Understanding the nutrient deficiency connection: Many of us who have microscopic colitis (MC) have noticed increasing symptoms such as flushing, headaches, itching, rash, hives, or worsening diarrhea after eating aged cheeses, fermented foods, or leftovers. These symptoms are almost surely a result of reactions triggered by high-histamine foods (or foods or medications that cause our body to release histamine). Understanding why this happens, and why it's rarely a single-cause problem. is necessary for effective management. How histamine balance works: Histamine balance in our body depends on three factors:
When any of these factors shifts unfavorably, histamine can accumulate and cause symptoms. For most people, the body handles dietary histamine efficiently through an enzyme called diamine oxidase (DAO), which is produced primarily in the intestinal lining and breaks down histamine in the gut before it can enter circulation. However, in MC patients, this system frequently becomes impaired, not because of a single defect, but through multiple overlapping problems involving nutrient deficiencies, gut damage, and ongoing losses due to chronic diarrhea. The key nutrient deficiencies and how they drive histamine problems: Magnesium stabilizes mast cells, preventing them from releasing histamine inappropriately. It also supports enzyme systems broadly and helps regulate nervous system excitability. When magnesium is deficient (which is extremely common in MC due to chronic diarrhea), mast cells become hyperreactive, releasing more histamine in response to normal triggers. This acts upstream t the release side of the equation. More histamine is being dumped into our system, even before considering dietary sources or breakdown capacity. Vitamin B6 is required for proper DAO function. The enzyme simply cannot work efficiently without adequate B6. Deficiency directly reduces DAO activity, meaning histamine from food and from mast cell release accumulates because it's not being broken down at a normal rate. This has a direct effect on our body's primary histamine-clearing mechanism. Copper supports the structural foundation of DAO. DAO is a copper-dependent enzyme. Copper is built into its molecular structure. Without sufficient copper, our body cannot produce functional DAO enzyme, regardless of how healthy our intestinal lining might be. This is one of the most under-recognized causes of low DAO activity. Copper deficiency in MC can develop from chronic diarrhea washing out minerals, and from restricted diets that eliminate copper-rich foods. The effect is straightforward — reduced DAO production and dramatically reduced DAO activity. Vitamin C helps degrade histamine and reduces mast cell activation. When levels are low, circulating histamine increases and clearance slows. While vitamin C isn't as direct a factor as B6 or copper for DAO function, it plays an important supporting role in overall histamine metabolism. Zinc maintains the intestinal lining and regulates immune response. Deficiency increases gut permeability (leaky gut), allowing more food antigens to reach immune cells, which triggers more histamine release. Deficiency also disrupts immune balance, potentially increasing mast cell activation. This creates a vicious cycle — zinc deficiency worsens gut integrity, which increases antigen exposure, which triggers more immune activation and histamine release. Vitamin D regulates immune and mast cell activity. Deficiency leads to increased inflammatory signaling and greater mast cell activation, meaning more histamine release even from normal, everyday triggers. In other words, single deficiencies matter, but synergy is the real problem. Each of these deficiencies can independently push our system toward histamine intolerance:
Any single one of these deficiencies can tip a borderline system into symptomatic histamine intolerance. However, the real problem in MC is synergy. These deficiencies don't just add together — they multiply each other's effects. The Synergistic Cascade: Here's how the cascade typically develops in MC: Step One: MC causes chronic diarrhea and nutrient loss. Magnesium, zinc, and B6 levels drop from constant losses in watery stool. These minerals and vitamins are water-soluble or poorly retained during rapid transit. Step Two: Gut inflammation reduces DAO production. The inflamed, damaged intestinal lining has fewer healthy cells capable of producing DAO enzyme. Even if nutrient levels were perfect, DAO production would be compromised. Step Three: Copper deficiency develops. Between diarrheal losses and dietary restrictions (many MC patients limit foods to manage symptoms), copper intake and absorption decline. The DAO enzyme that is being produced becomes structurally impaired. Step Four: Multiple effects converge. Now we simultaneously have:
The result is histamine overload from both directions — more histamine is being released by hyperactive mast cells, and less histamine is being broken down by impaired DAO. Why correcting just one nutrient deficiency often fails. Many MC patients discover magnesium or vitamin C, supplement it, and feel better briefly. Then symptoms return. Here's why: single-nutrient approaches typically fail: With supplementary magnesium, which stabilizes mast cells, and reduces histamine release, we feel improvement. However, our DAO is still impaired because B6 and copper remain low. Histamine still accumulates from dietary sources and any remaining mast cell activity. Within weeks, symptoms return despite continued magnesium supplementation. The problem is that we've addressed one piece of a multi-piece puzzle. The other deficiencies are still undermining histamine metabolism. Why a multi-layered approach actually works: Effective management requires addressing multiple layers simultaneously, not sequentially. Layer One: Stabilize mast cells to reduce histamine release.
