RAISE -- Radiology AI Safety, an End-to-end lifecycle approach

AI in radiology requires multiple safety checkpoints, not just initial approval: The paper emphasizes that safety assurance must occur at three distinct stages—before deployment (rigorous validation), during real-world use (input/output guardrails to catch individual failures), and after deployment (continuous monitoring for performance changes and fairness issues).

"Data drift" is an ongoing concern even after AI systems are approved: Post-deployment monitoring must track how the AI's performance changes over time as patient populations and imaging practices evolve, requiring scheduled reviews rather than a "set it and forget it" approach.

No AI system can guarantee absolute safety, even within its intended use: The paper stresses that radiologists and healthcare systems should not expect perfect performance from AI tools, emphasizing the need for human oversight and collaboration rather than full automation.

Trust in AI must be earned through transparency and demonstrated rigor: The authors argue that AI systems should be held to the same safety and effectiveness standards as other medical technologies, with clear communication to clinicians about how the AI was validated and what its limitations are.

Solving AI safety in radiology requires collaboration across multiple sectors: Healthcare systems, AI companies, academic institutions, and government regulators must work together to address technical, clinical, ethical, and regulatory challenges—no single organization can ensure responsible AI deployment alone.