The biggest barrier to AI adoption in healthcare isn’t regulation. It’s trust.

Every week seems to bring another announcement of an AI-enabled diagnostic, clinical decision support platform, remote monitoring solution, or digital therapeutic promising to improve outcomes, reduce burden, or transform the patient experience. Investment continues to accelerate, regulators are evolving their oversight, and healthcare organizations are actively exploring where AI can create meaningful value.

Much of the public conversation has focused on regulation, particularly as the European Union begins implementing the EU AI Act while the United States continues to develop oversight through existing healthcare and technology frameworks. Those differences deserve attention. Organizations launching AI-enabled healthcare products across multiple markets will need to understand varying regulatory expectations, documentation requirements, and approaches to governance.

Regulation, however, answers only the first question every innovation faces.

• Can this product enter the market?

The more difficult question comes after approval.

• Will physicians trust it enough to incorporate it into clinical practice? Will patients feel comfortable relying on it as part of their care? Will healthcare organizations embrace it as something that improves outcomes rather than complicates workflows?

Those questions are not answered by regulation alone. They are answered through trust.

And trust rarely develops on its own.

Different regulatory paths lead to the same commercial challenge

The European Union and the United States have chosen different paths toward AI governance.

The EU AI Act establishes the world’s first comprehensive legal framework dedicated specifically to artificial intelligence. Healthcare applications classified as high risk face detailed expectations around transparency, governance, human oversight and ongoing monitoring. Developers are encouraged to think about explainability, documentation, and accountability from the earliest stages of product development.

The United States has taken a more evolutionary approach. Rather than creating a single AI law, oversight continues to develop through existing regulatory authorities, including the FDA, alongside established privacy, medical device and healthcare regulations. This framework provides flexibility while placing greater responsibility on innovators to demonstrate clinical value and navigate an evolving regulatory landscape.

From a policy perspective, these approaches are different.

From a commercialization perspective, they converge on the same reality.

Neither guarantees adoption.

Regulators determine whether products may enter the market. Physicians, patients and healthcare systems ultimately determine whether they become part of everyday care.

Adoption is a human decision

Healthcare has always placed a premium on evidence, but evidence alone has never guaranteed adoption.

History is full of clinically sound innovations that struggled because people did not fully understand where they fit, how they should be used, or why they represented a meaningful improvement over existing approaches.

Artificial intelligence raises those questions again, often with greater urgency.

Unlike many previous healthcare innovations, AI frequently operates behind the scenes. Clinicians may see a recommendation without fully understanding how it was generated. Patients may benefit from an AI-assisted diagnosis without knowing where human judgment ends and algorithmic analysis begins. Healthcare leaders may recognize the promise of AI while remaining uncertain about governance, accountability, or implementation.

These questions are entirely reasonable.

Organizations that acknowledge them openly are more likely to build confidence than those that assume superior technology speaks for itself.

Explainability is becoming a competitive advantage

Explainability has traditionally been discussed as a regulatory requirement.

Increasingly, it represents something much more valuable.

It is becoming a competitive advantage.

Healthcare professionals want to understand how recommendations are generated, what evidence supports them, where human oversight remains, and what safeguards are in place when uncertainty arises. Patients want reassurance that technology is strengthening, rather than replacing, the relationship with their healthcare provider.

Clear answers create confidence.

Vague answers create hesitation.

Organizations that invest in helping people understand AI will often find themselves competing against products with similar technical capabilities. In those situations, confidence becomes a meaningful differentiator.

Communication and education should begin before launch

Many healthcare organizations still think of communication as something that begins once development is complete and regulatory approval has been secured.

Artificial intelligence challenges that sequence.

Communication and education now play an active role in commercialization long before launch. Scientific evidence, physician education, patient understanding, stakeholder engagement, and transparent discussions about data, privacy and human oversight all influence how AI-enabled products are perceived once they enter the market.

This work cannot be delegated to marketing after approval.

It should begin while products are still being developed.

Organizations that integrate regulatory strategy, clinical development, and communications planning are better positioned to build confidence throughout the product lifecycle rather than attempting to create it after launch.

The same challenge exists across healthcare

Women’s health provides a compelling illustration of these dynamics. AI is rapidly transforming fertility care, maternal health and menopause management, hormone health, longevity, and more accurate diagnostic testing, creating opportunities to improve access, personalize care and support clinical decision making.

The same questions emerge across diagnostics, clinical decision support, remote monitoring, digital therapeutics, predictive analytics and AI-assisted imaging.

• How was this recommendation generated?

• What role does the clinician continue to play?

• How should patients interpret these results?

• What evidence supports this approach?

Regardless of therapeutic area or technology, organizations face the same challenge: translating sophisticated innovation into something physicians trust, patients understand and healthcare systems are willing to adopt.

Global launches demand more than regulatory compliance

Organizations introducing AI-enabled healthcare products internationally face another layer of complexity.

Scientific evidence should remain consistent across markets.

Communication rarely can.

Healthcare systems operate differently. Cultural expectations influence how innovation is perceived. Regulatory priorities shape the questions physicians and patients ask before they are willing to adopt new technologies.

Successful global commercialization requires more than translating materials into different languages. It requires understanding how trust is established within different healthcare systems and adapting communication accordingly while maintaining scientific consistency.

Organizations that succeed globally will recognize that compliance and communication are complementary disciplines rather than sequential activities.

Looking ahead

Artificial intelligence will continue to reshape healthcare over the next decade. Regulatory frameworks will mature. Clinical evidence will expand. The technology itself will become increasingly capable.

The organizations that lead this next chapter are unlikely to distinguish themselves through algorithms alone.

They will distinguish themselves by helping people understand those algorithms.

Healthcare has always advanced through scientific innovation. It has also advanced because physicians gained confidence, patients felt informed, and healthcare systems recognized meaningful value.

Artificial intelligence will be no different.

Regulation may open the door.

But trust is what brings healthcare through it.

This article reflects a collaboration between Solaris and AbelsonTaylor Group, two independent healthcare communications organizations that share a commitment to helping healthcare innovators communicate more effectively and build trust with the audiences they serve. Through MISSION Health Hub—a flexible, global network of like-minded, independent healthcare agencies united by the belief that collaboration beats competition—our organizations bring complementary expertise across markets and disciplines. Together, we aim to advance healthcare communications by sharing perspectives that help our clients—and ultimately patients—benefit from more effective engagement.

Claire Dobbs is chief executive, Solaris Health.  Janet Barker-Evans is executive VP, chief creative officer, AbelsonTaylor Group.