AI is the New Pharmacist: How Large Language Models Influence Consumer Self-Care Decisions
Introduction
In the age of generative AI, consumers are increasingly turning to large language models (LLMs) like ChatGPT for health advice and product recommendations – effectively using AI as a “digital pharmacist.” Instead of simply Googling symptoms and scrolling through links, people can now have an interactive Q&A with an AI that feels conversational and personalized. This shift is significant: a recent poll found about one in six adults (17%) in the U.S. now uses AI chatbots at least once a month to seek health information or advice (rising to 25% among young adults). In another 2025 survey, 35% of Americans reported they have consulted AI about a health concern, with usage rates nearing 50% in younger demographicsdriphydration.com. Clearly, a behavioral change is underway – moving from the era of “Dr. Google” to asking “Dr. ChatGPT” for guidance.
Several factors drive this trend. First, accessibility and convenience: AI chatbots are available anytime, anywhere. They can instantly analyze a query like “Why do I have a headache and what can I take for it?” and provide an organized answer in plain language. This on-demand help is especially attractive in contexts where healthcare access is limited or slow. In one survey, the top reason people use AI for health is simply to get faster answers – cited by 43% of respondentsdriphydration.com. Second, LLMs can distill complex medical information or drug details into user-friendly explanations. For example, rather than reading through multiple webpages, a consumer can ask an AI, “What’s the difference between ibuprofen and acetaminophen?” and get a concise comparison. In fact, about 35% of people use AI to understand medication side effects or other drug informationdriphydration.com – exactly the kind of guidance a pharmacist might offer. Third, the conversational nature of LLMs makes the experience feel personalized. Unlike a search engine that returns a list of links, an LLM “chat” can simulate an empathetic expert who not only provides information but also follows up with clarifying questions or advice, creating a sense of support.
At the same time, both consumers and health professionals are approaching this new tool with cautious optimism. Most people recognize that “ChatGPT is not a doctor in the same way Google is not a doctor” – it can offer helpful insights, but it’s not a substitute for professional medical judgment. Polling reflects this ambivalence. While nearly 40% of Americans say they trust AI tools like ChatGPT for medical advicedriphydration.com, a majority still express some level of skepticism about accuracy and safety. For example, a KFF poll in 2024 found only 29% of adults trust that AI chatbots can provide reliable health information, and 56% of even the regular AI users were “not too confident” or “not at all confident” in the accuracy of health info from chatbots. In other words, consumers love the convenience and insight these “digital pharmacists” offer, but they remain wary – rightly so – of misinformation or misdiagnosis. This whitepaper will explore how LLMs are influencing consumer self-care decisions through a consumer behavior lens, the opportunities and risks involved, and strategies for brands to navigate this shift with authority and accuracy.
From “Googling Symptoms” to Asking ChatGPT: A Behavior Shift
For decades, the default first step in self-care has been to search the internet about one’s symptoms – the notorious “Dr. Google” phenomenon. By the mid-2000s, roughly 8 in 10 people were already using search engines to look up health information. This often involves reading through WebMD articles, patient forums, and myriad sources to self-diagnose or decide if a doctor’s visit is needed. However, this process can be overwhelming and anxiety-inducing – a single search can lead down a rabbit hole of worst-case scenarios (a dynamic so common it’s earned the nickname “cyberchondria”). Now, generative AI is streamlining that experience. Instead of sifting through links, consumers can pose their concern in natural language to an AI and receive a direct, synthesized answer. The query that might have been “stuffy nose, headache, fatigue causes” on Google becomes “I have a stuffy nose, headache, and fatigue – what might be wrong and what can I do?” on ChatGPT. The AI will respond with a sensible discussion of possible causes (e.g. a common cold vs. allergies), suggest over-the-counter treatments, and even add advice like staying hydrated or seeing a doctor if symptoms worsen. This more conversational approach can feel less laborious than clicking through multiple webpages, and it often provides a quicker sense of “answer” – which users clearly value.
Crucially, LLM-based search is not only accessible via ChatGPT’s website or app; it’s increasingly integrated into the search platforms people already use. Both Google and Bing have rolled out AI-generated summary answers (Google’s “AI Overview” and Bing’s “Chat”/Copilot mode) that appear above the traditional search results. For example, a Google search for “indigestion at night remedy” might first display a paragraph (with an AI icon) summarizing potential causes of indigestion and suggesting remedies like antacids or dietary changes, all before any standard links. These AI summaries come with disclaimers (“for informational purposes only… AI may include mistakes”), but millions of users see them. In an April 2025 survey of U.S. adults, 65% of online health searchers reported noticing AI-generated responses at the top of their search results. Moreover, most of these users found the AI information helpful – nearly 3 in 4 said the AI answers “sometimes” or “often” give them what they need. In fact, 63% considered the AI-provided health info at least somewhat reliable, despite the known caveats. This indicates a significant level of trust in the first answer that appears, which now is frequently an AI-crafted answer.
From a behavioral perspective, this represents a shift in the consumer decision journey for health and wellness issues. The search stage is being transformed into a conversation stage. Instead of opening multiple tabs and doing comparative research, many consumers start and end with the AI’s response. Some will still dig deeper – about two-thirds say they often follow the links or resources cited by the AI – but others may not go beyond that initial summary. Younger consumers especially seem comfortable treating AI as a preliminary advisor. A 2024 Kaiser Family Foundation poll found 25% of 18–29 year-olds use AI chatbots for health info on a monthly basis, and a 2025 survey noted that nearly half of people under 35 have ever turned to AI for a health questiondriphydration.com. For Gen Z and Millennials, asking ChatGPT may be as reflexive as Googling was for Millennials and Gen X. This generational uptick suggests that as these cohorts age (and as AI tools become even more ubiquitous), consulting AI will become a normalized first step for self-care decisions.
