Rhett Alden is CTO of Elsevier Health, a global leader in clinical decision support, medical education and healthcare technology solutions.gettyHealthcare organizations have spent years building sophisticated defenses against cyberattacks. Protecting patient records, restoring systems after breaches and maintaining uptime are priorities that have rightly dominated boardroom conversations and investment decisions. They remain essential, but they present a new challenge for enterprises.As AI becomes embedded in clinical workflows, a new and equally serious risk is emerging that existing cybersecurity frameworks weren't designed to address. The question is no longer only whether patient data is secure; it’s whether the clinical intelligence that AI delivers into care decisions can be trusted. A locked and protected system can still cause harm if the intelligence inside it is incomplete, outdated or unverifiable. Security protects the infrastructure. It does not protect the decision. For healthcare organizations, that frontier remains uncharted, and the window to get ahead of it is narrowing.Why General Purpose AI Falls Short In HealthcareAccording to my company's Clinician of the Future 2026 report, which surveyed 2,757 clinicians across 118 countries, around half of clinicians globally are already using AI tools for work purposes. Of those, the majority are using generalist platforms, not clinical-specific solutions. These tools were built for broad consumer and enterprise applications, not environments where AI-generated outputs directly influence clinical decisions.Collectively, these risks are significant and largely unmanaged. General-purpose AI tools in clinical environments do not offer reliable evidence traceability, operate under governance standards that aren’t rigorously stress-tested against clinical requirements, and take inconsistent approaches to privacy and security, particularly with respect to patient data privacy. When clinicians cannot verify the reasoning behind an AI response, the utility and integrity of clinical decisions come into question. Clinicians need—and patients deserve—answers grounded in verifiable medical expertise.The Clinician of the Future report makes the gap clear: only 37% of clinicians globally trust AI tools today, falling to 19% in the U.S. While AI adoption in healthcare surged in 2025, trust did not. The solution is not faster or more capable AI. Rather, clinical solutions leveraging AI should be architected and designed specifically for clinical environments, drawing exclusively from verified science and governed against the standards patient care requires.That architecture exists in the form of clinical-grade AI. Where general-purpose AI draws from broad, unvetted sources, clinical-grade AI is grounded in peer-reviewed, scientifically validated content, with answers traced to the exact evidence they were drawn from. Clinical-grade AI is updated every 24 hours, ensuring clinicians access the most current guidelines. It is also designed to augment clinical judgment, supporting the decision the clinician makes. ​Four Priorities For Clinical AIThe analogy to cybersecurity is instructive. A decade ago, many organizations treated cybersecurity as an IT issue rather than an enterprise risk. Data breaches and ransomware attacks changed that. Cybersecurity moved from the server room to the boardroom because leaders recognized that technology risk is business risk. A secure system reflects a high level of trust that businesses, users and customers value. AI integrity in healthcare has reached the same inflection point. An AI system delivering unverifiable clinical information compromises decision-making and confidence. Like cybersecurity, clinical AI systems demand governance and investment to realize the level of trust demanded. The organizations that waited on cybersecurity were more likely to find that the cost of inaction far exceeded the cost of preparation. Leaders who treat AI integrity the same way risk a similar outcome.​The practical shift requires four priorities:1. Evaluate AI through a clinical risk lens. Apply the same scrutiny to AI tools as any other critical clinical system. What was it trained on? How was it validated? Who is accountable when it is wrong?2. Make traceability non-negotiable. Sixty percent of clinicians in the report say transparent citations to peer-reviewed sources would increase their confidence in AI tools. Procurement decisions should reflect that.3. Integrate security, privacy and clinical governance. AI risk spans all three domains. Managing them in silos creates gaps that can compromise patient safety.4. Invest in purpose-built clinical AI. General-purpose AI has clear value for administrative workflows, but clinical decision-making demands tools designed, validated and governed for that purpose.For a healthcare institution, clinical AI can help ensure faster decision-making, improved productivity, lower administrative burden and reduced cognitive burden for clinicians. Healthcare leaders who frame clinical-grade AI as risk reduction (and not just efficiency) will likely find it easier to secure the organizational commitment meaningful implementation requires.ConclusionThe pressures on healthcare—cyber threats, operational strain and clinical complexity—are not easing. Strong infrastructure remains essential. But leadership in this environment means ensuring that what operates inside secure systems is as trustworthy as the systems themselves. Resilience is no longer only about protecting data. It is about ensuring the decisions that data enables are trustworthy.​Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?