Ami B. Bhatt, MD, Chief Innovation Officer, American College of Cardiology. Opinions my own.getty​Not long ago, patients had to wait for a phone call to have their lab results explained. Clinicians who wanted to compare new trial data depended on journals, institutional summaries or conference presentations. Startups that wanted to influence care pathways needed to partner with hospitals.For much of modern healthcare, interpretation remained within institutional boundaries. That is not completely true anymore.Artificial intelligence has brought something new to healthcare: interpretation that happens outside traditional settings. Now, patients can upload lab results and get explanations in seconds. Clinicians can keep up with changing guidelines right away. Sponsors can check eligibility across thousands of records without having to review charts by hand.The barriers are opening up. This change is not just about technology. It is also about how the system is organized.Why Information Is No Longer Confined To InstitutionsFor many years, healthcare institutions created knowledge and decided who could access it. Academic centers did the research, professional societies explained the findings and health systems decided how and when patients could get information explained to them.This system was supported by scarcity. There were only a few experts, data was kept separate and computers had limited power. AI helps reduce this scarcity. Now, the ability to interpret information is available outside of institutions. This does not mean expertise is going away. Instead, the power to understand information is being shared more widely.Patients are now more active in understanding their health. They come prepared with summaries and questions. Clinicians do not have to rely only on memory or manual work. They have tools that quickly find important information. In this way, the balance of power has changed.How Sharing Information Changes ResponsibilityHowever, having access to information does not mean being responsible for the results. AI tools can provide explanations, but they are not responsible for patient care. They do not have legal or regulatory duties unless they are specifically designed for that purpose.As more people gain the ability to interpret information, responsibility should also be shared. Institutions can no longer see their main job as controlling information. Now, they need to set clear rules for how tools are used, check that they work safely and keep monitoring them after they are put in place.Institutions are moving from controlling information to guiding and supporting its use. Patients, clinicians and technology companies all need clearer frameworks for evaluating AI-driven insights and applying them responsibly. Healthcare institutions are uniquely positioned to help establish those standards.​The Real Risk: Not Just Access​Much of the debate around AI in healthcare focuses on whether patients should use it or whether clinicians should trust it. These are important questions, but they are no longer the main issue.AI is already entering clinical care, administrative workflows, patient decision-making and institutional operations. The harder question is whether healthcare has the infrastructure to evaluate, monitor, govern and improve these tools once they are embedded in care delivery.​Can institutions evolve fast enough to shape how distributed intelligence operates within clinical care and avoid fragmentation? If we build in validation, transparency and monitoring from the start, sharing power can build trustworthy healthcare delivery instead of weakening it.How The Exam Room Is ChangingThe most visible impact of this shift may be subtle. For example, a patient might come in after using an AI tool to explain their cholesterol results. A clinician may check an AI summary before seeing the patient. A trial coordinator might use automated prescreening instead of reviewing charts by hand.The way people interact has changed, even if the results are the same. In this new setting, a clinician’s value is less about having all the information and more about understanding details, explaining risks and taking responsibility for decisions. Institutions that accept this change can update their processes to support it. Those that do not may end up holding on to an outdated system based on scarcity.A New Definition Of AuthorityThe end of institutional gatekeeping does not mean the end of institutions. It means their role has to mature. In medicine, trust has never come simply from who holds the microphone; it comes from whether a recommendation can withstand scrutiny at the bedside, in the evidence and over time. In an AI-enabled healthcare system, credibility will belong to the organizations that can show their methods, validate their tools in real workflows, monitor performance across the life cycle and scale oversight to the level of clinical consequence. AI has already moved interpretive power beyond traditional walls. The question now is not whether patients and clinicians will receive guidance from new sources. They will. The question is whether our institutions are determined to reach beyond their walls to make that guidance trustworthy, accountable and worthy of the people whose lives may depend on it.​Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?