Why are people making this shift? Aside from convenience, another driving force is the perceived scarcity of quick medical guidance from human providers. Reports of doctor shortages and long wait times abound, and many consumers (especially younger ones) feel their concerns are not always heard or addressed promptly in clinical settingsdriphydration.comdriphydration.com. In a survey by Drip Hydration, 34% of Americans – and a higher 39% of women – felt their symptoms had been dismissed by medical professionalsdriphydration.com. This frustration leads some to seek out information on their own. AI chatbots provide a non-judgmental, on-demand outlet for health questions, without the time pressure of a brief doctor’s visit. One telling data point: 43% of Americans say they use AI for health because it provides faster answers than waiting for a medical appointmentdriphydration.com. When faced with a new rash or a child’s mild fever, many would rather get immediate guidance online (even if imperfect) than sit anxiously for days until they can see a doctor. In essence, LLMs are filling a gap in immediate triage and information – a role that pharmacists and nurses have traditionally played for minor ailments, now scaled up digitally for anyone with an internet connection.
LLMs as Digital Pharmacists – Guiding Self-Diagnosis and OTC Choices
Large language models today effectively serve as digital pharmacists, advising consumers on everything from symptom management to over-the-counter (OTC) product choices. In pharmacies, a trusted pharmacist often assists customers by suggesting treatments for minor ailments, explaining how to use medications safely, and offering reassurance or referrals when something might require a doctor’s attention. Now, AI chatbots are taking on that advisory role in the virtual space, before consumers ever reach the retail shelf or clinic. Here’s how LLMs are guiding consumer self-care decisions in practice:
Symptom Assessment and Self-Diagnosis: When someone feels unwell, an LLM can help them interpret their symptoms in real time. For instance, a user might ask, “I have a sore throat and runny nose, what could it be?” The AI will typically walk through a brief differential diagnosis: it might say it could be a common cold, allergies, or strep throat, and then enumerate distinguishing signs (e.g. “If you have itchy eyes, it’s likely allergies; if you have fever or severe pain, consider seeing a doctor for strep testing”). This mirrors what a pharmacist or triage nurse might do when you describe symptoms – giving possible causes and advising on next steps. Crucially, the AI often provides a risk assessment: it may reassure the user that if symptoms are mild it’s probably viral and can be managed at home, but it will also include red flags (like difficulty breathing or high fever) that warrant professional care. In this way, the LLM offers reassurance for benign cases and urges caution when appropriate, functioning as a first-line triage. Many users find this helpful for managing health anxiety; about 31% of Americans say they use AI to reduce anxiety while waiting for a diagnosis or appointmentdriphydration.com. The AI’s “opinion” can calm nerves by indicating that symptoms are likely minor – or conversely, motivate someone to seek care sooner if the described scenario sounds concerning.
OTC Treatment Recommendations: After suggesting what the issue might be, LLMs readily recommend over-the-counter remedies and self-care measures – essentially pointing consumers to specific solution categories. Continuing the above example, the AI might add, “For a common cold, you can use OTC medications like acetaminophen (Tylenol) or ibuprofen (Advil) for pain and fever, and a decongestant for the stuffy nose. Stay hydrated and rest. For allergies, an antihistamine like cetirizine can help.” This kind of advice is extremely common in AI responses. In fact, when researchers evaluated ChatGPT’s answers to medical questions, they found it frequently suggested OTC drugs by name (generic and brand) for symptom relief. By providing these recommendations, the AI is shaping the consumer’s shopping list before they ever enter a pharmacy or open a retail website. A person who consults ChatGPT about heartburn, for example, may come away with the plan to “pick up an OTC antacid or acid reducer like famotidine” based on the chat. They’ll then go to the store looking for those specific items or ingredients. In essence, the AI can direct traffic toward certain product categories (analgesics, antihistamines, antacids, etc.), which has profound implications for OTC brands. Notably, the language models tend to be brand-agnostic in their default advice – they often mention the generic name (e.g. “ibuprofen”) and sometimes add well-known brand names in parentheses (e.g. “ibuprofen (Advil)”). This means consumers are primed with active ingredient knowledge more than brand loyalty when they reach the shelf. A savvy consumer might even opt for a store-brand generic if the AI didn’t emphasize any difference, potentially intensifying price competition in OTC categories.
Product Comparisons and Personalized Suggestions: LLMs also excel at comparing options, which is a key role human pharmacists play. Consumers often ask chatbots questions like, “Is it better to take Tylenol or Advil for my situation?” rather than seeking an open-ended recommendation. ChatGPT typically responds with a nuanced answer: for example, it might explain that acetaminophen (Tylenol) is often better for people who can’t tolerate NSAIDs or have sensitive stomachs, whereas ibuprofen (Advil) can help more with inflammation but should be taken with food and avoided by those with certain conditions. It will then tailor the advice to the scenario (e.g. “for a fever and general aches either is fine, but if you have inflammation or swelling, ibuprofen may help more”). This mirrors a pharmacist’s guidance, where individual factors (like stomach ulcer history, allergies, other medications) inform the recommendation. However, AI has limitations in personalization – it only knows what the user tells it. A critical difference is that a human pharmacist will proactively ask about contraindications (“Do you have any liver problems? What other meds are you on?”); an AI might not unless prompted. Nonetheless, users are learning to volunteer more context. Doctors advise that when querying AI for health, you should “plug in lots of details” about your age, medical history, etc. to get a more tailored answer. Many users follow this practice, essentially treating the AI as they would a telehealth provider. With enough detail, the chatbot’s advice can indeed approach the nuance of human counsel. For instance, an AI might recommend a specific allergy medicine if the user specifies, “I need something non-drowsy for daytime use” (likely suggesting a second-generation antihistamine like loratadine) versus a different suggestion if asked “What can I take at night for nasal congestion?” (perhaps an older antihistamine or a nasal decongestant spray). In surveys, understanding nuances like medication side effects and interactions is a popular use-case for AIdriphydration.com – one traditionally served by pharmacists. It’s not hard to see why: an LLM can instantly pull from its trained medical knowledge to explain that, say, pseudoephedrine (a decongestant) may cause insomnia or elevated blood pressure, whereas oxymetazoline nasal spray shouldn’t be used for more than 3 days to avoid rebound congestion. This level of detail empowers consumers to make informed product choices that align with their needs and constraints.
