Artificial intelligence is rapidly becoming crucial to how people search, read, and make decisions online. Broader consumer behavior underscores how fast AI is becoming part of everyday life. One-third of U.S. adults now report having used an AI chatbot. Among younger people, the numbers are much higher: 71% of young adults report moderate-to-high knowledge of AI compared to 33% among older adults.
How healthcare organizations choose to integrate these technologies has vast implications for the future of health. Likely many of these changes will be a net positive: some studies suggest that even non-specialized AI systems may soon be capable of more accurate diagnosis than medical professionals. But even though the emergence of Large Language Model (LLM)-driven AI presents unprecedented opportunity for the healthcare industry, it also presents risk: not just in how medical tools, devices and software integrate these technologies, but in how health information is managed and accessed online.
Today, those changes are most obvious in the world of online search and discovery. For healthcare providers, the transition from keyword search to a smarter ‘query’ and ‘answer’ system presents a tremendous opportunity to better capture how people learn and interact with healthcare information online. Search behaviors have been especially affected by the emergence of Google’s AI Overviews. These automated summaries appear at the top of search engine results pages (SERPs), offering users quick answers drawn from various sources.
Our team recently performed analysis of more than 100 healthcare websites, which shed light on what factors actually matter when it comes to appearing in these AI-generated summaries.
Perhaps our most striking finding: AI Overviews appeared for 99.2% of healthcare queries in our sample. When one recent study from MIT finds that patients are likely to overtrust inaccurate AI medical answers, that 99.2% can become a major point of contention. For an industry where information accuracy is non-negotiable, that prevalence means almost no user journey is untouched by AI.
With patients, caregivers, and consumers whose daily habits now include AI, it’s crucial that healthcare organizations understand how increasingly popular AI systems like Google’s AI overviews are answering health questions. Combined with broader adoption trends, these findings highlight the profound changes underway in how information is produced, delivered, and trusted.
A Response-First World
One of the most striking findings from Google’s patents is that AI Overviews are generated before sources are attached. The system drafts its response first, then looks for evidence to support it.
This reverses the logic of traditional SEO. Historically, content creators optimized for rankings in the hope of being surfaced in response to a query. In a response-first world, the task is different: marketers must anticipate the kinds of answers an AI is likely to generate and align content with those expectations—without sacrificing accuracy or compliance.
In healthcare, where misinformation carries real consequences, this new paradigm raises critical questions. What happens when AI fills gaps with incomplete or inaccurate summaries? And how can trustworthy providers ensure their content is consistently selected to support AI-generated answers?
What the Data Shows
To study how healthcare queries affected search, our team mapped AI Overviews for healthcare queries, capturing data from both the overviews themselves and the corresponding first and second page SERP results, analyzing both traditional and AI results by reading level, word count, domain types, page structure elements, and structured data presence. We also extensively studied Google patents that outline how exactly their AI system responds to a query.
Our work revealed several important patterns about how healthcare content appears in AI Overviews:
- Reading level parity: Complexity isn’t a differentiator. Both AI Overview and standard SERP results averaged an 8th-grade reading level. Clear, patient-friendly language seems to be a baseline requirement, not a differentiator.
- Word count moderation: Featured content tended to be just slightly shorter than key word equivalents. Overly long-form articles were less likely to be selected, but the gap between average word counts in SERP results vs AI overview results was small.
- Structural fundamentals: Every page in AI Overviews had proper titles and header tags. Pages missing these basics rarely surfaced. Structured design remains a must-have.
- Domain signals: .org domains appeared more often in AI Overviews compared to .com sites, hinting at a preference for perceived non-commercial authority. This reinforces the importance of credibility and trustworthiness as signals.
- Roughly half of first-page SERP results also showed up in AI Overviews, compared with less than a third from page two. While the SEO best practices that land you on page one are still best, landing on page 2 doesn’t necessarily preclude AI from pulling your content.
These findings illustrate that AI systems inherit many traditional SEO signals: external indicators of quality content are still key.
Implications for Healthcare Organizations
Some major takeaways from our research stand out for healthcare organizations.
Perhaps the most clear lesson: Don’t abandon SEO fundamentals. Structured data, clear headings, and question-answer formatting remain essential. AI Overviews draw heavily on these signals in just the same way that traditional SERP results do.
Then how can healthcare organizations go beyond those fundamentals to prepare their offerings for AI search? Conduct AI Overview gap analyses: by comparing existing content against what AI Overviews display for target queries, healthcare providers can identify mismatches and improve their content accordingly.
Of course, that optimization cannot sacrifice accuracy. Writing for AI must never override the responsibility to provide reliable, compliant information. When AI is used to summarize complex medical information, the risk of hallucination—or a plausible but incorrect output—becomes serious. Unlike retail or travel, where a misleading answer might cost time or money, in healthcare it can delay diagnosis or skew treatment decisions.
In healthcare, accuracy is paramount – it also is brand equity. For marketers, that means content strategies must go beyond visibility: building trust by producing accurate, user-centered information that AIs can safely draw upon.
While the fundamentals to quality content may in many ways remain the same, AI Overviews are not just another SERP feature. They represent a shift from ranking content to assembling answers. For sectors like healthcare, the implications are profound. Those who build strategies around accuracy, compliance, and user trust will define the standards others must follow.
