How HCPs are using AI search in 2026.

The prescriber's AI search behaviour changed faster than pharma brand teams anticipated, and it has changed in ways that matter commercially. According to a March 2026 IQVIA report, 54% of HCPs now use generative AI tools in clinical contexts — a figure that has risen steeply from near-zero just two years ago. The queries they run, the engines they trust, and the verification habits they have developed define exactly where your brand needs to be represented in the AI answer layer.

54% of HCPs are in the AI answer layer — and the platform they prefer is not ChatGPT

The physician AI landscape by platform

The headline adoption figure masks meaningful platform stratification. General AI tools like ChatGPT and Gemini have the broadest consumer reach, but the clinical AI platforms built specifically for verified physicians are where the high-stakes prescribing queries are concentrated. IQVIA's 2026 analysis reports OpenEvidence logs 18 million monthly queries from verified physicians — and in a single milestone session on 10 March 2026, OpenEvidence achieved 1 million clinical consultations in a single day. Meanwhile, the 2026 Doximity State of AI Medicine Report finds 94% of physicians use or are interested in AI — a number suggesting the remaining holdouts are dwindling rather than growing.

Platform preference divides along specialty lines. In peer physician ratings captured by IQVIA, DoxGPT is rated the best clinical AI by 61% of physicians surveyed, versus OpenEvidence at 26%. Specialists in academic settings tend toward platforms with visible citation links — Perplexity and OpenEvidence — while community physicians lean toward ChatGPT for fluency and speed. This split is commercially significant: optimising only for ChatGPT while your target prescribers use Perplexity builds a structural blind spot into your GEO program.

Sources: IQVIA March 2026; Doximity State of AI Medicine 2026; Spectrum Science 2026.

The accuracy problem that makes platform choice matter

Platform preference is not just about workflow convenience — it has a direct bearing on accuracy risk. A BMJ Open review of popular medical chatbots classified 50% of the medical information provided as problematic: inaccurate, incomplete, or potentially harmful. That baseline applies most acutely to general-purpose AI tools that have not been trained or curated specifically for clinical use. Specialist clinical AI platforms with physician-verification and real-time sourcing score materially better, but the residual inaccuracy rate across any AI system means that what your brand currently says in the AI answer layer may not reflect your label.

What HCPs ask — and where source quality determines the answer

Dosing and administration queries

The most common HCP query category is dosing. Physicians ask about starting doses, titration schedules, dose adjustments in renal or hepatic impairment, paediatric dosing, and maximum doses in combination regimens. These queries return specific numerical claims, and any inaccuracy carries direct clinical risk. The 50% problematic-answer rate from the BMJ Open study is most consequential here, because an inaccurate dosing answer is not merely unhelpful — it is a patient safety event in progress.

Brands that publish clear, structured dosing information in crawlable HTML pages rather than relying on PDF-format prescribing information perform substantially better on dosing query accuracy. The format difference is mechanically significant: LLMs extract text from HTML reliably; PDFs are indexed inconsistently and often stripped of formatting context that makes tables of dosing information interpretable.

Drug interaction queries

The second most common HCP AI query category covers drug interactions — CYP450 involvement, co-medication monitoring, interactions in complex polypharmacy patients. Perplexity, drawing from real-time indexed sources including FDA interaction databases, tends to return the most accurate interaction data. ChatGPT is reliable for well-established interactions but may miss recently identified signals. Gemini and Claude are more variable, sometimes drawing from consumer-facing drug interaction checkers that present interactions without clinical grading.

Comparative effectiveness queries

Comparative queries are commercially the highest-stakes category. Physicians ask directly: "How does Drug A compare to Drug B for Patient X?" The LLM's answer to that question can shape a prescribing decision. Brands that perform well in comparative queries share a specific characteristic: distinct, mechanism-differentiated positioning that an LLM can reproduce accurately in a comparison table. Brands described in language similar to competitors tend to be treated as equivalent, often yielding citation to whichever brand has deeper clinical evidence infrastructure in PubMed and society guidelines.