Layer Two: Restore DAO function:
Layer Three: Improve histamine clearance:
Layer Four: Repair gut environment:
Layer Five: Monitor and adjust:
Where do we start, to achieve practical implementation? If we're experiencing histamine sensitivity symptoms alongside MC, we should consider this approach: Immediate steps:
Short-term (within 1-2 months):
The most important takeaway: Histamine intolerance in MC is rarely a single deficiency problem — it's a network failure involving gut damage, multiple interacting nutrient deficiencies, and ongoing losses from chronic diarrhea. The deficiencies work synergistically, meaning their combined effect is greater than the sum of their individual effects.
This explains why histamine problems in MC patients often feel overwhelming and why simple solutions like "just take DAO" or "just avoid high-histamine foods" rarely work long-term. You're dealing with a systemic problem that requires systemic solutions. The good news is that when you address multiple nutritional deficiencies simultaneously, support gut healing, and maintain adequate nutrient levels despite ongoing losses, many MC patients see significant improvement in histamine tolerance. Foods that previously triggered immediate reactions become tolerable again. The constant background symptoms of flushing, itching, headaches, and anxiety improve. But this requires patience, comprehensive supplementation guided by testing, continued MC treatment to reduce gut inflammation, and working with healthcare providers who understand the complex interaction between MC, nutrient status, and histamine metabolism. It's not a quick fix, but it's an achievable goal based on understanding the actual mechanisms that interact to cause the problem.
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Despite all the elaborate rituals, Latin terminology, and authoritative pronouncements, early medicine is probably best described as fundamentally primitive — operating without understanding of basic biological processes, disease mechanisms, or scientific method. The sophistication was all surface-level theatrical display masking profound ignorance about how the human body actually works. It's a sobering description because it forces us to confront how much of what seemed "advanced" to practitioners of the time was actually just dressed-up guesswork. Fast forward to today, and consider that: Labels are replacing understanding in modern medicine. Medicine has come a long way from the days when unexplained symptoms were attributed to “bad humors” or witchcraft. Yet, in some ways, today’s medical system continues to apply labels that offer more comfort to physicians, than clarity to patients. Conditions such as Irritable Bowel Syndrome (IBS), Chronic Fatigue Syndrome (CFS), and Functional Neurological Disorder (FND) illustrate a troubling trend — when tests fail to reveal a clear pathology, the medical profession often creates a “syndrome” to fill the gap. These syndromes, presented as diagnoses, provide a name, but not an explanation. Syndromes are being defined as diseases. A syndrome is meant to describe a cluster of symptoms that appear together, without implying a known cause. That’s reasonable as a temporary classification. The problem arises when these placeholders harden into quasi-diseases, presented to patients as final diagnoses. This can mislead both patient and doctor into thinking the mystery is solved. Consider how:
EHR systems promote labels rather than understanding. Electronic health record (EHR) systems require clinicians to enter standardized diagnostic codes for billing, insurance, and regulatory reporting. This means that even when the cause of a patient’s symptoms is uncertain, the physician must still pick a code. Instead of documenting “undiagnosed abdominal pain pending further investigation,” the system often pressures the clinician into selecting “IBS, functional dyspepsia, or another convenient label. Once entered, that label becomes part of the official record, shaping how future providers perceive the patient’s case. Once a diagnosis is coded into the record, it tends to persist — even if later evidence proves it wrong.
This undermines accuracy.
Patients may be permanently tagged with a condition they don’t truly have, which can:
Where will this lead?
This trend doesn't bode well for the integrity of medicine. On one level, these labels reflect medical humility — physicians acknowledge they don’t yet know the underlying cause. But in practice, they are often wielded as if they were genuine disease entities. This practice undermines trust in medicine because:
This echoes the intellectual shortcuts of pre-scientific medicine, where unexplained conditions were attributed to supernatural causes. While today’s terms are more scientific-sounding, the underlying issue — explaining the unknown with arbitrary categories, remains. The trend is ultimately counterproductive. If medicine continues down this path, more patients will feel alienated, and public confidence will erode. Worse, real discoveries may be delayed because conditions are dismissed as “functional” rather than investigated. The challenge for modern medicine is to replace syndrome labeling with biological understanding. That requires:
Conclusion: The reliance on arbitrary syndromes is a symptom of medicine’s discomfort with uncertainty. While such labels may ease clinical conversations, they risk repeating the mistakes of history—swapping “witchcraft” for “functional” and leaving patients with no true answers. The future of integrity in medicine depends on humility, honesty, and a commitment to dig deeper until symptoms are explained not by labels, but by knowledge. Looking at this situation realistically, though, does anyone believe that medicine will "heal itself"? Here's my opinion of why this is happening. The basic problem here appears to be that precious few certified medical specialists have ever learned a basic fact that most PhD holders, for example, clearly understand — despite their certification as an "expert", experts can never learn everything there is to know about their field of expertise, making them perpetual students. That implies that they never stop learning (at least that's how it's supposed to work, in theory). It works for PhD holders — why doesn't it work for MD holders?
