AI Is Quietly Becoming Medicine’s New Referral Engine
For concierge and membership-based medicine, the most important application of AI today isn’t diagnosis or clinical decision support. It’s patient matching. AI is quietly becoming an always-on referral engine — one that evaluates physician fit across dozens of dimensions based on your digital footprint, then routes the right patient to the right practice for the right reasons.
By Michael Tetreault, Editor-in-Chief
Concierge Medicine Today | Est. 2007
The Industry’s Independent Trade Publication
Whenever I ask a physician, “Who is your ideal patient?” — or more simply put, “Who do you serve best?” — the response is almost immediate.
They usually smile, sit up in their chair and lean in closer. They great physicians I know, understand with precise detail their target audience.
They get specific.
They know exactly who they’re built to care for.
So, what’s changed?
Well, AI is now answering that same question — from the patient’s perspective.
And it’s using that answer to match patients to physicians in your area (and mine) with a level of precision we haven’t seen before.
Only a few physicians I encounter may disagree with that patient use of AI and that's okay. For the majority of you, let me share a true story that happened to one of your peers just this month.
One concierge physician shared something on LinkedIn that’s hard to ignore:
“Guess how my last three patients found me? Not through a referral. Not through Google. Through AI.” ~Concierge Medicine Physician, Arizona, LinkedIn, March 2026
That observation sparked immediate discussion. My enthusiastic and supportive response was pretty simple:
“I love this! AI is doing something directories and Google never could. It’s matching on fit — expertise, specificity, credibility — and it’s only getting better as patients refine how they prompt it. For concierge medicine especially, that’s everything. The whole model depends on the right patient finding the right doctor. This may be the first technology that actually makes that happen at scale.” ~Editor-In-Chief, Concierge Medicine Today, LinkedIn, March 2026
The physician replied with a line worth remembering and putting into practice (pun intended):
“The referral pathway has changed. The preparation for it hasn’t.”
This article is about AI use among patients, how it will impact your practice and what you can do to prepare for the months and years ahead.
Part One: A New Referral Pathway Is Already Operating
For decades, the concierge medicine referral pathway worked like this: a trusted colleague mentioned your name, a satisfied patient told a friend, or someone searched Google and landed on your website. These pathways rewarded visibility, proximity, and volume. The best-positioned practice — not necessarily the best-fit practice — won the patient.
AI changes the fundamental logic of that system.
When a prospective patient today types a detailed query into an AI —
“I’m a 48-year-old executive with a history of cardiovascular disease, chronic stress, and frequent international travel. I want a concierge physician in Atlanta who specializes in preventive medicine and longevity” —
Now, the AI does not return a directory listing. It synthesizes.
It reads published content, professional profiles, patient reviews, specialty credentials, documented clinical philosophy, and online positioning across multiple platforms simultaneously. It filters for fit. And no, your web site isn't the only thing it looks at, it's just one of many sources it's looking into.
This is what the concierge physician observed in that LinkedIn post: AI matched three patients not because the physician had the most visibility, but because the physician had the clearest, most specific, most documented record of who they are and who they help.
Vague positioning does not synthesize well. Specificity does.
In fact, did you know that in 2024, approximately 37% of consumers reported using generative AI tools for health and wellness purposes, including to research medical conditions and identify potential providers.¹ Among Americans aged 18 to 34, 80% have embraced AI healthcare solutions, according to a PwC Healthcare Survey.² One in four Americans say they would not choose a healthcare provider who refuses to adopt AI technology.³
These are not future projections. They are current behavior. And, they're only gaining momentum.
Part Two: What AI Is Already Doing in Healthcare — and the Data Behind It
Before addressing why physicians at-large (not just concierge medicine physicians) have been slow to embrace AI and where adoption is accelerating, it is worth establishing what AI is already doing in clinical practice — because the data reveals a market moving considerably faster than the conversation suggests.
Physician Adoption Has Nearly Doubled in One Year
According to an American Medical Association survey of 1,183 physicians conducted in November 2024, 66% of U.S. physicians reported using healthcare AI in 2024 — a 78% jump from the 38% who said the same in 2023. The AMA described this as “unusually fast” clinician adoption.⁴ The non-user population collapsed even more dramatically: in 2023, 62% of physicians said they did not use AI in any form. By 2024, that number had fallen to 33%.⁴
What stands out for me in that data... The AMA described this as “unusually fast” clinician adoption.⁴
Unusually fast?!
