The New Front Door to Healthcare

How AI Models Are Rewriting Pharma's Communication Playbook

A patient researching your therapy right now will likely consult an AI chatbot before calling their doctor. They'll trust that AI response more than your marketing materials and make treatment decisions based on information you didn't provide—and can't directly control.

This isn't hypothetical. Nearly 6 out of 10 U.S. patients now use AI tools like ChatGPT, Claude, Gemini, or Microsoft's Copilot as their first step for health questions.¹ Even more striking: 63% already consider AI health answers reliable.² At major medical centers, more than 60% of AI queries are health-related.³

Unlike Google search results that show multiple sources, AI models create single, confident-sounding answers by combining information from across the web. When your brand is missing from these responses—or when competitors' language dominates—the consequences reach far beyond marketing metrics into patient safety, treatment adherence, and healthcare access.

Healthcare's Evolving Information Gateway

Healthcare's information gateway has shifted repeatedly, and pharma has adapted each time. Physicians once controlled treatment information entirely. Managed care organizations became decisive gatekeepers in the 1990s. Direct-to-consumer TV ads created patient demand in the 2000s. Google became patients' first stop in the 2010s as they took increasingly active roles in treatment decisions.

Now AI models can serve as primary health advisors, giving patients two things they can't easily get from doctors: empathy and time. While Google usage continues to grow, AI adoption is accelerating faster. Recent data shows AI health queries growing 300% year-over-year while traditional search grows at 8%.⁴ Industry projections suggest AI will handle the majority of initial health information requests within three years.⁵

This transformation may be the most significant yet: patients who once browsed multiple sources are beginning to trust single AI-generated answers, and each AI model answers them differently. Unlike traditional healthcare stakeholders—patients, providers, and payers—AI models actively shape how all three perceive treatment options while serving none of their specific interests directly.

The Hidden Risks of Invisible Brands

Our testing across major AI platforms using common patient questions about usage, benefits, side effects, and costs revealed troubling gaps. One model provided dosing instructions that could trigger side effects and reduce effectiveness—essentially turning patient education into a safety hazard. Another cited a monthly cost of $500 and falsely attributed the pricing to a brand's website that didn't exist, when patient assistance programs typically reduce out-of-pocket costs to under $50.

When pharmaceutical brands fail to establish presence in AI responses, patients may assume treatments are ineffective or unaffordable before consulting providers. Caregivers might overlook beneficial therapies, especially problematic for rare conditions. Providers could miss treatment options during busy clinical decisions, and payers may rely on AI summaries dominated by competitor framing when making coverage decisions.

The preparation gap is striking: while 58% of patients use AI tools first for health questions and 67% of pharma executives expect patients to rely on AI for medication information within three years, less than 1% of companies say their AI strategies are mature.⁶

Four Essential Actions for Pharmaceutical Brands

1. Create Consistent Messaging Across All Touchpoints

• Develop core positioning language that clearly explains how your treatment works and what makes it different, using identical terminology everywhere
• Ensure FAQ sections, patient materials, and third-party health portals repeat key phrases consistently
• Track where competitor language dominates category discussions and develop counter-narratives

2. Structure Content for AI Understanding and Patient Usefulness

• Convert common questions into clear FAQ formats covering usage, benefits, risks, and realistic costs including assistance programs
• Write in plain language that prioritizes clarity over technical jargon or marketing copy
• Use structured data markup on websites to help AI models understand your content better

3. Build Credibility in Health Information Ecosystems

• Review major health websites like Drugs.com, WebMD, and Wikipedia to ensure accurate treatment information
• Support research publication using consistent, clear language that matches patient communication needs
• Monitor discussion forums where treatment conversations influence AI training data, with compliant response protocols

4. Establish Cross-Functional Ownership and Processes

• Create dialogue between medical, legal, marketing, and IT teams about AI's role in patient communications
• Assign clear responsibility for monitoring and improving AI responses as a fourth stakeholder alongside patients, providers, and payers
• Develop guidelines that enable compliant engagement, including updated social media policies for appropriate forum participation

Looking Forward: From Efficiency Tool to Influence Partner

Pharmaceutical marketing teams consistently focus on using AI for cost reduction and operational efficiency. This misses a more fundamental shift: AI models aren't simply tools for creating faster, cheaper content. They're becoming primary influencers of patient beliefs and behaviors.

As patients increasingly turn to AI for thorough, patient-paced discussions of their conditions and treatment options, healthcare professionals risk losing influence over critical decision-making moments. When brands are well-represented in AI responses, patients receive accurate information supporting better outcomes. When brands are absent or poorly represented, patients develop misconceptions that harm both welfare and commercial performance.

Unlike traditional marketing channels where visibility can be purchased, AI presence must be earned through strategic content creation and ecosystem engagement. Companies that recognize AI as a stakeholder to engage—rather than simply a tool to deploy—will establish lasting advantages precisely when narrative control matters most.

What Leaders Should Do Next

Start by testing what major AI platforms tell patients and providers about your brand using standard questions about usage, benefits, safety, and costs. Educate cross-functional teams on the gaps using real AI response examples to build informed confidence rather than fearful caution. Then focus on high-impact fixes: FAQ optimization, cost clarification, and ecosystem development—with proper social media policies enabling compliant engagement.

The pharmaceutical industry's careful approach to new communication channels makes sense, but the risks of inaction—patients receiving wrong instructions, assuming treatments are unaffordable, or missing beneficial options entirely—now exceed the risks of thoughtful engagement. This isn't about chasing the latest technology trend. It's about ensuring accurate health information reaches patients through the channels they're already using to make treatment decisions.

The companies acting now will influence how AI describes their therapies for years. Those waiting for clarity risk discovering that competitor narratives have already become the standard language for their therapeutic category.

References

1. Prophet Healthcare Survey, 2024
2. Annenberg Public Policy Center, "Many in U.S. Consider AI-Generated Health Information Useful and Reliable," July 2025
3. JAMA, "Health-Related Prompts in Large Language Models at Academic Centers," April 2024
4. SearchEngine Journal, "AI Search vs Traditional Search Growth Rates," 2025
5. Accenture Life Sciences Pulse Survey, 2023
6. McKinsey & Company, The State of AI: How Organizations Are Rewiring to Capture Value, March 2025