Off-label use queries

Off-label queries are commercially sensitive and structurally unavoidable. When physicians ask about uses beyond an approved indication, LLMs draw from the full scientific literature — not from the brand's communications programme. For oncology, neurology, and rare disease, off-label query volume is substantial. The AI answer layer is generating off-label brand mentions regardless of whether brand teams engage with GEO, because the published scientific literature already contains them. The strategic response is not to suppress off-label content (which LLMs cannot be instructed to do by manufacturers) but to ensure the approved-indication content is indexed so authoritatively that it frames all responses in which the brand appears.

Lung cancer: the source monopoly that defines HCP-visible answers

What HCPs see when they query NSCLC treatment

The May 2026 PharmaGEO public index provides a concrete illustration of how source dominance shapes clinical AI answers. In Perplexity — the platform preferred by academic oncologists — lung cancer queries are answered almost entirely from a single source cluster: NCCN. The NSCLC v5.2025 PDF accounts for 136 citation uses; the NSCLC landing page for 64; NSCLC v5.2026 for 18. Of approximately 258 total citation uses for lung cancer queries, roughly 218 are from nccn.org. The NCCN guideline is not a source pharma owns or influences directly — but it is the primary frame through which HCPs encounter drug mentions in AI answers to NSCLC queries.

The practical implication: for oncology brands, NCCN guideline inclusion is a prerequisite for visibility in the AI answer layer to the prescribing audience. An oncology product not named in NCCN guidelines is essentially absent from the AI answers that academic oncologists receive in response to clinical queries. This is a content environment pharma teams cannot replicate through owned-content investment alone — it requires engagement with the guideline process itself.

Consumer AI vs. HCP-specialised AI: the divergence table

Sources: BMJ Open 2024; IQVIA March 2026.

Where HCPs verify — and why the verification layer matters

Verification destinations define the full information chain

The majority of physicians using AI search do not treat the answer as final for safety-critical decisions. They verify — particularly for dosing, safety, and interaction information. The verification destinations are consistent across surveys: UpToDate for clinical accuracy checks, PubMed and Cochrane for specialists and academic physicians, national formulary resources for prescribing-specific details.

This verification behaviour has a direct implication for GEO strategy. If a physician queries ChatGPT about your drug and receives an incomplete answer, then verifies in UpToDate and finds an accurate description, the brand site plays no role in that workflow. The critical touchpoints are the LLM answer and the verification resource. Brand teams without a presence in clinical reference platforms — UpToDate, Lexicomp, the relevant national formulary — are missing key nodes in the HCP information chain that precede and follow the AI answer.

Three behavioural patterns that carry direct strategic implications

Across all HCP query categories, three patterns recur and are commercially actionable. First, physicians rarely scroll past the first synthesised AI answer. Unlike web search, where multi-link scanning is normal, AI search returns a single response and most users accept it without requesting follow-up sources. Being present in the first answer is not an advantage — it is the requirement.

Second, the phrasing of the LLM answer shapes clinical framing beyond the factual content. When an LLM frames an answer about a drug primarily around tolerability concerns — even accurately — that framing influences how the physician thinks about the drug in subsequent prescribing decisions. The emphasis distribution of AI answers has clinical influence that is independent of factual accuracy.

Third, HCPs have developed platform preferences that segment by specialty and setting. Specialists in academic medicine prefer Perplexity for citation transparency; community physicians prefer ChatGPT for fluency. A GEO program that treats all HCPs as a single audience optimising for a single platform will systematically under-serve one segment or the other. The 2026 data from IQVIA and Doximity make clear that this stratification is now established rather than emerging.

The payer and access query gap

A growing but underappreciated HCP AI use case is payer and access queries. Physicians use AI to understand formulary status, prior authorisation requirements, step therapy protocols, and patient assistance programme availability. LLM performance on payer and access queries is the weakest of all HCP query categories. Formulary information is time-sensitive and plan-specific; most LLMs return outdated or overly general payer information, often with appropriate caveats but little clinical utility. For brands where access is a key commercial challenge, the AI answer layer currently provides limited help and some risk: outdated formulary information may create inaccurate prescribing-decision expectations. The operational fix is structural: publish clearly indexed, HTML-formatted access and support pages with current prior authorisation criteria, updated on a defined review cycle.

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