Furthermore, a Doximity study released in March 2026, drawing on survey responses from 3,151 U.S. physicians across 15 specialties, found AI use had risen from 47% of physicians in April 2025 to 63% in January 2026 — a 16-point increase in under a year. Among AI users, 75% reported that AI had already reduced their administrative workload and improved job satisfaction. Literature search was the most common use case (35%), followed by voice-based documentation and ambient listening tools (29%).⁵
The AMA survey identified the top physician uses of AI in 2024: documentation of billing codes, medical charts, and visit notes (21%, up from 13%); creation of discharge instructions, care plans, and progress notes (20%, up from 14%); translation services (14%); summaries of medical research and standards of care (13%); and assistive diagnosis (12%).⁴ These are primarily administrative and workflow applications — which is exactly where adoption begins, and exactly where the most immediate return on investment lives.
The Market Behind the Momentum
The AI healthcare market grew to $32.34 billion in 2024 and is projected to reach $431.05 billion by 2032, according to Global Market Insights.⁶ The U.S. FDA had authorized approximately 950 AI-enabled medical devices as of August 2024, with 77% concentrated in radiology.⁷ Eighty percent of hospitals now use AI in some form to enhance patient care and workflow efficiency, according to Deloitte’s 2024 Health Care Outlook.⁸ And 92% of healthcare leaders believe generative AI improves operational efficiency, with 65% identifying it as a tool for faster clinical decision-making.⁸
For concierge medicine and membership-based practices, the most relevant application is not diagnostic AI or clinical decision support. It is the patient-matching function described by the concierge physician whose post opened this article — AI as a sophisticated, always-on referral engine that evaluates physician fit across dozens of dimensions simultaneously and routes the right patient to the right practice.
Part Three: Why Physicians Are Resistant — and Why the Resistance Is Reasonable
Physician resistance to AI is real, documented, and in many respects, intellectually defensible. Understanding it is essential for anyone making the case for adoption inside a practice or a health system.
The Concerns Are Structural, Not Irrational
A 2024 Statista survey found that 63% of respondents cited data security and privacy risks as a major concern in implementing healthcare AI.⁶ A 2024 Canadian physician survey found that only 21% of physicians were confident about AI and patient confidentiality — with 79% either not confident or unsure.⁹ A peer-reviewed analysis in the Journal of Medical Internet Research (2025) identified the primary barriers to physician AI adoption as: data integrity and privacy concerns, lack of clinical validation, underdeveloped digital infrastructure, liability uncertainty, and insufficient training.¹⁰
Researchers at ScienceDirect found in 2025 that a significant number of healthcare professionals remain distant from AI due to concerns that it may threaten their professional roles or reputations. The same research emphasized that clearly defining the relationship between physicians and AI — as assistant, not replacement — is a prerequisite for broader adoption.¹¹ Meanwhile, 40% of physicians believe AI is overhyped and will not meet high expectations, according to a Forrester 2024 survey.⁶
The liability question is particularly acute as well. When a physician relies on an AI-assisted diagnosis that proves incorrect, the question of accountability is genuinely unresolved in most U.S. legal and regulatory frameworks. Until those frameworks mature, physician caution reflects professional self-preservation as much as technological skepticism.
The Administrative Wall
There is a second, more practical dimension to physician resistance: the implementation burden. EHR integration has historically been one of the most disruptive forces in physician workflow, and many physicians carry a form of technology trauma from prior implementation failures. Adding AI on top of systems that were not designed for it — or without adequate training, feedback channels, or institutional support — is perceived not as relief but as additional administrative weight.
For example, we spoke with a chiropractor just last week and how AI in her practice is saving giving her more time to spend with more patients. She uses Rheo, an AI assistant built into ChiroTouch that helps chiropractors handle patient intake, documentation, and compliance automatically. She noted that it reduces her administrative workload, time spent in front of a monitor or at her desk doing keystrokes, saves her more time (giving her hours back, not minutes), and keeps physicians focused on patient care — all within the EHR they already use.
Pretty neat, eh?!