As most MC patients can attest, many (possibly most) gastroenterologists have never significantly advanced past their initial relatively basic understanding of MC, and its treatment. They are clearly not continuing to learn (or else they are doing a good job of hiding their newly acquired knowledge from their patients). Apparently this concept is not being properly taught during their medical training. And the confusing part of that attitude is that patients don't expect specialists to know everything there is to know about their specialty — they only expect specialists to provide the best care of which they are capable, and for which they are charging relatively high fees. So why do specialists choose to compromise the integrity of their profession by defining "diseases" that don't actually exist? Although it surely isn't intentional, by doing so, they are effectively devolving medical care as they choose to use some of the techniques that we find so appalling from the early days of medicine.
A dramatic shift is underway in healthcare, and microscopic colitis (MC) patients are part of it. Increasingly, people with complex, chronic conditions are turning to artificial intelligence tools — not out of curiosity, but out of necessity. For many, AI has become a parallel information system used alongside, (or sometimes in place of) traditional medical care, when conventional approaches have failed to resolve their symptoms. This article addresses a reality many MC patients face — after years of unsuccessful symptom management, dietary trials that don't work, medications that provide only partial relief, and physicians who seem unable to explain why some of us are still suffering, AI tools can seem like a promising alternative source of answers. This trend was recently highlighted in a New York Times article describing patients who, after exhausting traditional medical options, began using AI chatbots to identify overlooked diagnoses and treatment strategies (Astor, 2026, April 2).1 But before you turn to AI in frustration, you need to understand both why it might help and why it can be dangerous. Why MC patients struggle with the current system: Modern medicine is built around specialization. You're referred from gastroenterologist to allergist to rheumatologist to endocrinologist, with each specialist focusing narrowly on their domain. But MC — particularly when it doesn't respond well to standard treatment — often doesn't respect these boundaries. Many MC patients experience symptoms that affect multiple systems, including chronic diarrhea and abdominal pain (gastroenterology), food sensitivities and possible mast cell involvement (immunology/allergy), fatigue and brain fog (potentially endocrine or neurological), electrolyte imbalances affecting heart rhythm (cardiology), bone loss from chronic inflammation and malabsorption (endocrinology), and anxiety or depression from chronic illness (psychiatry). Each specialist addresses a single symptom, yet none assembles the full picture. This fragmentation isn't incidental, it's structural. Medical training, reimbursement systems, and clinical workflows all reinforce a pattern where symptoms are assigned to categories, diagnoses are isolated and separated, and cross-system interactions are often overlooked. For MC patients whose symptoms involve immune dysfunction, gut-brain axis disruption, autonomic nervous system effects, or complex food and medication reactions, this model frequently breaks down entirely. You leave appointments feeling unheard, with your complex reality reduced to "just IBS" or "stress-related symptoms" despite a confirmed MC diagnosis. What AI offers that the healthcare system often doesn't: AI tools provide something the fragmented specialist system cannot — the ability to synthesize large amounts of information across many disciplines, without boundaries. You can input your complete symptom history, upload lab results, ask about connections between seemingly unrelated symptoms, explore differential diagnoses, and investigate whether your medication side effects might be worsening your condition. Unlike a specialist trained in gastroenterology who may have limited knowledge of autonomic dysfunction or mast cell disorders, AI doesn't operate within a single discipline. This makes it particularly appealing for MC cases that involve multiple organ systems, present with fluctuating or atypical symptoms, haven't responded to standard treatment, or seem connected to other conditions your doctors haven't explored. Real success stories, and their limitations. The New York Times article describes several cases where AI contributed to meaningful diagnostic breakthroughs. A long COVID patient connected her symptoms to dysautonomia after multiple specialists missed it. Another patient identified mast cell activation syndrome through AI suggestions and later obtained formal diagnosis and effective treatment. A third patient, medically trained herself, used AI to generate differential diagnoses that led to identifying and surgically correcting pelvic congestion syndrome. These cases share a critical common theme: AI didn't replace physicians—it helped patients ask better questions and pursue overlooked possibilities that they then confirmed with appropriate medical testing and treatment. However, success is not the norm. The same article makes clear that chatbots can be dangerously wrong. In one study, users reached correct diagnoses less than half the time when relying on AI tools. AI can hallucinate sources and citations, misinterpret lab values in dangerous ways, overemphasize irrelevant findings while missing critical ones, provide false reassurance about serious conditions, and suggest treatments that are contraindicated or unsafe. Patients interviewed were generally aware of these risks but felt they had little choice. As one patient stated: "Is it a good thing to be depending on AI for medical advice? I don't think so. But it's the option that's available." Who uses AI successfully? One of the most important findings is that the most successful AI users tend to have strong analytical or medical skills. In the documented examples, users included a physical therapist and someone with a research background in illness and disability. These individuals could