Although AI agents represent a nascent phenomenon in medicine, their potential to revolutionize clinical practice is profound when implemented appropriately. Ensuring their effective integration into clinical diagnosis and treatment requires exploring the “mind” of the agent by understanding its thought processes and decision-making mechanisms. 

Explainability is essential for clinicians who will only trust AI tools if these tools conceptualize data comparably to human physiological reasoning.

For instance, eye movements offer low fidelity yet insightful data on brain activity, providing a window into how an AI model processes information. Consider developing a model for cardiac disease classification using fundamental computer vision tasks inspired by the human visual pathway. Notably, my fellowship training is within the niche subspecialty of neurology encompassing neuro-eye disorders. 

By visualizing what the model “sees” to differentiate between disease and non-disease states, we can assess whether its feature detection aligns with clinicians’ visual interpretations of EKG waveforms characteristic of conditions such as heart attacks. This approach grants insight into the AI’s decision-making processes, which is indispensable for refining models: Flaws cannot be rectified without understanding the underlying rationale, including why specific features are prioritized in computer vision.