Providing Self-Care Instructions and Precautions: Beyond naming a medication, LLMs give context on how to use it and what else to do, much like a pharmacist’s advice when handing over an OTC product. They often include dosing guidelines or timing (though users must be cautious, as there have been instances of AI erring on specifics). For example, an AI answer to “I have a headache, what should I do?” might be: “You can take an over-the-counter pain reliever such as ibuprofen – typically 200–400 mg for adults, every 4-6 hours as needed. Make sure not to exceed the recommended daily amount. Also, drink plenty of water and rest in a quiet, dark room. If the headache is very severe or lasts more than a couple of days despite OTC treatment, consider seeing a doctor.” This kind of response is comprehensive: it pairs the product recommendation with home remedies (hydration, rest), and it gives a gentle timeline for medical escalation (if it doesn’t improve, seek medical advice). In doing so, the LLM positions itself as a responsible advisor, echoing standard pharmacist guidance: treat at home first, but know when to escalate. Indeed, many chatbots have a learned tendency to err on the side of caution in their final advice, often concluding answers with a version of “if you’re unsure or symptoms worsen, consult a healthcare professional.” This is partly a built-in ethical measure, but it also reinforces to the user that the AI’s role is informational, not diagnostic. Users still appreciate this, as it helps them gauge the seriousness of their situation. In surveys, a notable share of people (about 20%) say they even use AI to seek a “second opinion” or check if something might have been missed in their caredriphydration.com – not to self-treat entirely in isolation, but to be better informed for subsequent professional consultations.
Influence on Key OTC Categories: While AI chatbots field questions across the medical spectrum, certain everyday health categories see particularly heavy engagement. These tend to be the same areas for which consumers traditionally rely on OTC solutions and pharmacist advice. Below, we explore a few such categories and how AI’s guidance is impacting consumer behavior within them:
Pain Relief
Pain is one of the most common complaints prompting self-care – from headaches and migraines to muscle aches and menstrual cramps. It’s no surprise that users frequently ask LLMs about pain management. Queries range from general (“How can I get rid of a headache?”) to specific (“Can I take ibuprofen for a sprained ankle, and how much?”). ChatGPT and similar models provide structured advice here: they typically recommend appropriate OTC analgesics (acetaminophen, ibuprofen, or naproxen, often with dosages if asked), and sometimes non-pharmacological tips (cold or warm compress, stretching, hydration, etc., depending on the pain type). An interesting observation is that AI advice might influence which analgesic people choose. For example, if a user mentions stomach sensitivity, the AI may steer them toward acetaminophen over an NSAID, as it knows NSAIDs can irritate the stomach. If the concern is inflammation (say a sports injury), the AI will likely suggest an NSAID for its anti-inflammatory benefit. These nuances mean consumers are getting a form of triage and product filtering before purchase. Instead of randomly picking a painkiller or defaulting to what they used before, they might choose one based on the AI’s explanation of which is better suited for their scenario. This could reduce trial-and-error for the consumer, leading to higher satisfaction with the first product they buy – a good outcome if the advice is correct. On the flip side, it might also reduce brand loyalty. The AI tends to discuss the drug ingredient (e.g. “naproxen”) rather than endorsing a brand name like Aleve, unless specifically asked. That could level the playing field in a category where branding has traditionally been strong. Pain relief is also an area where overuse and safety are concerns (e.g. taking too much acetaminophen can harm the liver, mixing multiple pain meds can be dangerous). Ideally, the AI will caution users on these points, and often it does include warnings like “Do not exceed X tablets in 24 hours” or “avoid taking these two medications together”. Such guidance mimics what a pharmacist would stress, and if followed, it can prevent misuse. However, context is everything: the AI only warns about what it “knows” from the query. A troubling example was an AI that failed to ask about other substances and inadvertently okayed a dangerous combination – one documented case involved a patient who asked ChatGPT for a salt substitute for health reasons, and the bot recommended sodium bromide (a pool cleaner chemical), which the patient then ingested for months, resulting in poisoning and hospitalization. In pain management scenarios, a parallel risk would be if a user doesn’t mention a condition (like a kidney issue) and the AI suggests high-dose ibuprofen; a human pharmacist might have caught the omission by asking follow-ups. This underscores that while AI can guide pain self-care, consumer diligence is still required – they must provide thorough information and ideally double-check advice, especially for persistent or severe pain.
Digestive and Gastrointestinal Health
Digestive ailments – think heartburn, acid reflux, bloating, constipation, diarrhea – are another staple of OTC medicine and a frequent topic in AI health Q&A. Many such issues are episodic or chronic-but-manageable, making them prime candidates for self-care guided by information. Consumers ask LLMs questions like “What can I do for indigestion after eating?”, “Best remedies for constipation?” or “I have diarrhea; should I take something or let it run its course?”. The answers usually cover both lifestyle/home remedies and OTC products. For heartburn, for instance, ChatGPT might advise avoiding heavy meals and trigger foods, elevating the head during sleep, and then mention antacids (like calcium carbonate chewables) for quick relief, or H<sub>2</sub> blockers (famotidine) and proton-pump inhibitors (omeprazole) for persistent cases – with a note that PPIs take a day or two to fully work. In doing so, the AI essentially provides a mini-consultation akin to talking with a pharmacist or even a gastroenterologist in simplified terms. A user who was unaware of these medication classes might now go to the pharmacy specifically looking for a famotidine product for their nighttime reflux because the AI explained its benefits (e.g. “lasts longer than antacids and can prevent acid if taken before a meal”). Similarly, for constipation, the AI might walk through options: increase fiber and water, consider a gentle OTC laxative like polyethylene glycol (Miralax) or a stool softener, but avoid dependency on stimulant laxatives – again, advice that mirrors standard pharmacist counseling. By outlining the range of options, LLMs educate consumers on the OTC toolkit available to them.