challenge incorrect AI suggestions, filter plausible from implausible diagnoses, recognize when AI was overreaching or fabricating conclusions, and verify information through credible medical sources. Without that skill set, outcomes are far less reliable and potentially dangerous. If you cannot critically evaluate what AI tells you, you are at serious risk of following harmful advice. Potentially helpful uses of AI by MC: patients: Pattern recognition across symptoms: AI can help you identify connections between your GI symptoms and other issues like joint pain, fatigue, skin problems, or cognitive difficulties that might suggest related conditions like mast cell activation or autoimmune overlap. Trigger identification: You can describe complex symptom patterns after eating certain foods or taking specific medications, and AI can help generate hypotheses about mechanisms (although you must verify these through elimination trials and medical testing, not assume they're correct). Treatment option exploration: AI can provide information about medications, supplements, or dietary approaches you haven't tried, giving you informed questions to bring to your physician rather than instructions to follow independently. Lab interpretation context: If you have unusual lab results your doctor hasn't fully explained, AI can suggest possible interpretations (but these should be discussed with your physician, not acted upon alone). Preparing for appointments: AI can help you organize your symptom timeline, prioritize questions, and anticipate what information your doctor might need, making appointments more productive. AI cannot and should not: Replace diagnostic testing: Suggesting you might have bile acid diarrhea doesn't mean you do—you need a SeHCAT scan or therapeutic trial with cholestyramine under medical supervision. Provide personalized treatment plans: What works for "most MC patients" may be dangerous for you specifically based on your medications, comorbidities, or individual physiology. Interpret complex or borderline lab results: A magnesium level that's "technically normal" might still be inadequate for someone with chronic diarrhea and absorption issues. But this requires clinical judgment, not algorithm output. Override medical advice: If your gastroenterologist says not to stop budesonide yet and AI suggests you can, your doctor's recommendation should prevail unless you get a second opinion from another qualified physician. Diagnose serious conditions: If AI suggests you might have colon cancer, inflammatory bowel disease overlap, or a cardiac arrhythmia, you need immediate medical evaluation, not more AI research. How to use AI safely if you choose to use it: If you decide to use AI for medical information despite these risks, follow these essential safety guidelines:
When you absolutely need professional medical care: Regardless of how frustrated you are with the healthcare system, certain situations require immediate physician involvement:
A Balanced Perspective: The growing reliance on AI in healthcare reflects two simultaneous truths.
What we are witnessing is not the replacement of doctors but a workaround for structural gaps in healthcare. AI is being used to generate hypotheses, connect symptoms across systems, identify overlooked conditions, and prepare patients for more informed discussions with physicians. But it is absolutely not a substitute for clinical judgment, diagnostic testing, safe treatment planning, or the irreplaceable value of a physician who knows your complete medical history. Moving Forward: If you're considering using AI because conventional care hasn't resolved your MC symptoms, first exhaust appropriate medical options:
It's interesting to note that according to a recent article on the Medscape website, in 2023, about 38% of physicians were using AI. Now, in 2026, that number has jumped to 81% (Whyte, 2026, March 26).2 The Bottom Line: The rise of AI-assisted medical information seeking is not primarily a story about technology. It's a story about unmet needs. MC patients are not turning to AI because they prefer it to physicians — they're turning to it because their symptoms remain unexplained, their conditions are fragmented across specialties that don't communicate, standard treatments have failed, and their questions remain unanswered. AI, for all its flaws, offers something that some patients cannot find elsewhere — a system that attempts to synthesize information across boundaries and look at the whole picture. Whether that system reaches accurate or dangerous conclusions is still an open question that depends heavily on how it's used. But the reason people are using it is painfully clear — when the healthcare system repeatedly fails to help you, you will look for help elsewhere. The solution isn't uncritical embrace of AI, it's fixing the systemic problems that drive desperate patients to seek answers from algorithms in the first place. References: 1. Astor, M. (2026, April 2). Doctors Couldn’t Help Them. They Rolled the Dice With A.I. New York Times, Retrieved from https://www.nytimes.com/2026/04/02/well/live/ai-illness-claude-chatgpt.html?unlocked_article_code=1.ZFA.tWdj.SkN38Nt5nrCA&smid=url-share 2. Whyte, J. (2026, March 26). Physician AI Adoption Is Surging: We Must Lead Its Integration. Medscape, Retrieved from https://www.medscape.com/viewarticle/physician-ai-adoption-surging-we-must-lead-its-integration-2026a100095u
More and more people are turning to AI for answers to questions about their health problems, rather than going to their doctors. According to a survey by the Annenberg Public Policy Center of the University of Pennsylvania (April 2025, involving about 1,600 adults) (Staff, 2025, July 14):1