But, the Advisory Board identified in its 2025 analysis of the AMA survey that physicians would increase AI adoption significantly if provided: a designated feedback channel for reporting AI errors or concerns; data privacy assurance; EHR integration; and proper training and education before deployment.¹² These are not demanding conditions. They are basic implementation hygiene. The problem is that most institutional deployments skip them.
Today. only about 12% of physicians currently rely on AI for diagnostic help, according to a Georgia Tech researcher who studies AI and healthcare analytics — even as AI use for administrative tasks has surpassed 20%.¹³ The gap between administrative adoption and clinical adoption is not a gap in technology. It is a gap in trust — and trust is built incrementally, with evidence and transparency, not mandated top-down.
Part Four: Where Adoption Will Come First — and Why Physicians Will Eventually Embrace It
The trajectory of AI adoption in medicine follows a pretty predictable pattern — one that has already played out with every major technology shift in healthcare, from EHRs to telemedicine to online scheduling. Adoption begins where the pain is most acute, the return is most immediate, and the risk is most manageable. It then expands.
Stage One: Documentation and Administrative Relief (Already Underway)
The first and fastest wave of AI adoption in medicine is administrative. Ambient listening tools and AI scribes — from platforms like Nuance DAX Copilot, Abridge, Suki, and Augmedix — listen during patient visits and automatically draft clinical notes. More than 20% of physicians already use AI for documentation tasks, and voice-based documentation adoption rose from 20% to 29% of physicians between April 2025 and January 2026, according to the Doximity study.⁵ Epic Systems, which holds approximately 40% of the U.S. hospital EHR market, announced in mid-2025 an AI overhaul that embeds ambient scribes, clinical co-pilots, and patient-facing chatbots directly into its platform.¹⁴
For concierge physicians specifically, this is the most immediate opportunity. The promise of the membership model — more time with patients, less time on paperwork — is what patients pay for and what physicians entered the model to deliver. AI documentation tools restore that time -- like the chiropractic provider example I described above from just last week. Physicians who adopt them are not compromising the relationship. They are protecting it.
Stage Two: Diagnostic Support and Pattern Recognition (Emerging)
The second wave — AI as a clinical decision support tool — is moving more slowly but steadily. The FDA had authorized 950 AI-enabled medical devices as of August 2024, with radiology leading all specialties.⁷ Hospitals are deploying AI to project patient health trajectories and flag high-risk cases before they escalate. A 2024 Health Affairs study found that 61% of hospitals were examining their predictive AI models for accuracy, and 44% were reviewing them for bias — evidence that the governance frameworks necessary for clinical trust are being built.¹⁵
For the concierge physician whose practice is built around prevention and early detection, AI-driven pattern recognition across a patient’s longitudinal health data — flaging inflammation markers, identifying cardiovascular risk trajectories, connecting symptom clusters across visits — will become a competitive differentiator. Not a replacement for clinical judgment, but an amplifier of it.
Stage Three: Patient Matching and Referral Intelligence (The Emerging Frontier)
The third wave — the one the concierge physician’s LinkedIn post described earlier (above) — is AI as a patient-to-physician matching engine. This is the application most directly relevant to membership-based and direct-pay practices, and it is the one that will reshape the economics of patient acquisition for the concierge model over the next several years.
As the Arizona concierge doctor observed, the mechanism is straightforward: AI reads a physician’s entire published record — articles, posts, videos, podcast appearances, documented clinical philosophy, specialty credentials, and patient-facing content — and synthesizes it into a profile of fit. A patient who prompts an AI with a detailed, specific set of needs gets a highly targeted match. Vague positioning produces vague results. Specific, documented, differentiated positioning produces precise matches.
The practical implication: every piece of content a concierge physician publishes is now a data point in the AI matching algorithm. The articles, the podcast interviews, the LinkedIn posts, the documented point of view — AI is indexing all of it. Physicians who have been specific, consistent, and visible in their content will be found by the patients who fit them best. Those who have not will be invisible to a referral pathway that is already operating at scale.
Why Physicians Will Eventually Come Around
The path from resistance to adoption follows the evidence. Physicians who have used AI scribes report spending more time with patients and less time on paperwork. The 75% of AI-using physicians in the Doximity study who reported reduced administrative burden and improved job satisfaction are not describing a technology transaction — they are describing a recovery of what drew them to medicine.⁵ That is a powerful recruiting argument for adoption, and it is already spreading through physician networks organically.