An important behavior change here is that consumers may feel more confident managing ongoing issues. Someone with mild irritable bowel syndrome, for example, might regularly consult an AI for diet tips or OTC supplements (like enzymes or probiotics) to alleviate symptoms, rather than visiting a doctor for every flare-up. The AI can continuously provide reassurance – “bloating can often be managed with simethicone drops and avoiding certain foods” – and suggest when a symptom (like significant unintentional weight loss or blood in stool) is a red flag requiring a doctor. This can lead to a longer self-care trial period before seeking formal care, which has a double-edged sword: it might reduce unnecessary doctor visits for simple cases, but it could also mean delays in getting care for something more serious if the AI wrongly assures the user it’s “just indigestion.” The risk of misdiagnosis is real. As one study noted, ChatGPT’s accuracy in diagnosing conditions is mediocre (~<span style="white-space:nowrap">50% accuracy</span> in one analysis). So if a user presents ambiguous GI symptoms (which could be diet-related or something like an ulcer), the AI’s “best guess” might be wrong. This is where citations and emphasis on uncertainty are crucial. Encouragingly, LLMs often do qualify their statements with probabilities or by saying “I am not a doctor, but…”. Nonetheless, the consumer’s interpretation matters – if they latch onto the benign explanation and ignore the disclaimer, they might treat a serious condition with home remedies too long. There have been reports of AI missing serious conditions that a doctor caught, but also vice versa: in one anecdote, a patient with chronic pain consulted ChatGPT after seeing 17 doctors and the AI astutely suggested a diagnosis (a rare condition) that turned out correct. Digestive complaints often lie in a gray area where it’s hard even for professionals to immediately distinguish functional issues from serious ones, and AI is no different. Therefore, while LLMs act as a helpful digestive health coach – guiding use of OTC antacids, anti-gas pills, anti-diarrheals (like loperamide), etc. – both users and brands should be aware that misinformation or misplaced confidence can have consequences. Ensuring that AI outputs for these topics include clear signals about when to seek medical care is essential.
From a brand and product standpoint, AI advice in GI health tends to be descriptive of active ingredients and general measures. It might not mention a specific product brand unless that brand name has become synonymous with the ingredient (e.g. “Pepto-Bismol” might be mentioned alongside “bismuth subsalicylate” for diarrhea/nausea because it’s a very iconic product). This means brands in these categories should not assume consumers will specifically ask for them by name as much anymore – the consumer might just remember “bismuth” or “fiber supplements” from the AI chat. Brands will need to ensure their value proposition (e.g. faster relief, better taste, dual-action, etc.) is well-documented in reputable sources so that if an AI is drawing from medical literature or reviews, it might surface those differentiators. For example, if a particular antacid brand has clinical evidence of working in 10 minutes versus others’ 30 minutes, having that info in the public domain (and cited by, say, a health website) could lead the AI to mention it. Otherwise, the AI will treat products as interchangeable commodities, potentially accelerating commoditization in OTC.
Allergy and Respiratory Symptoms
Allergies – from seasonal hay fever to mild allergic reactions – are another arena where LLMs play advisor and influencer. Consumers commonly pose questions about allergy symptoms: “My eyes are itchy and I’m sneezing a lot – is it allergies or a cold?”, “What allergy medicine won’t make me drowsy?”, or “How can I treat allergies naturally?”. AI chatbots, drawing on extensive medical text training, can delineate the differences between an allergy and a viral cold (e.g. allergies usually don’t cause fever, allergy mucus is thin and clear vs. a cold’s thicker discharge, etc.), helping users self-identify what they’re dealing with. If it sounds like allergies, the AI will enumerate OTC antihistamines and other remedies. A typical ChatGPT answer might say: “For seasonal allergies, common over-the-counter antihistamines include loratadine (Claritin), cetirizine (Zyrtec), and fexofenadine (Allegra). These are non-drowsy for most people. If you have congestion, a nasal corticosteroid spray like fluticasone (Flonase) can help, or a decongestant.” Immediately, the consumer has a mini-formulary of options to consider. The mention of non-drowsy second-generation antihistamines is valuable – it educates users who might not know why Benadryl (diphenhydramine) makes them sleepy whereas Claritin doesn’t. Over time, widespread use of AI for such queries could actually shift product demand within the category: if most AI answers favor newer non-drowsy antihistamines for day-time allergy relief, consumers might move away from first-generation antihistamines except perhaps for nighttime use. Likewise, AI’s often state an important caveat: “Older antihistamines like diphenhydramine can cause sedation and are generally not recommended for routine daytime allergy treatment.” This reflects medical consensus and steers consumer behavior toward what specialists would recommend, not just what’s historically been popular.
Another area is asthma vs. allergies vs. other respiratory issues. People ask if their chronic cough could be allergy-related, or what to do about shortness of breath when pollen counts are high. AI will provide basic screening: if someone describes wheezing and chest tightness, the AI might suggest it could be asthma and advise seeing a doctor, possibly mentioning OTC allergy meds won’t suffice in that case and a proper inhaler or prescription is needed. In contrast, for mild symptoms it might reassure that OTC allergy meds and saline nasal rinses can manage it. Again, the AI here acts like a pharmacist who might say “This sounds beyond OTC scope, you may need an Rx,” vs. “This you can handle with what’s on the shelf.” We see boundary-setting in many AI responses. Indeed, surveys show about 20% of people use AI to help decide if they need to see a doctor or can self-treatdriphydration.com – essentially using it as a triage tool. Allergy sufferers, who often get the same treatments year after year, may especially appreciate an AI confirming that it’s reasonable to continue self-managing or suggesting when to consider allergy shots or a specialist if things are worsening.
