Artificial intelligence (AI) is no longer operating quietly behind the scenes of healthcare. It is showing up in exam rooms, as patients arrive with AI‑generated summaries, chatbot-informed questions, and crowdsourced insights that shape clinical conversations before a physician ever speaks. These inputs, often shaped by large language models and non‑traditional digital sources, are influencing not only patient expectations but also the direction of clinical conversations.
This shift marks a meaningful change in how health information is accessed and interpreted. Patients are more engaged, more informed, and more confident in the questions they bring forward. In many cases, that engagement can be constructive. But it also introduces new complexity. Much of this information is generated outside traditional clinical governance structures. Regulators and physician organizations have warned that unvetted AI‑generated content risks introducing bias and undermining clinical confidence. Those concerns are now arriving directly in the exam room.
Healthcare leaders are now facing a reality that regulators, physician organizations, and global health authorities have been signaling for some time: AI is moving faster than the systems designed to govern it. As AI becomes more embedded in how patients understand their health, the ability of providers to engage with AI-influenced information safely and confidently has become a strategic imperative.
The trust gap behind the promise of AI
The promise of AI in healthcare has been discussed for years, from improving efficiency and access to supporting better decision-making. Yet for many organizations, tangible returns have lagged expectations. Experience across the sector shows that this gap is rarely about technology alone. It is about trust.
Health systems that struggle to scale AI often do so because foundational questions remain unanswered: Can the data be trusted? Are outputs explainable? How is bias identified and managed? Who is accountable when AI-influenced decisions shape patient care? Without clear answers, AI remains stuck in pilots or narrowly scoped use cases.
Leading providers are taking a different approach. They recognize that trust is the prerequisite for scale, not the outcome of it. Governance, explainability, and oversight are treated as design principles that allow AI to move into daily operations. When these elements are embedded from the outset, clinicians are better equipped to engage with AI-generated insights, whether those insights originate inside the organization or from patients themselves.
Why governance must start on day one
Recent guidance from regulators and physician organizations reflects growing alignment that AI governance cannot be retrofitted once tools are in use. Regulators have been clear that as AI-enabled tools proliferate across healthcare, oversight cannot stop at deployment. Ongoing validation, transparency, and post-deployment monitoring are essential to maintaining safety and trust. Global health authorities have cautioned that without strong governance, AI risks reinforcing bias, widening inequities, and eroding trust, particularly when patients rely on algorithm-generated information to inform care decisions.
For providers, this means governance must evolve beyond policies on paper. Effective AI governance establishes clear ownership, embeds transparency into model outputs, ensures data lineage is auditable, and creates mechanisms for continuous monitoring in real-world settings. These guardrails do not slow innovation. They enable it by creating the confidence required for clinicians and leaders to rely on AI-supported insights.
As AI-generated information increasingly enters the exam room through external channels, governance also becomes a communication enabler. Clinicians who understand the strengths and limitations of AI are better positioned to respond constructively while grounding decisions in clinical judgment.
From experimentation to execution discipline
Another lesson emerging from leading health systems is the importance of execution discipline. In today’s operating environment, defined by margin pressure, workforce constraints, and rising patient expectations, organizations cannot afford fragmented AI efforts or investments untethered from outcomes. Providers that are making progress with AI focus less on ambition and more on execution, prioritizing governed use cases where value can be measured, accountability is clear, and oversight is embedded from the start.
This discipline is particularly important as AI becomes more visible to patients. Tools that influence clinical conversations must be resilient, explainable, and trusted. Governance provides the structure that allows AI to transition from novelty to necessity.
Governance as a confidence builder, not a constraint
At its best, AI governance is not about restriction; it is about confidence. When AI outputs are explainable, auditable, and grounded in trusted data, clinicians and leaders can focus less on questioning validity and more on care delivery.
Healthcare leaders now face a clear choice. AI will continue to shape how patients engage with their health, whether organizations feel ready or not. Those that treat governance as a strategic capability rather than a back-office function will be best positioned to meet this moment. By embedding trust into AI from day one, they can protect clinical integrity, strengthen patient relationships, and ensure that AI becomes a durable asset, not a source of uncertainty.
Drew Corrigan
Drew Corrigan is the U.S. Healthcare Sector Leader at KPMG. Drew previously served as the Managing Partner for the KPMG Portland office and has more than 25 years of experience, primarily serving large not-for-profit health systems, academic medical centers, and payors.
