As clinicians, we learn not to be agreeable, we learn to challenge and sit in discomfort. We ask the hard questions underneath the one being asked. That is the work these tools are not yet built to do.
Patients are trying to navigate insurance confusion, coordinate care, manage prescriptions, and understand the following steps that often feel unclear. Static chatbots were never built for this level of responsibility. Agentic AI is. Or at least that is the idea.
Healthcare payments are no longer about transactions. It’s about controlling the revenue cycle. And increasingly, that control is not sitting with ISOs.
These three patterns keep showing up, and each is much cheaper to prevent than to fix later.
These tools are worth implementing. They are not worth implementing without the right preparation.
Anthropic unveiled Claude Science, an AI workbench designed to run — not just assist with — pharma research. The company is seeking to build out its customer base in the pharma sector as it deepens its healthcare push ahead of a widely anticipated IPO.
This requires more than deploying the right technology. It demands a reevaluation of how these systems fit into the healthcare environment and how to bring patients and staff along.
Discover the top AI-powered EAP providers for 2026. Compare platforms like Kyan Health and Spring Health on triage speed, global reach, and clinical quality to transform workforce wellbeing.
We should ask a more nuanced question than "does AI work in drug discovery." Rather, we should ask which approaches are about to be proven, and which are about to be exposed, because the field is several bets, and they are not equally sound.
Zelis CEO Amanda Eisel discussed how AI can create value when it is applied to measurable outcomes: simplifying claims workflows, reducing administrative burden, strengthening payment integrity, improving the payments experience, and empowering members to navigate their healthcare financial journey with confidence.
Current AI tools often increase the disruption that radiologists already experience from using non-integrated platforms.
AI built on transparent, causal reasoning, systems that ground every output in validated biological mechanisms, show the pathway behind each recommendation, and cite their sources is what makes the difference between a tool that erodes clinical trust and one that rebuilds it.
By reducing administrative burden and redesigning workflows around human needs, it creates space for what matters most: connection between clinicians and patients.
AI isn’t failing because it lacks capability. It’s failing because it isn’t consistently reaching the moments where decisions are made.
For years the healthcare system has relied on the slow machinery of research — and that system is failing the people it was built for. It was designed for a world that operated with an abundance of caution and lacked the modern tools of today. That world no longer exists.
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AI adoption in healthcare isn't being led by innovation teams, it's the clinicians
A July 22 webinar, scheduled for 1 pm ET and sponsored by Verato, will explore why the next era of payer performance will depend on building a trusted data foundation that connects people, providers, and relationships across the enterprise.