For OTC allergy products, comparisons are key. Users often specifically ask, “Which is better for me, Claritin or Zyrtec?” or “Does XYZ allergy pill cause drowsiness?”. ChatGPT will typically summarize the differences: cetirizine (Zyrtec) might work faster or be more potent for some but has a slightly higher chance of causing mild drowsiness than loratadine (Claritin). It might mention that fexofenadine (Allegra) is another non-drowsy option. It could also bring up nasal sprays vs. pills if congestion is prominent. These detailed comparisons can heavily influence brand choice and product type. A consumer who always bought one brand may switch because the AI highlighted a drawback they weren’t aware of or a benefit of another product. For instance, if they learn that one antihistamine lasts 24 hours while another might wear off by evening, they might opt for the longer-acting one for convenience. Or if AI notes, “Loratadine is less likely to make you drowsy but some people find cetirizine more effective for symptom relief,” an allergy sufferer might decide to try cetirizine for potentially better relief, accepting a small risk of drowsiness. This nuanced decision-making used to rely on either personal trial-and-error or a conversation with a pharmacist or doctor. Now AI can supply the information instantly, leading to more informed consumer trial. Brands in the allergy space will need to be cognizant that consumers may approach the shelf with these AI-fed insights (“I heard this one works fastest,” “that one might make me sleepy,” etc.). Marketing claims will need to align with what AI is likely to say based on available evidence. If there’s misinformation floating around (e.g., an overstated side effect), the brand might consider countering it through educational content, because AI could be picking up and repeating those points.
One must also consider safety and misinformation in this category. Allergy meds are generally safe, but AI might not know a user’s full medical picture. For example, decongestants like pseudoephedrine can raise blood pressure – an AI would only warn about that if the user mentioned hypertension or asked specifically. A pharmacist behind the counter might ask about conditions before selling certain meds; an AI won’t unless prompted or it “thinks” of it. This gap means some at-risk consumers (elderly, those with multiple conditions) could get advice not fully tailored to their needs – a limitation of the AI pharmacist that users must remain aware of. Misinformation in allergy care might include old wives’ tales or unproven remedies. Generally, LLMs trained on medical sources stick to proven advice (they’re likely to recommend antihistamines, nasal corticosteroids, etc., rather than say “eat local honey” which is a common folk remedy for allergies but not strongly supported by evidence). However, if the user specifically asks about a home remedy, the AI might discuss it and could give it more credibility than deserved if its sources were not authoritative. It’s important that LLMs guide users back to evidence-based treatments – something brands and health professionals have a stake in ensuring.
Risks: Misinformation, “Hallucinations,” and Consumer Safety
While the benefits of AI guidance in self-care are clear, there is also a serious risk dimension that cannot be overlooked. LLMs like ChatGPT do not have guaranteed accuracy, and in health matters, bad information can be dangerous. We’ve touched on a few examples already, but let’s examine the key risks:
Inaccurate or Outdated Information: LLMs generate answers based on patterns in their training data, which might not always be up-to-date or correct. Medical knowledge evolves quickly – new drug warnings, revised guidelines, or recently discovered side effects may not be reflected in the AI’s responses, especially if its knowledge cutoff is months or years behind. Even within known information, the AI might recall details imperfectly, leading to subtle mistakes. For instance, it might confuse dosage units (giving mg instead of μg for a drug, which could be a 1000x error), or misremember an interaction (saying two drugs are safe together when they are not). According to one analysis in 2025, ChatGPT’s medical answer accuracy ranged wildly from 20% to 95% depending on the question domain. That inconsistency is a problem – users won’t know if the answer they got falls in the accurate bracket or not without external verification. Unfortunately, many people may not double-check if the answer sounds confident and authoritative, which AI outputs typically do. LLMs have a way of phrasing information in a very fluent, matter-of-fact manner, which can lull users into accepting it. It’s telling that when ChatGPT provides a lengthy explanation with medical jargon, it can create a “false sense of knowledge and reliability,” essentially masking errors behind fluent prose. The average consumer might not detect a mistake in that sea of information. A related issue is that AI often doesn’t cite sources by default (unless using a system like Bing’s which does). Without citations, users can’t easily verify claims. If an AI tells someone “Medication X is safe for pregnant women” and that person trusts it without checking, the results could be harmful if that statement was false or context-dependent.
Hallucinations and Fabricated Advice: One of the most notorious flaws of generative models is their tendency to “hallucinate” – i.e., produce information that is completely fabricated, even including fake references or statistics. In a health context, a hallucinated answer can be downright dangerous. The AI might invent a recommendation that no doctor would give. For example, an AI might erroneously state, “Clinical trials show that taking high-dose vitamin D cures migraines” and even concoct a citation to a non-existent study to back it up. A user following such bogus advice could forgo proper treatment or take inappropriate supplements in harmful quantities. Researchers have noted that these falsehoods are “hard to spot” because the AI’s explanation can be very detailed and plausible-sounding. Essentially, the model will say it with a straight face (since it has no concept of truth, just language patterns). If the user doesn’t cross-check the information on a reputable site, they might easily believe it. This risk is amplified when people specifically don’t want to see a doctor – they might be more inclined to believe an AI that tells them what they want to hear (confirmation bias), even if it’s a hallucination. There’s also the risk of fake “cures” or diagnostic traps. We saw an extreme case earlier: ChatGPT suggested an obviously inappropriate chemical for consumption. While that might be a rare and bizarre example, it underlines that AI is not bounded by common sense or legal approval the way professionals are – it could mention some fringe therapy or misinterpret a query in a way that leads to a harmful suggestion. Another example documented in medical blogs was an AI recommending an imaging test for a headache that was not indicated, just because it misinterpreted the context. If users act on these hallucinations (e.g., pestering doctors for unneeded tests or taking a substance that isn’t actually safe), it can cause real harm or at least confusion.
Overconfidence and Delayed Care: Even when the AI’s information is generally correct, there is the risk that a user will treat the AI’s word as the final word – overestimating the AI’s diagnostic ability. If an LLM tells someone their symptoms “sound like just a migraine,” that person might decide not to see a doctor, missing that their symptoms perhaps were of a more dangerous condition. One clinician noted that when an AI is wrong, “it can be pretty catastrophic,” especially if the user lacks the expertise to recognize the error. In the KFF poll, a majority of chatbot users themselves admitted they are “not confident they could tell apart true vs. false information” given by an AI. In other words, people know they might be out of their depth in verifying AI health advice – yet some will still rely on it out of convenience or hope. This dynamic can lead to delayed medical care, as individuals might self-treat based on AI guidance longer than is wise. They might also skip professional advice entirely for something an AI deemed minor, which could be dangerous if that assessment was wrong. The public health community is aware of this; surveys show mixed opinions on AI’s net effect, with around 21% of people thinking AI is helping health info seekers and 23% thinking it’s hurting – and over half simply “unsure”. That uncertainty itself is telling: we are in uncharted waters.
