Physicians are increasingly viewed not as passive users but as essential arbiters in AI-driven care. The article outlines how clinicians help steer model development, validate performance on patient data, and set safety thresholds that keep patient outcomes at the forefront. In practice, a human-in-the-loop approach is needed to interpret AI recommendations, flag uncertainty, and mitigate risks from data gaps, bias, and nonrepresentative populations before AI tools reach the bedside.
The piece highlights governance models that codify physician accountability for AI-assisted decisions, with emphasis on transparency, clinician education, and ongoing monitoring. It argues for explainable AI that supports clinicians rather than replaces judgment, and for seamless integration into clinical workflows, audit trails, and clear escalation paths when AI outputs conflict with patient context or standards of care.
