What’s next for AI and health care?
This week in Los Angeles at HLTH’s annual ViVE conference, global health care leaders will gather to collaborate, learn and shape the next wave of digital transformation. The conversation will most likely shift from “what’s new” to “what’s next,” as C-suite executives concentrate on artificial intelligence, data interoperability, workforce enablement, and the policy and market forces that will determine how these innovations scale across real-world care settings. Here are some of the themes that will be explored at the event.
Health system and insurer adoption: From pilots to enterprise strategy
AI adoption by health systems and insurers serves as a clear indicator of how quickly digital innovation is moving from pilots to enterprise-grade strategy. Health systems are scaling AI for back office and workflow heavy use cases such as ambient documentation, revenue cycle management and prior authorization to protect margins in a high-cost, low-margin environment while supporting a broader push toward operational excellence and smarter systems. AI is also becoming a core lever for payers, with large insurers ramping automation in utilization management, fraud detection and customer operations, underscoring that AI is now embedded in the infrastructure of care financing and delivery rather than existing as a standalone experiment.
The emphasis on responsible, human-centered deployment is evident in how organizations position AI as both a cost-control mechanism and a growth driver. Leaders are justifying continued AI investment, even amid tight budgets, by linking it to quality, safety and more predictive, patient-focused care translating technology spend into tangible benefits for clinicians and patients. Early results from ambient scribing and similar tools show measurable time savings and more face-to-face interactions with patients, highlighting workforce enablement in action; the most impactful deployments are those that give meaningful time back to people, not just more data to dashboards.
Clinical and diagnostic innovations: AI at the point of care
The clinical agenda makes it clear that AI is no longer confined to reducing administrative burden; it is increasingly central to diagnostic accuracy, access and equity. Recent developments in cardiology, such as AI models capable of estimating heart function (for example, left ventricular ejection fraction) from limited imaging inputs, reflect a push to extend specialty-level capabilities into more settings and make advanced diagnostics more accessible at the point of care. These innovations bring the “connected data” theme to life by transforming fragmented clinical inputs into actionable, interpretable insights that support frontline decision-making.
Imaging is another focal point, with substantial funding flowing into companies developing AI for automated MRI interpretation. This wave of investment signals strong confidence that algorithm-driven workflows can relieve radiology bottlenecks and expand capacity in both hospital and outpatient environments. Health systems and investors increasingly view these tools not only as efficient plays but also as mechanisms to standardize quality across sites, reduce unwarranted variation and move more diagnostics closer to communities. In doing so, clinical AI becomes a bridge between innovation and access, aligning with a vision of more distributed, patient-centered care ecosystem.
Regulation and governance: Turning compliance into competitive edge
Discussions are unfolding against a backdrop where AI innovation is outpacing traditional policy frameworks, making regulation and governance central to digital strategy rather than peripheral concerns. The federal government has proposed streamlined certification requirements to help accelerate the deployment of AI technologies and is considering a unified national framework to reduce inconsistencies across states. Clear federal guidance will play a key role in determining how quickly health systems can transition from pilot projects to organization-wide AI adoption.
Concurrently, nearly every state has introduced some form of AI-related legislation, including requirements to disclose AI use to patients and potential constraints on insurers’ use of AI in utilization management. By positioning governance as a strategic enabler, regulatory readiness is a competitive advantage. Organizations that can demonstrate transparency, safety and trustworthiness will be best positioned to scale AI in sensitive, patient-impacting use cases.
The takeaway
AI in health care is moving decisively from pilots to enterprise strategy, reshaping how care is delivered, financed and supported. The organizations that lead next will be those that pair scalable AI with connected data, workforce enablement and strong governance to turn innovation into real‑world impact.
Learn more about what’s happening in health care in our industry outlook.