Misinformation Amplification: LLMs learn from the internet, and the internet has plenty of myths, biases, and inaccuracies. Without careful grounding, AI can inadvertently amplify health misinformation. For example, if many blog posts falsely claim that a certain supplement cures COVID-19, an AI might regurgitate that narrative if asked about COVID remedies. Or consider vaccine misinformation – an AI could potentially provide anti-vaccine talking points if prompted in certain ways, especially models not carefully aligned to avoid that. This is obviously a major concern. Big platforms and model developers put effort into moderation and have guidelines to prevent disinformation, but users have found workarounds or phrasing that can still elicit problematic responses. From the consumer side, one saving grace is that many users do remain skeptical. Even among those using AI, only 36% say they trust chatbots to give reliable health info (meaning the majority do not fully trust it). However, being skeptical in principle doesn’t always prevent someone from being influenced in practice – if the misinformation aligns with their hopes or biases, they might go along with it (“well, the AI said this herbal tea could detox me, maybe I’ll try it…”). Health misinformation can spread faster through AI if not checked, because an AI can produce a coherent narrative for a false claim that makes it sound quite convincing, and do so for millions of users simultaneously.
Lack of Personalization & Missed Context: As mentioned, AI doesn’t know you personally (unless you feed it that info, and even then it has no memory of past conversations unless it’s within the same chat session). This means advice isn’t personalized by default. Key personal factors – age, pregnancy status, chronic conditions, other medications, allergies – might be overlooked. A human pharmacist would usually ask about these when recommending something. An AI will only account for them if told. Many users may not realize they should volunteer such info, or they might not know which details matter. This can lead to one-size-fits-all advice that might not fit the user at all. For instance, a person with high blood pressure asking about cold medicine might get a list of common cold medicines, not realizing some (decongestants) could worsen blood pressure. The AI wouldn’t spontaneously warn them unless they specifically said “I have hypertension, what cold medicine is safe?” Similarly, dosing advice from AI might not account for a person’s smaller body size or kidney problems that a doctor would adjust for. This is a subtle risk – the AI’s answer might be technically accurate for a general adult, but wrong for that individual. Consumers must remember that chatbot advice is generic unless tailored, and even when they provide details, the AI might not always use them correctly or might forget them across a long answer.
Privacy and Ethical Concerns: While not misinformation per se, it’s worth noting as a risk that people may divulge a lot of personal health data to these AI systems, which could have privacy implications (chatbot queries might be stored on servers, etc.). Some users share medical reports or images with AI for interpretation, not realizing this data could be retained and isn’t protected by doctor-patient confidentiality. There is an inherent risk in trusting sensitive information to third-party AI services. If that data were misused or leaked, it could harm consumers. This concern might indirectly affect the reliability of advice too – if privacy issues lead users to withhold key info from the AI (out of caution), the advice they get will be based on incomplete information, and thus potentially off-target.
The consequences of these risks range from minor (wasting money on an ineffective supplement) to severe (health deterioration, adverse drug reactions, etc.). Real-world examples already illustrate both ends: On one hand, we have success stories like the “rabbit fever” case where a patient got a correct rare diagnosis from ChatGPT that doctors initially missed – a case of AI information actually improving care. On the other, we have the poisoning case where AI nearly cost a man his life by suggesting a toxic substance. There was also an anecdote of a young boy with a chronic condition that went undiagnosed by numerous specialists until ChatGPT pieced together the clues. These starkly different outcomes highlight that AI is not uniformly good or bad at this stage – it’s unpredictable. As one Stanford physician put it, “When it’s correct, it does a pretty good job, but when it’s incorrect, it can be pretty catastrophic.” This variance is why most healthcare experts strongly advise caution. Many suggest using AI as a supplemental tool – e.g., to prepare for doctor visits, gather questions, or do follow-up research – rather than as a standalone diagnostician or prescriber. Even savvy patients are warned: “Be very careful about using it for any medical purpose, especially if you don’t have the expertise to know what’s true or not”. In practice, that means double-checking AI advice against trusted sources or with a professional.
For brand managers and consumer health companies, these risks are critical to acknowledge. Misinformation can not only harm consumers but also erode trust in entire product categories or brands. If, for instance, an AI falsely claims an OTC product is dangerous (a hallucinated side effect), it could scare people away from helpful treatments. Conversely, if it over-recommends a product inappropriately, there could be backlash or regulatory scrutiny. Thus, accuracy and safety of AI-generated content is in the interest of all stakeholders. It’s part of why Google has, for example, collaborated with Mayo Clinic and Harvard experts for years to curate its health information and fight “cyberchondria”. But now with AI summaries sometimes leapfrogging those vetted results, ensuring the AI itself is reliable has become the new challenge.
Strategy for Brands: Building Authority and Accuracy Through Verified Citations
In a world where AI chatbots might recommend the next cough syrup or pain reliever to a consumer, health brands and retailers must adapt their strategies to remain authoritative and relevant. The key is to build trust and accuracy into the AI-driven consumer journey. How can companies do this? A cornerstone is ensuring that AI systems have access to verified, high-quality information about their products and category – and that this information is cited in responses.
1. Provide and Promote Authoritative Content: Brands should invest in creating expert-reviewed, fact-checked content about the conditions their products address and the products themselves. This content might take the form of informative articles on their websites, research whitepapers, how-to guides, FAQs, etc. Importantly, it should be hosted in places and formats that AI models are likely to draw from. LLMs are often trained on a vast swath of the internet, but not every site is weighted equally. Authoritative sources (academic journals, reputable health sites, governmental or professional organizations) tend to influence AI answers more. Therefore, a brand might consider publishing in collaboration with such sources – for instance, a pharmaceutical company could support a study published in a peer-reviewed journal about their OTC drug’s efficacy, or a consumer health brand might work with reputable sites like WebMD or Healthline to get their expert content featured. If ChatGPT’s training data (or a search-augmented AI’s results) include those sources, the AI’s answers will reflect that vetted information. The goal is that when a user asks about a product or its active ingredient, the AI’s knowledge is grounded in truth and not hearsay.
