For most of my life, I have experienced medicine from both sides of the exam table. As a patient, I spent years on Social Security disability and accumulated diagnoses across specialties. At one point, I had my brain electrocuted twenty times for what ultimately turned out to be an infection. Eventually, a primary immunodeficiency helped explain why so many unrelated things had happened to me.
I went on to train more than 70,000 practitioners (MDs, DOs, NPs, PAs, DCs, NDs, and RNs) and build an eight-figure health education company that ranked No. 150 on the Inc. 5000. I think medicine is approaching a bigger disruption than the debate over whether AI will replace doctors.
That is the wrong question. AI is changing what patients will expect their practitioners to know.
Modern medicine was built around population medicine. Guidelines, specialties, ICD-10 codes, CPT codes, and third-party reimbursement let us deliver care at scale.
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But there has always been another patient: the person with multiple chronic diagnoses, subclinical disease, symptoms that predate formal criteria, or five specialists treating five pieces of what may be one larger problem. Many migrated toward concierge, integrative, functional, and other private-pay models, where there was room for an N-of-1 investigation.
Individualized synthesis was expensive because human cognition was expensive. Reading years of records takes time. Meanwhile, the complex patient is becoming ordinary. A 2025 Centers for Disease Control and Prevention analysis found that 76.4 percent of U.S. adults had at least one of 12 chronic conditions and 51.4 percent had two or more. Add an aging population, long COVID, dysautonomia, autoimmune disease, and rare diseases we are getting better at identifying, and the complicated patient no longer looks like an outlier.
AI changes the constraint. Advanced models can review years of records, search current literature, generate competing hypotheses, and recognize patterns beyond what even the smartest practitioner could reliably hold in working memory. Research using real emergency-department cases has already shown AI performing at or above physician comparators on some diagnostic reasoning tasks.
But another study may tell us more. Physicians given GPT-4 did not significantly outperform physicians using conventional resources. GPT-4 alone performed substantially better.
Access to intelligence is not the same thing as knowing how to use it. Right now, output is heavily determined by input. Two practitioners can use the same model and get radically different results depending on what they provide and what they ask.
Prompt engineering is the issue du jour because the user is still being asked to do too much. We are not going to turn hundreds of thousands of practitioners into expert prompt engineers. The prompt engineering has to be built into the tool.
I know how difficult that is because I have spent the past year and more than $1 million building a clinical AI system intended to do exactly that. The tool has to control input and output: gather the right information, recognize what is missing, challenge incomplete reasoning, and force contradictions back into view. Eventually, prompt engineering itself should become passé.
Patients are not waiting. One in four U.S. adults now reports using AI chatbots to help determine what is causing symptoms. The American Medical Association reports that 81 percent of physicians now use AI professionally.
Patients are going to ask why, and they are going to expect a real answer. Why did this happen? Why are these diagnoses being treated separately? What are we missing? “We don’t know” will increasingly need to mean that we have pushed as far as today’s evidence can take us, not that no one had the time or tools to look.
AI cannot read the room. It cannot put its hands on the patient. It does not reliably know that one person under-reports symptoms while another over-reports them, or whether a treatment plan has any chance of being followed.
Good practitioners do something I call biohacking human behavior. They understand not only what should work biologically, but what can work for the person sitting in front of them.
That is the bionic practitioner: human judgment, pattern recognition, and the ability to understand another person, amplified by computational memory and synthesis that can see relationships beyond what even the smartest unaided practitioner could consistently recognize.
My background makes it impossible for me to reduce this to conventional medicine versus alternative medicine. I have worked with thousands of patients and tens of thousands of practitioners in individualized medicine, but conventional intervention also kept me alive.
I was a young woman when disability put me on Medicare. One of my treatments approached $1 million a year. I would not have made it without that medical intervention. I also got off disability twice and went on to build an eight-figure company.
That is an uncommon résumé, and it is why I have little patience for the idea that either side has all the answers. The future has to be an “and.”
That is where the economics become uncomfortable. Private insurers are businesses. Medicare faces an aging, increasingly complex population. Better diagnostics will identify more rare and complex disease, while some therapies already cost millions.
We are going to get better at finding what is wrong. The harder question is what we do once we find it, and who pays.
My hypothesis is that reimbursement will become more restrictive. Finding more answers does not make those answers cheaper to treat.
Private practice faces the problem from the other direction. Finding a complex condition outside the insurance system does not mean it can be treated there. The patient may still need a hospital, specialist, surgery, biologic, or advanced therapy.
I don’t know exactly what traditional medicine, private practice, hospitals, and insurance will look like on the other side of this transition. But I think we already know what patients will expect: a practitioner with access to the accumulated knowledge of medicine, who knows their history, recognizes what does not fit, keeps asking why, and uses extraordinary technology without surrendering judgment to it.
We are spending too much time asking whether AI will replace the doctor. The more important question is what happens when patients discover what a doctor amplified by AI can become.
They are already discovering it. Soon, they will expect it everywhere.
Brandy Zachary, known as Dr. Z, is a chiropractic physician, an Institute for Functional Medicine Certified Practitioner, and the founder and CEO of TDZ Functional Medicine Academy (TDZ-FMA), LabDX, and DiagnoseThis. She built TDZ-FMA with no outside investment into a 2026 Inc. 5000 company with 2,095 percent three-year growth, and she is the inaugural Gold Stevie Award winner for Best Female Entrepreneur.
Zachary graduated cum laude from the Honors College at Loyola Marymount University with a degree in communications and earned her Doctor of Chiropractic degree in California in 2001, graduating magna cum laude as salutatorian. After reactivating her chiropractic license, she opened Body Love Cafe in Walnut Creek, California, in fall 2016. The award-winning holistic health clinic, with several practitioners, earned Diablo Magazine’s Best of the East Bay recognition across multiple years during her ownership.
She is the lead instructor for TDZ-FMA, which is jointly accredited through bodies including the Accreditation Council for Continuing Medical Education and the American Nurses Credentialing Center, and she developed the academy’s FMACP certification. She has taught for or been interviewed by the Institute for Functional Medicine, Rupa Health, Designs for Health, 3×4 Genetics, and SIBO SOS. She also knows the health care system as a patient, having traveled to Princeton, Stanford, Harvard, and the University of California, San Francisco, in pursuit of her own diagnosis.
Zachary is the author of eight books, six of them Amazon No. 1 bestsellers. They include How to Read a Client from Across the Room (McGraw-Hill, 2012), which won a Bronze Axiom Business Book Award and introduced the Character Code System; Middle-Aged Mama in a Muumuu, a marketing and business playbook for practitioners; and A Practitioner’s Guide to Mastering Functional Medicine Lab Values, a four-volume series used as required reading in other functional medicine training programs. Her writing has also appeared in Entrepreneur, and she hosts the Functional Medicine 2.0 Podcast. She shares updates from the academy on Instagram.