Doximity’s Chief Clinical Experience Officer, Amit Phull, MD, framed the future clearly: “Doctors see its potential to reduce administrative burden, improve job satisfaction, and expand time with patients. But the future of AI in medicine will depend on accuracy, transparency, and strong physician leadership. Real physician involvement in the development and deployment of AI will be key to unlocking its value in healthcare.”⁵
Physicians are not resistant to tools that make their work better. They are resistant to tools that are imposed on them without training, without transparency, and without their input in the design. The practices and health systems that bring physicians into AI development and deployment — rather than deploying AI at physicians — will see adoption. History with every prior healthcare technology confirms this.
Part Five: What This Means for Concierge and Membership-Based Medicine Right Now
The concierge and membership medicine model has always been built on a premise that the broader healthcare system struggles to deliver: the right physician for the right patient, with enough time to do the work properly. AI is now making that match at scale — without a referral, without a directory, and without the geographic and network constraints that have historically limited patient acquisition for independent and boutique practices.
The practices that will benefit most are those that have already done the work as the Arizona concierge physician described: documented a specific clinical philosophy, established a visible and consistent content record, and built a digital presence that reflects who they are and who they help best. The AI does not invent the match. It reads what you have already made visible and routes accordingly.
For practices that have not yet made that record visible, the work is not complicated. It is consistent. Articles, posts, podcast appearances, documented clinical perspective, and a clear articulation of the patient population you serve best — these are not marketing tactics. In the age of AI-driven referral, they are your referral network. The referral pathway has changed. The preparation for it hasn’t. That is both the challenge and the opportunity.
Editor’s Note: This article is intended for informational and educational purposes for physicians, practice administrators, and healthcare professionals. This content does not constitute legal, financial, medical, or compliance advice. AI tools and platforms referenced are named for illustrative purposes; Concierge Medicine Today does not endorse specific vendors or products.
Sources & Citations
TempDev / Keragon. (2024–2025). AI in Healthcare Statistics. Citing: approximately 37% of consumers used generative AI for health and wellness purposes in 2024. https://www.tempdev.com/blog/2025/05/28/65-key-ai-in-healthcare-statistics/ and https://www.keragon.com/blog/ai-in-healthcare-statistics
PwC Healthcare Survey 2024. Cited via Docus AI Healthcare Statistics: 80% of consumers aged 18–34 have embraced AI healthcare solutions; less than 60% of those over 55 willing to use them. https://docus.ai/blog/ai-healthcare-statistics
Tebra. (2023). Perceptions of AI in Healthcare: What Professionals and the Public Think. Key finding: 1 in 4 Americans would not choose a healthcare provider who refuses to embrace AI technology. Cited via TempDev. https://www.tempdev.com/blog/2025/05/28/65-key-ai-in-healthcare-statistics/
American Medical Association. (February 2025). “2 in 3 Physicians Are Using Health AI — Up 78% from 2023.” Survey of 1,183 physicians, November 2024. Key findings: 66% of physicians using AI in 2024, up from 38% in 2023; 78% jump in usage; non-user population fell from 62% to 33%; top uses: documentation (21%), discharge summaries/care plans (20%), translation (14%), research summaries (13%), assistive diagnosis (12%); 35% say enthusiasm exceeds concerns; 68% recognize AI has at least some advantage in patient care. https://www.ama-assn.org/practice-management/digital-health/2-3-physicians-are-using-health-ai-78-2023
Doximity, Inc. (March 2026). Study: Physicians Rapidly Adopting AI, But Accuracy Concerns Persist. Survey of 3,151 U.S. physicians across 15 specialties, March–April 2025 and November 2025–January 2026 cohorts. Key findings: AI use rose from 47% (April 2025) to 63% (January 2026); 75% of AI users report reduced administrative burden and improved job satisfaction; literature search is top use (35%); voice-based documentation rose from 20% to 29%; quote from Amit Phull, MD, Chief Clinical Experience Officer. https://www.businesswire.com/news/home/20260317453665/en/Doximity-Study-Finds-Physicians-Rapidly-Adopting-AI-But-Accuracy-Concerns-Persist
