Is your brand appearing in the AI answer layer? How to measure LLM visibility

AI-powered search tools, such as ChatGPT and Google’s AI Overviews, are reshaping how HCPs and patients find health information online.  A modern organic strategy is about more than ranking well, it must account for both search visibility and AI visibility to stay competitive long-term. 

Healthcare brands that establish themselves as trusted resources within the AI-driven “answer layer” will earn stronger visibility, deeper credibility, and ultimately greater market share. There’s no silver bullet – it’s a process that starts with gathering the right insights, building an informed content strategy, planning and executing content updates, and ensuring your technical foundations support strong performance. 

But before any of that, there’s a critical first step: measurement.

Why does LLM visibility matter for pharma brands?

Both HCPs and patients are now using LLMs to explore symptoms, conditions, treatments, brands, and therapy areas at every stage of the customer journey. To engage meaningfully, your content must be part of the information these models draw from, or you risk losing to competitors who show up more consistently in AI-generated answers. 

Current research makes this shift impossible to ignore. 

What we know about consumer use of LLMs 

A U.S. study of “laypeople” found that 32.6% had used an LLM to research health-related topics such as symptoms, conditions, or treatments.¹ 

Another U.S. study reported that: 

• 75% of those surveyed said AI-generated responses often or sometimes provided the answer they needed.² 

• 63% found those responses somewhat or very reliable.² 

Europe shows similar patterns: 

• A 2025 representative German survey reported 20% of adults had used LLMs for health information.³ 

• A UK survey of 2,000 patients found 24% had already consulted tools like ChatGPT with younger users especially likely to “ask ChatGPT before their GP.”⁴ 

Trend data indicates rapid acceleration, with forecasts suggesting GenAI usage will reach the same level as traditional search by 2030.⁵ As AI models improve, so will users’ trust and reliance on their responses. 

Are HCPs using Generative AI?

Yes and adoption is growing quickly. 

• In the U.S., the AMA’s 2024 Physician Innovation Network Survey found 66% of physicians were using health-AI tools, including LLM-based systems up from 38% the previous year.⁶ 

• A UK-wide survey of GPs found 20% were already using LLMs in clinical practice, from documentation and differential diagnosis to treatment suggestions.⁷ 

LLMs aren’t a future behavior, they’re already shaping how both patients and professionals understand diseases, treatments, and brands. 

Why is it important to measure your LLM Visibility?

Before you can improve your presence in LLM-generated responses, you need a clear view of where you stand today. Measurement gives you a baseline, enabling you to benchmark against competitors and identify where you have the most potential to grow. 

With LLM measurement, you’ll understand: 

• How often your brand or product is cited across tools like ChatGPT, Gemini, Perplexity, and Google AI Overviews. 
• How prominently you appear – top positions vs. deeper in the answer (or not at all). 
• Which topics you’re visible for (disease state, therapy area, product class) and where the gaps are. 
• The sentiment and context surrounding your mentions. 
• Referral traffic arriving from LLMs. Although volumes may be modest, these visits offer meaningful trend insights and the users are highly qualified. Some analysis suggests LLM referrals could be worth 4.4x more than organic search traffic.⁸
• Your competitive position, including share of visibility and share of citations. 

How to design your measurement approach

1. Select your prompt set

Identify a broad list of queries your brand or therapy area should appear for spanning patient, HCP, and payer perspectives. Include both branded prompts (e.g., “What are the side effects of [brand]?”) and non-branded prompts (e.g., “What are treatment options for [indication]?”). 

2. Set prompts in your measurement platform

Tools such as SEMRUSH, Accuranker, and Omnia now support LLM visibility tracking. Start by assessing whether your current organic measurement tools offer this capability. If not, choose a platform based on both LLM needs and your broader organic performance requirements. 

When setting up, effective tagging is essential. Consider segmenting prompts by: 

• Query type – diagnosis, treatment, etc. 

• Brand presence – your brand vs. competitors. 

• Audience – likely patient vs. HCP queries. 

• Strategic importance – prioritizing prompts most aligned with your brand’s objectives. 

What happens next?

Once you’ve collected and analysed the data, this helps inform your content and optimisation plan to strengthen visibility. This work must tie directly into your broader content strategy, aligning with brand goals, audience needs, and your existing content calendar. Again, no silver bullets, but understanding your current AI visibility levels is the critical first step.  

LLMs are already shaping how audiences learn about conditions and treatments. With significant and growing usage across both the U.S. and Europe, visibility in AI-generated answers is now a strategic imperative, not a future consideration.

Sources

1. 1. Laypeople’s Use of and Attitudes Toward Large Language Models and Search Engines for Health Queries
   2. Annenberg Public Policy Center’s April 2025 health survey
   3. Who uses AI chatbots to obtain nutrition and healthrelated information? Analysis of a representative German sample by Laura M. König, Maren C. Podszun, Wolfgang Gaissmaier & Helge Giese (2025)
   4. From AI curiosity to connected care — Semble report (2025)
   5. When Will AI Search Beat Google? 2025–2030 Forecast
   6. American Medical Association. (2025). Physician Sentiments Toward Augmented Intelligence in Health Care: 2023–2024 Survey. Chicago, IL: AMA.