Another report from a wellness trend survey, discussed in the New York Post, found that “more than 1 in 3 Americans (35%) are using AI to learn about and manage aspects of their health and wellness” — including researching conditions or meal planning (Beaner, 2025, July 24).2 For example, according to the article, 31% used AI to get insight into specific medical conditions/issues. But people don't want their doctors to rely on AI. On the flip side, a broader attitude survey by the Pew Research Center in February of 2023 found that 60% of U.S. adults would feel uncomfortable if their own health-care provider relied on AI to make diagnoses or recommend treatments. Academic evidence also shows a risk. For example, one recent paper found that when users evaluate health advice from AI, they may over-trust it and treat it as equivalent to a doctor’s advice, even when accuracy is low. So it's safe to say that somewhere between 30-40% of adults (at least in more tech-enabled populations like the U.S.) are already using AI tools for health-information or health-management tasks. True “replacing the doctor” is much less common — the data suggest more “supplementing online information” rather than full replacement of a clinician. What's likely to happen in the future? Given the trends in technology, user-behaviour, regulation, and health-care delivery, several scenarios seem likely: 1. AI will probably become a more common first choice.
2. AI will probably be integrated with clinical care, and hybrid models will evolve.
3. This may improve accessibility, but increase regulatory and ethical complexity.
4. A potential shift in healthcare-consumer behavior and business models will be developed.
There will be risks — particularly for complex or chronic conditions. For people with chronic, complex, or serious conditions (for example. autoimmune/inflammatory bowel diseases such as microscopic colitis (MC), or thyroid disorders), the risk of relying solely on AI is higher — comorbidities, medication interactions, and personalised history, for example, may be overlooked. That said, in all fairness, it should be noted that most of those issues are often overlooked in the current healthcare environment.
How is this likely to affect MC Patients? Many patients with chronic GI or autoimmune issues may use AI tools to ask symptom-based questions (“Why do I still have diarrhea?”, “Is my diet okay?”, “What antibiotic risk do I have?”) — which means more people will arrive at forums, chats or doctor visits already armed with AI-generated thoughts or conclusions.
But the foregoing analysis assumes that healthcare professionals will remain more knowledgeable and more reliable than AI. And most importantly, the analysis assumes that healthcare professionals currently provide adequate explanations and information to patients regarding health issues. Unfortunately, in many cases, including MC, that simply is not the case. Additionally, many clinicians have personal biases that affect their viewpoints regarding healthcare. What if AI truly knows more about health than the average doctor? Up to now, most discussions assume that AI is an assistant, and the doctor is the authority. But if AI eventually becomes more accurate, more comprehensive, more up-to-date, and more diagnostically reliable than the average doctor, the entire structure of healthcare will change so that AI becomes the primary source of medical information. If AI becomes consistently:
then AI will naturally become the first choice for health information — not because people prefer technology, but because it will outperform physicians. This would make AI‐driven self-assessment the new default, and doctor visits would become secondary, confirmational, or reserved for cases requiring physical procedures. Clinicians would shift from diagnostic experts to human-care specialists. A glimpse into the way most hospitals operate reveals that this is already happening, to some extent. GPs have less diagnostic and prescribing authority, and patients are sent to specialists to diagnose many health issues that previously were diagnosed by GPs. If AI becomes the superior diagnostician: This would eliminate the need for a physician's presence in at least 40 to 70% of office visits. Obviously, hands-on work such as surgery and endoscopy cannot be replaced by AI (at least, not in the immediate future). But wait times would be reduced, and healthcare costs would drop dramatically, because (for example), most of the time consuming record-keeping requirements would be handled by AI. Clinicians would need to transition into interpreters and counselors, to discuss various treatment options, accommodate patient preferences, fears, social issues and any concepts that AI can't fully handle. Medical training would need to be redesigned so that physicians would be skilled at:
Physicians would shift from being a walking textbook, into an AI enhanced medical operator. What's wrong with this picture? The attributes listed above are currently weak areas for most physicians. All of them are currently deemphasized by the healthcare system. So these changes would require a paradigm shift. Will physicians be willing to make those dramatic changes? That's not likely for older, well-established doctors. Surely the changes will need to occur gradually, as med school graduates evolve. Physicians will probably resist these changes, but patients will demand them if the changes result in improved healthcare at reduced cost. Liability will shift. Right now, if a doctor follows AI and AI is wrong → the doctor is blamed. If AI proves more accurate than the average doctor, the opposite will happen: it will become malpractice not to use AI. Physicians struggle most with:
But AI can handle all these easily, and virtually instantaneously. Healthcare will become decentralized and patient driven. Currently, medical knowledge flows from medical schools through doctors to patients. In an AI dominated healthcare system, knowledge will flow directly to patients. Rather than dictating healthcare, doctors of the future will become "assistants" in the healthcare system. While that sounds somewhat ominous, it's actually already the case. How doctors are currently able to perform their duties is dictated by the government, insurance companies, pharmaceutical companies, attorneys, hospital administrators, and state regulators. For doctors, choosing to think "out of the box" is a risky venture. But an AI takeover can only go so far, because: AI cannot, for example, palpate an abdomen. Nor can it handle various real-time physical observations encountered in various diagnostic procedures. So there will always be a need for physicians during examinations, endoscopies, surgeries, managing emergencies, and various other procedures. AI has already surpassed human doctors. in a few narrow medical tasks, such as:.
