians today. An AI system can generate a convincing physician recommendation that does not correspond to a registered clinician in the requested market.
It can also confuse two physicians with similar names. It can associate a physician with an outdated location. It can combine details from different providers. It can misunderstand a specialty, practice affiliation, or area of expertise. It can present information with confidence even when the underlying identity is wrong. And the risk is not limited to fabricated physicians.
A real physician can also be represented inaccurately because the digital record surrounding that physician is fragmented. The practice website may list one address while a directory lists another. A hospital profile may use an outdated biography. A medical directory may identify an old practice affiliation. A Google Business Profile may use a different version of the practice name. Specialty information may be broad in one source and highly specific in another.
To a patient, these may look like inconsistencies, which is harmful enough. But to an AI system attempting to determine whether multiple references describe the same physician, those inconsistencies can weaken confidence and create confusion. Not good!
AI Verifiability: Can the Recommendation Be Corroborated?
Now the third challenge is probably be the most important:
Can the AI system verify what it is saying?
The September 2026 registry-backed audit compared recommendations generated without live internet retrieval with recommendations produced after the AI system was allowed to search current web sources. When the system could retrieve information from the web before answering, valid-provider match rates improved to between 64% and 71%.
That is a dramatic improvement over the 4% and 11% match rates produced without web search. It suggests that access to current external sources can make AI-generated recommendations considerably more reliable. On the other hand, it also reinforces an important distinction:
An AI system generating an answer from its internal model is not performing the same task as a system retrieving and checking current sources. And patients may not recognize that distinction. An answer produced without live retrieval can sound just as polished and confident as one grounded in current information….and no one is the wiser.
Reviewing the audit more closely, I learned that the researchers noted that an answer created without retrieval may provide no visible indication that its recommendations were never verified. That is exactly why AI verifiability matters, a lot!
A physician’s information should not exist in only one location. The goal, and it’s a critical one, is a digital identity that can be corroborated across multiple credible sources that consistently confirm the physician’s identity, qualifications, specialty, geography, and practice affiliation.
Online Reputation May Also Shape Who AI Recommends
Accuracy and verifiability are only the tip of the iceberg. AI systems may also favor providers with stronger digital visibility and more substantial online reputations.
The rater8 survey found that a whopping 75% of respondents would not book with a physician rated below four stars, 55% had avoided or canceled an appointment because of online reviews, and 66% said a provider’s responses to reviews influenced their trust. These numbers point to an important shift:
Online reputation is no longer confined to review websites.
It may influence patient decisions directly, appear inside search summaries, contribute to the information AI systems retrieve, and shape how a physician is characterized during AI-assisted discovery. And here’s the concerning part: A physician may earn a referral and still lose the patient during digital validation. Why? Because now the physician may also be excluded before the patient ever sees a traditional search result.
Physician Practices Really Need a Corroborated Digital Identity
Practices cannot control every AI response. Of course they can’t! They also can’t guarantee that a particular model will mention a physician, reproduce every detail correctly, or recommend the practice for a specific query. Don’t worry about things you can’t control, right? But practices can definitely control and strengthen the digital foundation from which those systems draw conclusions. And that begins with consistency across the sources patients and AI systems are most likely to encounter:
The practice website
Google Business Profile
State medical-board records
Hospital and health-system listings
Specialty-association profiles
Professional directories
Insurance directories
Physician biographies
Location and contact listings
Authoritative articles, interviews, and publications
This is not simply an SEO exercise. That would be easy! It’s the intentional work of building a digital identity that patients can understand and AI systems have a better chance of interpreting and corroborating.
The Modern Digital Front Door Now Serves Two Audiences
A practice’s Digital Front Door has traditionally been discussed as the online experience patients encounter before calling, scheduling, or arriving. That remains true and relevant today. But as we can see, the Digital Front Door increasingly serves another audience: the systems interpreting the practice on the patient’s behalf.
Things like how a website is structured, how physician biographies are written, service explanations, location information, reviews, professional profiles, and external references all matter in how AI understands the practice….and, in turn, perhaps a potential patient.
AI Is Changing the Meaning of Discoverability…And Practices Must Take Heed
Not too long ago, discoverability once meant appearing in search results. Those days are over. Discoverability now includes whether an AI system can identify the physician, distinguish that physician from others, describe the practice accurately, connect the physician with the appropriate specialty and location, and support its answer with credible information.
That creates a new responsibility for independent and physician-led practices. They must pay attention today not only to whether they are visible, but also to what the digital ecosystem says about them and whether those signals are accurate!
AI visibility without accuracy can mislead. AI accuracy without verifiability may not earn trust.
In today’s world, practices increasingly need all three!
DeGobbi Practice Lighthouse helps independent and physician-led practices evaluate and strengthen the digital identity AI systems and patients encounter. We clarify positioning and physician expertise, improve website and Digital Front Door content, assess the consistency of key profiles and listings, identify inaccurate or conflicting information, strengthen patient-trust signals, and conduct practical AI visibility and accuracy checks.
The purpose is not to manipulate AI recommendations or promise placement. Nobody can do that. But we do have the expertise to make the practice’s value, identity, expertise, and location clearer, more consistent, more credible, and easier for both patients and emerging discovery tools to verify.
