Why trust matters in AI-driven diagnosis
Editor’s note: Darja Irdam is partner at market research firm Hall & Partners.
Artificial intelligence is transforming health diagnostics. It can detect patterns even experienced clinicians might miss, analyze vast amounts of data in seconds and offer earlier, more personalized health insights. Only a few years ago, this level of precision would have felt like science fiction. Today, it is rapidly becoming part of mainstream health care.
But the rise of AI has also created a new kind of complexity, one that can leave patients, clinicians and even brands unsure how to navigate it. In a world where algorithms can help detect cancer, predict cardiovascular events or analyze genetic risk, the question is no longer simply what the technology can do – it is how people feel about using it.
That emotional dimension is often overlooked. Yet, the brands that succeed won’t be the ones with the flashiest algorithms, but the ones that make health care conversations simple, human and built on trust. The power of AI is only half the story. The other half is communication.
AI and health care: A high-risk environment where trust matters most
AI may be driving change across many sectors, but health care is one of the few places where the stakes are uniquely high. When an algorithm informs a diagnosis or flags a risk factor, the consequences affect people’s health, their treatment decisions and in some cases, their survival. A misunderstood message or poorly framed insight can have a significant emotional and practical impact.
This is why regulators have begun stepping in. Under the EU AI Act, any system used in diagnostics, life sciences or pharma is now classified as “high-risk.” That label doesn’t mean AI is unsafe; it means the people building and promoting these tools must meet higher standards of transparency, traceability and human oversight. High-risk systems must be able to show their workings, justify their outputs and operate within clear ethical boundaries that protect patients.
For developers and communicators alike, this creates both obligation and opportunity: the obligation to be clear and honest about what the technology is doing, and the opportunity to build confidence from the very beginning.
Turning algorithms into understanding
The irony of AI in diagnostics is that the more advanced the technology becomes, the harder it can be for ordinary people to grasp what is happening. Even clinicians can feel uncertain when tools deliver results using unfamiliar terminology or opaque logic. Patients often encounter AI-driven insights through online communities or advocacy groups and may feel unsure how to raise them with their doctor.
This is where communication becomes indispensable. People rarely need to know how a model was trained or what its error margins are. They want to know what the insight means, why it matters and what happens next. Effective communication translates complexity into clarity without stripping away nuance. It acknowledges the science but speaks in human terms.
Tone plays an important role. Many people find AI intimidating because it feels mechanical or detached. A warmer, more conversational tone can make a marked difference in how people experience a digital diagnostic tool. Even small decisions, such as choosing a friendly name, designing an approachable interface or adopting reassuring language, can make technology feel more like a partner in care.
Framing matters too. Telling someone their cancer risk has doubled sounds frightening. But if the shift is from 0.003 percent to 0.006 percent, the emotional reaction changes completely once the numbers are put into context. Communicating risk responsibly is essential if AI is to support, rather than alarm, the people who rely on it.
Most people are not seeking a crash course in genomics or machine learning; they want just enough information to feel confident and in control. Giving simple explanations first and then providing the option to explore the science in more detail, respects different levels of health literacy without overwhelming anyone.
Helping people have better conversations with clinicians
Despite speculation that AI might replace health care professionals, the reality is far more grounded. AI will not replace doctors, but it can change the quality of conversations patients have with them.
Many people hesitate to ask their general practitioner about an AI tool they found online for fear of seeming misinformed or too reliant on “Dr. Google.” Brands can help by giving people the confidence to raise these topics in their appointment. A clear explanation of how to start the conversation or what questions to ask can make a significant difference.
This works best when AI is framed not as a challenger to clinical expertise but as a companion that supports both clinicians and patients. When a brand positions itself as a bridge, helping people understand an AI-generated insight before, during or after a medical appointment, the technology becomes a facilitator rather than a disruptor.
Learning from companies that did it well – and those that didn’t
The health care sector has seen excellent examples of communication, and some that have gone spectacularly wrong.
Amgen’s lipoprotein(a) awareness campaign is one of the successes. Lp(a) is a lesser-known cardiovascular risk factor and explaining it requires careful balance. The campaign simplified something scientifically dense without flattening the science. It showed how complex information can be made meaningful and actionable.
On the opposite end lies Theranos, a company that promised fast, simple, accessible blood testing and built an inspiring story around that vision. But its promises were not backed by evidence. When the truth emerged, confidence evaporated overnight. The lesson for any brand working in AI diagnostics is clear: storytelling can help people engage, but transparency and evidence must sustain the narrative. If the story outruns the science, trust collapses.
These examples remind us that trust is built not only through innovation, but through communication that is truthful, empathetic and grounded in data.
Humanizing AI without misrepresenting it
Humanizing AI does not mean pretending the technology has emotions or intentions. It means designing and communicating in a way that makes the experience feel supportive, understandable and safe. This might involve explaining how an insight was generated, clarifying the limits of what the tool can and cannot do or making it clear that a clinician will review any results. Transparency reduces the “black box” feeling and reassures users that they remain in control.
Insight work is crucial here. People interpret risk and medical information in ways shaped by culture, personal experience and health literacy. Understanding these nuances makes it possible to create AI tools and communications that speak directly to people’s needs and concerns. The more deeply brands understand how people think, feel and behave in health care settings, the more likely they are to trust the innovations offered to them.
What health care brands must understand about trust
Trust in AI does not appear automatically. It grows when patients feel seen, supported and informed. It weakens when communication feels opaque or overly technical. And trust disappears entirely when claims are exaggerated or evidence becomes unclear.
This is why successful AI-driven diagnostic brands focus not only on what their technology can do but also on how it fits into real human lives. They explain where insights come from, set realistic expectations, prioritize safety and prepare for difficult questions. Most importantly, they aim not simply to impress people, but to empower them.
A final thought: Innovation only works when people trust it
AI is pushing health care forward at remarkable speed, but it will only fulfill its promise when people feel confident enough to embrace it. That confidence comes from communication that is clear, responsible and human-centered. It grows out of transparency, empathy and science that is explained rather than hidden.