But AI is still far behind In the core skills of medicine, such as:
A huge portion of healing is relational and emotional. But note that GPs typically possess those attributes, while many specialists do not, and the healthcare system appears to be phasing out GPs, which implies that it's phasing out relational and emotional attributes. So when will AI surpass doctors overall? Experts in medical AI tend to concur on three timelines: 1. Narrow medical tasks: 2025–2030 (already partly achieved)
AI will be consistently better than average doctors in these narrow tasks. But this is not true general doctor-level performance. 2. Broad diagnostic reasoning: 2035–2045 (optimistic) This refers to what primary-care physicians or gastroenterologists do:
Some AI models are getting surprisingly close in controlled tests, but real-world generalization remains extremely difficult. 3. Full-spectrum medical practice: Not foreseeable yet (maybe never) This includes:
But a more likely outcome is that instead of replacing doctors, AI will:
And doctors will:
So that AI becomes a copilot, rather than a replacement. Could AI realistically diagnose microscopic colitis better than doctors? The answer is, "Yes, eventually.” In fact, MC is one of the conditions most likely to benefit from AI -assisted diagnosis — but that will only be possible after additional progress is made. Reasons why AI could diagnose MC better than many doctors include: 1. Many doctors simply miss MC:
2. AI can detect symptom patterns doctors routinely overlook. Most physicians don’t know the typical patterns associated with MC:
AI can learn these patterns from large datasets, medical literature, and patient histories. Doctors rarely integrate all of them. 3. AI could flag missing biopsies or inadequate sampling: A huge share of missed diagnoses result from:
AI reviewing procedural notes and pathology reports could automatically detect:
AI can follow the guidelines perfectly, every time — something human clinicians often fail to do. 4. AI can analyze pathology slides more sensitively:
AI vision models are already excellent at detecting subtle histologic changes in:
So applying similar models to:
would likely yield more consistent and earlier detection than many general pathologists provide today. But first, high-quality MC datasets must be available for training AI. Unlike Crohn's and ulcerative colitis (UC) MC datasets are still small, and AI training requires thousands of biopsy slides, clinical histories, medication lists, and follow-up outcomes. Furthermore, pathologists don't have standard labeling policies regarding specifying collagenous colitis versus lymphocytic colitis. There are AI tools for:
But precious few exist for MC, because of it's microscopic nature, and analysis requires histology training, not endoscopy. And, of course, gastroenterologists would still need to supply the biopsy samples. Looking ahead, curating adequate data sets will surely take at least 3 to 7 years, suggesting that for AI to be able to gain superior diagnostic capability for MC, at least 10 to 15 years would be required. The Bottom Line: AI is already changing how patients seek health information, with roughly one-third of Americans using it for medical guidance—yet most remain uncomfortable with doctors relying on it. This paradox won't last. As AI demonstrates superior accuracy in diagnostic tasks, exceeds physicians' ability to integrate complex data, and avoids the cognitive biases that plague clinical decision-making, it will naturally become the primary source of medical information within the next 10-20 years. For MC patients specifically, this shift offers particular promise: AI could eliminate the misdiagnoses, overlooked patterns, and inadequate biopsies that currently plague 70% of cases. The future won't see AI replacing doctors entirely, but rather transforming them from walking textbooks into interpreters, counselors, and procedural specialists — a shift that may finally give chronic disease patients like those with MC the thorough, unbiased, data-driven care they've long deserved. The question isn't whether this will happen, but whether the medical profession will adapt gracefully or resist until patients demand change. References: 1. Staff. (2025, July 14). Many in U.S. Consider AI-Generated Health Information Useful and Reliable. Annenberg Public Policy Center, University Of Pennsylvania, Retrieved from https://www.annenbergpublicpolicycenter.org/many-in-u-s-consider-ai-generated-health-information-useful-and-reliable/ 2. Beaner, L. (2025, July 24). More than one in 3 Americans are using AI to manage their health, according to new survey. New York Post, Retrieved from https://nypost.com/2025/07/24/tech/more-than-1-in-3-americans-are-using-ai-to-manage-their-health-according-to-new-survey/
