From Experimentation to Clinical-grade AI in Healthcare

Source Domain: emerj.com

The article highlights that while model capability for enterprise AI has reached a high level, the necessary enterprise readiness in infrastructure, security, and workflow is lagging.
The federal government’s reaction to a Request for Information on AI agent security shows a pressing need for stronger safety frameworks, signaling a gap between model capability and operational deployment.
Security concerns, such as identity management and access control, are major infrastructural issues that prevent autonomous systems from operating safely.
Healthcare specifically outlines the issues related to data readiness, interoperability, and auditability for ensuring AI systems operate within regulated workflows.
Enterprise readiness is emphasized as a bottleneck to the adoption of agentic AI, focusing mainly on three key areas:
- Infrastructure readiness must adapt from human-based to machine-based execution.
- Domain-adapted reasoning and fine-tuning are critical for reliable agentic performance.
- An autonomous security posture is necessary to manage the expanded attack surface and compliance needs due to machine-driven actions.

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