Brands should also use schema markup and clear data on their own sites to make factual information easy for AI (and search engines) to parse. For example, providing a well-structured table of drug facts, indications, dosages, and contraindications in text form (not just an image of a label) can help ensure that any AI that scrapes the site for information gets the correct details. Up-to-date information is crucial – if dosing guidelines change or new warnings are added, brands need to put that out publicly so AI models can be retrained or updated with it. In short, brands must be sources of truth in their domains.
2. Embrace Verified Citations in AI Responses: When an AI provides an answer with cited sources (as Bing AI, Perplexity, and other search-integrated models do), it dramatically increases user trust because they can see the origin of the information. Consumers can click through to verify, which acts as a safety net against hallucinations. As a strategy, brands and health organizations should encourage the use of citations and evidence by AI. One practical way is through structured Q&A content on platforms known for high-quality answers (for example, contributing to FAQ pages, Q&A forums like StackExchange health threads, etc., with rigorous answers). If an AI model finds a concise, cited answer to a common question on a respected forum, it might incorporate that with attribution. Another approach is for companies to collaborate with AI developers or data providers to feed verified information into the models. We are already seeing early moves in this direction – some LLMs allow plugins or retrieval from custom knowledge bases. A consumer health company could have an official plugin or API that an AI can query, ensuring that, say, product-specific questions get answered with the company’s verified data (within ethical bounds, of course, avoiding overt bias or promotion in a way that could be unsafe).
Citations are also an opportunity for brand visibility. If an AI consistently cites, for example, a Mayo Clinic page for information on heartburn, and a brand’s content is referenced on that Mayo page, the brand indirectly gains authority in the eyes of the consumer. Or if a model cites a company’s own whitepaper for a statistic, that is a huge credibility win (provided the whitepaper itself is credible). The platform, for instance, highlights the importance of citations in AI answers – it helps brands “find out which sources are informing the AI conversation about your brand”. This is valuable because it means brands can audit what information (and misinformation) AI might be using. If you discover the top sources are outdated or incorrect, you know where to focus corrective efforts (e.g., publish updated info, improve SEO for your accurate content so it outranks the old, etc.). In other words, citation analysis becomes the new SEO for AI. Just as companies used to optimize to appear on the first page of Google search results, now they will optimize to be among the cited sources an AI pulls in its synthesized answer. Being cited not only guarantees the user can verify the info, but it confers a sense of trust: “this answer was based on [TrustedSite].org or [FamousJournal].com, so it’s likely legitimate.”
3. Ensure Compliance and Accuracy to Avoid AI-spread Misinformation: Brands must scrutinize their own marketing and product claims in the AI era. Exaggerations or unverified claims won’t just be on a package or ad – if they seep into the web, an AI might pick them up and state them as fact to consumers, which could backfire legally and reputationally. For example, if a supplement brand’s website ambiguously suggests their product “cures” a condition (language not approved by regulators), an AI could ingest that and later tell a user the product is a cure. This could lead to consumer harm and regulatory action. It’s imperative that all public-facing content from brands is medically accurate and appropriately couched. Innovation directors and marketing teams should coordinate closely with medical advisors to ensure that what the AI might learn about their product will hold up to scrutiny. Additionally, if there is prevalent misinformation about a category (e.g., “vaccines cause X” or “ingredient Y is dangerous”), companies in that space might take a proactive role in combating it by publishing clear, evidence-based refutations. If the misinformation is beaten back in the data landscape, the AI is less likely to propagate it.
4. Leverage AI Monitoring Tools: The very technology that poses this challenge can also assist in managing it. There are emerging tools and platforms ( being one) that allow brands to monitor their presence in AI-driven search and conversation. These tools can show how often and in what context your brand or product is mentioned by AI assistants, what questions consumers are asking about your domain, and what sources are being referenced. For example, a cold medicine brand could discover that many people ask ChatGPT “What’s the best cold medicine for a stuffy nose?” and that the AI often answers with a competitor’s product (or a generic term). Knowing this, the brand can strategize – maybe create content comparing cold medicines, or ensure that positive reviews and data about their product’s efficacy are widely available for the AI to possibly incorporate. Monitoring also helps catch misinformation early. If an AI is giving a faulty answer about your product (“e.g., says it’s not safe for children when it actually is, per label instructions for certain ages”), you can address that by clarifying information online or even reaching out to AI developers with the correction if needed. In essence, brands should treat the AI answer space as a new battleground for reputation and visibility, akin to search engine result pages (SERPs) in the past. The difference is, in AI answers, there’s typically only one amalgamated answer shown to the user (often with maybe a few alternatives or a small set of sources). That makes it even more critical to be part of that initial answer.
5. Champion AI Literacy and Caution in Consumers: Brands can also play a role in educating consumers about the proper use of AI for health. For instance, a pharmaceutical company might run an online campaign or include in their FAQs: “It’s great to research your health questions – here are some tips for using tools like ChatGPT safely,” echoing what experts say. They could emphasize: always check that the advice is coming from a cited medical source, use AI as a second opinion not a final verdict, keep your doctor in the loop about what you learn, etc. This not only demonstrates corporate responsibility but also builds trust with the consumer base. If a brand’s messaging is transparent – acknowledging AI’s convenience but also its limits – consumers may actually trust that brand more, seeing it as a partner in their health journey rather than just pushing products.
One concrete tactic might be integrating a “Ask an Expert” feature on brand websites, where common questions (which people might otherwise pose to ChatGPT) are answered by medical professionals or validated by them. Even if AI still answers those questions elsewhere, the brand’s site can be one of the sources of truth that AI might pull from or that consumers can refer to for verification. In time, perhaps brands will collaborate with AI providers for verified brand-specific Q&A – similar to how some companies currently work with voice assistants (like Alexa Skills) to provide official answers about their products. For example, a vitamin brand could have an official plugin such that if a user explicitly asks the AI about that brand’s product, the AI can fetch the answer from the brand’s verified database. Care needs to be taken to keep such answers unbiased and helpful, otherwise users will lose trust if they sense it’s just marketing.
