At their best, conversational AI tools make health information more accessible. They explain complex topics in clear language, respond to follow-up questions, and adapt tone and depth to the user’s needs. For people who may feel rushed in consultations or overwhelmed by dense medical content, this accessibility is genuinely valuable. A strong example of such a tool, Patiently AI, was recognised at the Communiqué Awards in 2025 as the winner of the Progress Award.

These tools also help users organise information. Rather than trawling through multiple sources, patients can ask open questions and receive structured summaries that help them think through symptoms, conditions or treatment pathways. In that sense, these tools often act as guides rather than originators of information.

This reflects a wider expectation shift. Consumers are used to personalised digital services elsewhere in their lives and they increasingly expect the same from health-related interactions.

Alongside the benefits, there are clear limitations and growing criticism. One concern frequently raised is data confidentiality. Even when platforms state that health data is handled carefully, users may not fully understand how their inputs are stored, processed or used. In a healthcare context, perceived ambiguity around privacy can quickly undermine trust.

There are also clinical limitations. AI tools rely on what users choose to share. They cannot see the full medical picture, assess risk holistically or take responsibility for outcomes. When responses are delivered fluently and confidently, there is a risk that nuance is lost and uncertainty understated.

On one hand, there is clear demand for more accessible, conversational health content. On the other, there is growing recognition from tech companies that health is not just another information category. Accuracy, context and accountability matter more.

For pharma, this creates an interesting dynamic. Technology platforms are experimenting in public, adjusting their approach as risks become clearer. Pharma, by contrast, operates in an environment where caution is embedded from the outset.

Rather than trying to match the tone or speed of AI platforms, pharma can differentiate itself through credibility and transparency while creating accessible information. Health communicators have an important role to play:
• Raising standards around health literacy, helping patients understand uncertainty, evidence quality and appropriate next steps
• Blending public relations (PR), search engine optimisation (SEO), content expertise, earned media, expert voices and thought leadership: this influences LLMs, and pharma needs to optimise for this
• Being explicit about data responsibility, reinforcing the value of trusted, ethical handling of health information
• Supporting healthcare professional conversations, acknowledging that AI tools may now shape the questions patients bring into the room.

LLMs are changing how health information is accessed and interpreted. At the same time, the pullback seen elsewhere shows that even the largest tech players are still working out where the boundaries should sit.
For pharma, the opportunity here is less about disruption and more about definition. The industry’s role is to help ensure that faster, more conversational access to health information does not come at the expense of trust, privacy or patient safety.
Handled well, this shift can strengthen pharma’s position as a responsible, steady presence in a rapidly shifting AI landscape.