Healthcare AI development often focuses heavily on model performance.
Accuracy, AUROC, sensitivity, specificity, calibration, and other metrics are important.
But none of them describe how a person interacts with the system.
A technically strong model can still create poor outcomes if its interface increases cognitive workload or its recommendations appear at the wrong point in a workflow.
Consider alert fatigue.
An AI model may correctly identify many clinically relevant events. If the system produces too many notifications, however, users may become desensitized to alerts and miss the most important ones.
Automation bias creates another risk.
When users perceive an AI system as highly capable, they may accept its recommendations without sufficient independent assessment. This makes the communication of uncertainty particularly important.
Human factors engineering can address these problems by studying how users perceive information, make decisions, respond to interruptions, interpret uncertainty, and recover from errors.
For healthcare AI, evaluation should therefore extend beyond model metrics.
Organizations should examine cognitive workload, alert burden, usability, decision time, override behavior, error patterns, and how AI changes the workflow.
Agentic AI adds another dimension.
When an AI system can perform actions rather than simply display information, the design must account for human supervision, confirmation requirements, escalation, and recovery from unexpected behavior.
The central principle is straightforward:
AI should be designed around real human work.
The objective is not to make humans adapt to machines.
It is to build systems that help humans perform better in complex healthcare environments.
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