6. Regulatory and Ethical Compliance: With AI giving what can amount to medical or pseudo-medical advice, there may come regulations on how these recommendations are sourced and presented. Companies should stay ahead of this by aligning with best practices voluntarily. That could mean working with healthcare professionals to vet any AI-supported tool they deploy, sticking to evidence-based claims in all content, and possibly providing disclaimers where appropriate. For instance, if a brand utilizes an AI chatbot on its own platform (some have started doing this for customer service or FAQs), it should clearly disclaim that it’s not a medical professional and provide citations in its answers to maintain transparency. There is also an opportunity for industry coalitions to work on standards – e.g., a standard dataset of approved information for certain OTC ingredients that AI companies could use to train or fact-check their models. Being part of the conversation with regulators and AI developers can help brands ensure consumer safety is prioritized, which ultimately benefits everyone.
Top reasons consumers turn to AI for health questions. In a 2025 survey, respondents cited getting faster answers (43%) as the leading motivation for using AI chatbots in health, followed by a desire to understand medication side effects (35%) and to prepare for doctor appointments (31%) by gathering information and questionsdriphydration.comdriphydration.com. Reducing anxiety while awaiting care (mentioned by 24%) and simple curiosity about what AI would say (17%) were also factors. Notably, a portion use AI to avoid costs (23%) or seek second opinions (20%)driphydration.com. These reasons highlight that consumers primarily value the convenience, education, and reassurance AI can provide – benefits that brands can support by ensuring the information AI delivers about their products and categories is accurate and trustworthy.
By focusing on verified information and strategic engagement with the AI ecosystem, brands can turn the phenomenon of “AI as the new pharmacist” to their advantage. Rather than seeing it as a threat, they can see it as an extension of their consumer communication channels – one that, if managed well, can enhance consumer trust and satisfaction. The brands that succeed in this new landscape will likely be those who proactively supply the facts that AI assistants disseminate, thereby becoming a part of the trusted advice that guides consumer decisions.
Conclusion
The rise of large language models in healthcare advice marks a transformative moment in consumer behavior. AI-driven chats are becoming the first touchpoint for health information and self-care guidance, effectively acting as digital pharmacists that counsel consumers before any human professional or product interaction takes place. This shift brings both opportunities and challenges. On one hand, it can empower consumers with knowledge, convenience, and confidence – imagine millions of people getting instant answers about minor health concerns, leading them to effective OTC solutions or calming their worries. It also has potential to triage and direct people to proper care more efficiently (ensuring those who need a doctor get that nudge early). On the other hand, the pitfalls of misinformation, lack of personal nuance, and overreliance on AI are significant issues we must address.
For brand managers, consumer insights leads, and innovation directors in the health and wellness industry, the implications are profound. No longer is the consumer journey a simple linear path from symptom to search to store. It now includes an AI consultation phase that can heavily influence which products the consumer considers or whether they decide to purchase at all. Understanding what information these AI “consultants” provide about your category or brand is therefore crucial. The savvy organizations are those already monitoring AI responses, learning what consumers ask and how the AI answers, and then aligning their strategies accordingly. Does the AI consistently recommend a competitor or a generic term? That’s insight into consumer mindshare and possibly your content gaps. Does it provide correct usage instructions for your product? If not, there’s an education opportunity to ensure better content is available for it to learn from.
Moreover, as consumers increasingly flip from “Googling symptoms” to “asking ChatGPT,” traditional SEO and digital marketing tactics need to evolve. It’s not just about being on the first page of results anymore; it’s about being embedded in the answers that AI delivers. Share of voice in AI-generated answers could become a new key metric for brand visibility. Tools and analytics to measure that (like tracking citation frequency, sentiment of AI mentions, etc.) will likely become part of the marketer’s toolkit. Indeed, early solutions are appearing, promising to “turn AI search into brand growth” by helping companies optimize content for LLM consumption. The competitive landscape might shift as well – smaller or lesser-known brands could gain ground if AI treats them equally (for example, highlighting a generic product that has good reviews even if it lacks big advertising). Brand loyalty could be tested in new ways; consumers might ask AI, “Is Brand A better than Brand B for this?” and a fact-based answer could overturn years of brand messaging if Brand B objectively works longer or has better ingredients. Brands must be prepared for radical transparency in that sense – AI will compare and contrast products based on data, not just slogans.
Crucially, the industry and regulators will need to collaborate to maintain safety and ethics in this AI-mediated advice ecosystem. There may be calls for standards (perhaps LLMs that give medical or pharmaceutical advice should be certified or use only vetted data sources). Already, the FDA and other bodies are examining AI in healthcare, mostly focused on clinical use, but consumer-facing advisory roles may come under scrutiny too. Companies that stay ahead by championing accuracy and supporting beneficial uses of AI will not only avoid possible regulatory pitfalls but also earn consumer goodwill.
In summary, AI has indeed become a kind of new pharmacist – one that millions are consulting from the privacy of their homes. It’s changing how consumers diagnose, reassure themselves, and choose products. Like any disruptive technology, it brings a mix of enthusiasm and concern. For every person thrilled that “ChatGPT solved my problem in minutes,” there’s another who wonders “Is this advice trustworthy?”. The onus is on all stakeholders – AI developers, healthcare professionals, brands, and users – to make this phenomenon a net positive. That means harnessing the convenience and reach of AI while embedding it in a framework of credibility, verification, and human oversight. Brands who position themselves as champions of that balanced approach will not only influence consumer decisions in the short term but also build lasting trust that their priority is the consumer’s well-being in this AI-guided world. In the end, the most successful “digital pharmacists” may be those AIs that effectively channel the wisdom and integrity of real pharmacists and health experts – and it’s up to us now to feed them the right information and guardrails to do so. By proactively adapting to this shift, we ensure that AI’s influence on self-care remains a boon for consumers’ health and for the brands that serve them.