Multiple sources: Global Market Insights 2024 (AI healthcare market $32.34B in 2024; projected $431.05B by 2032); Statista 2024 (63% cite data security concerns; 40% of physicians believe AI is overhyped [Forrester 2024]; 68% of U.S. adults fear AI will weaken patient-provider relationships); PwC and HealthEdge data. Cited via Docus AI and Keragon. https://docus.ai/blog/ai-healthcare-statistics and https://www.keragon.com/blog/ai-in-healthcare-statistics
U.S. Food and Drug Administration. As of August 2024, the FDA had authorized approximately 950 AI-enabled medical devices; 77% in the field of radiology. Cited via NCBI Bookshelf (2025 Watch List: Artificial Intelligence in Health Care) and TempDev. https://www.ncbi.nlm.nih.gov/books/NBK613808/ and https://www.tempdev.com/blog/2025/05/28/65-key-ai-in-healthcare-statistics/
Deloitte. (2024). Health Care Outlook. Key findings: 80% of hospitals use AI to enhance patient care and workflow efficiency; 92% of healthcare leaders believe generative AI improves operational efficiency; 65% see it as a tool for faster decision-making. Cited via Docus AI Healthcare Statistics. https://docus.ai/blog/ai-healthcare-statistics
Canadian Institute for Health Information (CIHI) / NCBI. (2025 Watch List: Artificial Intelligence in Health Care). Citing a 2024 survey of Canadian physicians: only 21% confident about AI and patient confidentiality; 79% either not confident or unsure. https://www.ncbi.nlm.nih.gov/books/NBK613808/
Heinrichs, H. et al. (2025). “Physicians’ Attitudes Toward Artificial Intelligence in Medicine: Mixed Methods Survey and Interview Study.” Journal of Medical Internet Research. Key finding: primary barriers to physician AI adoption include data integrity and privacy concerns, lack of clinical validation, underdeveloped digital infrastructure, liability uncertainty, and insufficient training. DOI: 10.2196/74187. https://www.jmir.org/2025/1/e74187
ScienceDirect / Artificial Intelligence in Medicine. (2025). Position of Artificial Intelligence in Healthcare and Future Perspective. Key finding: a significant number of healthcare professionals remain distant from AI due to concerns that it may threaten their professional roles or reputations; clearly defining the physician-AI relationship is a prerequisite for adoption. https://www.sciencedirect.com/science/article/abs/pii/S0933365725001289
Advisory Board / American Medical Association. (2025). Analysis of AMA physician AI survey. Key finding: physicians identified four conditions that would increase adoption — designated feedback channel, data privacy assurance, EHR integration, and proper training. https://www.advisory.com/daily-briefing/2025/02/17/ai-use
Georgia Tech. (July 2025). “AI in Healthcare Could Save Lives and Money — But Change Won’t Happen Overnight.” Analysis by professor and researcher studying AI and healthcare analytics. Key finding: only approximately 12% of physicians currently rely on AI for diagnostic help, even as administrative use exceeds 20%; transition will be incremental. https://www.gatech.edu/news/2025/07/11/ai-healthcare-could-save-lives-and-money-change-wont-happen-overnight
STAT News / Epic Systems. (August 2025). Epic EHR announces AI overhaul featuring ambient scribes, clinical co-pilots, and patient-facing chatbots embedded directly into the Epic platform. Epic holds approximately 40% of the U.S. hospital EHR market. Cited via IntuitionLabs AI in Hospitals analysis. https://intuitionlabs.ai/articles/ai-adoption-us-hospitals-2025
Health Affairs. (2024). Study on hospital use of predictive AI: 61% of hospitals examining predictive models for accuracy; 44% examining for bias. Cited via Chief Healthcare Executive and IntuitionLabs. https://www.chiefhealthcareexecutive.com/view/two-out-of-three-doctors-say-they-re-using-ai
Brook Choulet, MD, FAPA. Comment posted on LinkedIn, March 2026, in response to a post by a concierge physician reporting that her last three patients found her through AI rather than through referrals or search engines. Quoted with context. Dr. Choulet is a board-certified psychiatrist and Fellow of the American Psychiatric Association.
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