The 1910 Flexner Report revolutionized medical education by moving training into universities, formalizing clinical education, and establishing residency programs. These reforms, expanded mid-century with research training integration, created the foundation of modern medicine. But in the 75 years since those last major structural changes, medical practice has transformed dramatically while medical school curricula have largely remained unchanged. Physicians now practice in a data-driven, technology-rich society, treating aging populations with chronic illness. Yet the core curriculum still reflects a 20th-century model focused primarily on diagnosing and treating disease after it appears. Dr. Henry Buchwald recently argued that medical education is overdue for another Flexner-level overhaul, and a review of curricula at ten leading U.S. institutions revealed striking gaps in subjects essential for modern practice (Buchwald, 2026, January 30)1 The question is no longer whether reform is needed—it's what must change and how quickly. The primary problem is we're still using reactive medicine in a preventive era. Modern medicine faces challenges that didn't exist when current curricula were designed. Chronic disease dominates healthcare spending. Patients live longer and accumulate multiple conditions. Nutrition misinformation is widespread. Data and statistics guide nearly every medical decision. Technology increasingly shapes diagnosis and treatment. Physician burnout is rising sharply. Yet medical training still emphasizes memorization of disease and treatment,` rather than prevention, thinking in terms of systems,and fully utilizing life-long data records. A modern curriculum should shift from reactive medicine to preventive, systems-based, and data-driven medicine. Six major gaps illustrate why these changes are urgently needed. Statistics are the language of modern medicine: Every clinical decision relies on probability, risk, and statistical interpretation. Physicians must interpret clinical trials, risk-benefit ratios, screening test accuracy, epidemiology, and treatment effectiveness. Yet Buchwald's review found that statistics is rarely a required course in medical school. Even at elite institutions, it's often merely "recommended" as pre-medical preparation. Historically, students learned calculus but not probability or statistical reasoning. As a result, many physicians rely on research they don't fully understand how to evaluate. Without the ability to critically evaluate medical literature independently, evidence-based medicine becomes impossible. Medical students should complete applied biostatistics, research interpretation, risk communication, Bayesian reasoning, and evidence quality assessment as basic requirements. Nutrition is medicine's largest preventable risk factor: Diet drives obesity, type 2 diabetes, cardiovascular disease, fatty liver disease, some cancers, and autoimmune and inflammatory disorders. The global weight-loss industry exceeds $300 billion annually, and patients constantly receive conflicting nutrition advice. Yet only one of the surveyed medical schools offered a dedicated nutrition course. Many physicians graduate with minimal training in metabolism or dietary intervention. Doctors cannot guide patients if they're not adequately educated about nutrition. Medical education should include metabolism and energy balance, dietary patterns and chronic disease, nutritional counseling skills, and public nutrition misinformation literacy. Preventive public health would end the artificial divide: Medicine treats individuals. Public health treats populations. This division made sense historically, but in modern healthcare it's increasingly harmful. Pandemics, chronic disease prevention, environmental exposures, and lifestyle risks blur the boundary between individual and population health. Despite this reality, most medical schools still separate public health from clinical training. Public health and medicine should be integrated. Epidemiology should be a part of basic clinical knowledge, alongside environmental health literacy, pandemic preparedness, and preventive medicine training. Prevention should become a central physician skill, not a peripheral specialty. Bioengineering and technology are transforming medicine into a tech profession: Modern physicians work with implantable devices, advanced imaging, wearables and remote monitoring, AI diagnostics, and robotics. Biology increasingly intersects with engineering, physics, and computing. Yet bioengineering is rarely taught to medical students. Understanding how medical technology works improves clinical decision-making, patient safety, innovation, and collaboration. Basic medical training should include biomedical engineering fundamentals, medical device literacy, digital health technologies, and AI and machine learning basics. Future physicians must be partners in innovation, not passive users of technology. Older adults (65+) are the fastest-growing segment of the patient population. The demographic reality is stark. Approximately 18% of Americans are over 65, nearly 40% of healthcare spending is devoted to this group, and fewer than 7,000 geriatricians serve a nation of over one million physicians (in the U.S.). Most patients treated by physicians today have multiple chronic conditions, polypharmacy (the concurrent use of five or more medications), and age-related physiological changes. Yet geriatrics receives minimal curricular attention. All physicians need training in polypharmacy and drug interactions, frailty and fall risk, cognitive decline, end-of-life care, and age-specific lab interpretation. Geriatric medicine should be an essential part of training, not optional. Medical history and professional identity: Medical education rarely teaches the history of medicine, yet historical perspective provides ethical context, reveals how knowledge evolves, encourages humility and critical