  The integration of AI into radiology introduces opportunities for improved
clinical care provision and efficiency but it demands a meticulous approach to
mitigate potential risks as with any other new technology. Beginning with
rigorous pre-deployment evaluation and validation, the focus should be on
ensuring models meet the highest standards of safety, effectiveness and
efficacy for their intended applications. Input and output guardrails
implemented during production usage act as an additional layer of protection,
identifying and addressing individual failures as they occur. Continuous
post-deployment monitoring allows for tracking population-level performance
(data drift), fairness, and value delivery over time. Scheduling reviews of
post-deployment model performance and educating radiologists about new
algorithmic-driven findings is critical for AI to be effective in clinical
practice. Recognizing that no single AI solution can provide absolute assurance
even when limited to its intended use, the synergistic application of quality
assurance at multiple levels - regulatory, clinical, technical, and ethical -
is emphasized. Collaborative efforts between stakeholders spanning healthcare
systems, industry, academia, and government are imperative to address the
multifaceted challenges involved. Trust in AI is an earned privilege,
contingent on a broad set of goals, among them transparently demonstrating that
the AI adheres to the same rigorous safety, effectiveness and efficacy
standards as other established medical technologies. By doing so, developers
can instil confidence among providers and patients alike, enabling the
responsible scaling of AI and the realization of its potential benefits. The
roadmap presented herein aims to expedite the achievement of deployable,
reliable, and safe AI in radiology.