thinking, and strengthens professional identity. Understanding the past helps physicians navigate uncertainty and change. Curricula should include the history of medical discovery, the evolution of medical ethics, and lessons from past epidemics and breakthroughs. Medicine is not just a science — it's a centuries-long human endeavor. Additional training needs beyond the gaps: Digital literacy and AI training are essential as physicians must learn to work with AI decision tools responsibly. Communication and behavioral science skills are critical because most chronic disease is behavior-driven. Physician wellbeing and burnout prevention desperately needs to be addressed, as half of physicians report burnout. Balancing reform: A modern medical curriculum should balance traditional strengths (anatomy and physiology, pathology, pharmacology, clinical training, research, and ethics) with new essential skills including statistics and data literacy, nutrition and prevention, public health integration, bioengineering and technology, geriatrics and aging, and history and professional identity. The goal is not to remove traditional sciences but to expand medicine's intellectual toolkit. These reforms are urgently needed — sooner, rather than later. The Flexner Report transformed medical education once before. Today, medicine faces another turning point. Chronic disease, aging populations, technological disruption, and information overload demand a new kind of physician — one trained not only to treat disease, but to understand systems, data, prevention, and human behavior. Updating medical curricula is not merely an academic exercise. It's essential for the future of healthcare. The next generation of physicians must be trained for the world they will practice in, not the one medicine has left behind. This wasn't mentioned in the article cited above: But this will surely present a major obstacle as medical schools attempt to make these changes, because of the way that physicians are currently trained — there's a memorization paradox. Although modern medical schools officially emphasize critical thinking, clinical reasoning, problem-based learning, and evidence-based medicine, real-world outcomes do not accurately reflect that goal. Most have redesigned their curricula around small-group case learning, systems-based teaching, and early clinical exposure. On paper, medical education moved away from "memorize textbooks" decades ago. However, the reality of students' experience is quite different. In practice, students must still learn an enormous volume of information including anatomy, physiology, biochemistry, pharmacology, pathology, microbiology, and clinical guidelines. The amount of required knowledge is so large that memorization becomes unavoidable, especially in the preclinical years. Students often describe the reality as "understanding is ideal, but memorization is required to pass." This tension exists because licensing exams like the USMLE still test thousands of facts, rare diseases, drug mechanisms, and biochemical pathways. Even when exams attempt to test reasoning, they require huge knowledge recall, creating powerful incentives for students to prioritize memorization. Medical training today operates as a hybrid system. Memorization remains heavy during early training, while understanding increases during clinical years, and pattern recognition dominates in residency and actual practice. Students often use tools like Anki flashcards for thousands of facts daily, leading to the perception of "rote memorization." Clinical expertise ultimately relies on pattern recognition and mental libraries of cases, so memorization becomes the foundation of clinical intuition rather than an end in itself. Medical schools are attempting to shift toward more clinical reasoning earlier through case-based learning, simulation labs, integrated curricula that teach anatomy, physiology, and pathology together, and more active learning with fewer traditional lectures. However, change is slow because licensing exams still require massive knowledge recall, creating what educators call "assessment drives learning" — students study to pass the exam, and schools teach toward the exam. The tension between the volume of knowledge medicine requires and the desire to develop true clinical reasoning remains one of the biggest debates in modern medical education. So what does all this suggest? Medical students find it necessary to resort to memorizing rather than learning and understanding most of the information that they're forced to remember, simply because they don't have sufficient time to allow them to understand what they are expected to repeat on tests. So if all the changes to medical school curricula suggested by Dr. Buchwald are actually attempted, the number of years required for receiving a medical degree will almost surely have to be extended significantly, especially if students are expected to actually learn the information, rather than memorize it. Reference: 1. Buchwald, H. (2026, January 30). Tweaking the Curriculum. Gastroenterology & Endoscopy News, https://www.gastroendonews.com/Opinions-and-Letters/Article/01-26/Medical-Curriculum-Innovation-and-Training-Reform/79404
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AuthorWayne Persky Archives
August 2026
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