Healthcare AI systems frequently operate across domains with different terminology, evidence standards, and professional responsibilities. A system capable of processing information from multiple domains does not necessarily possess the authority or validated capability to resolve every issue independently.
Developers can address this challenge by defining task boundaries, identifying decisions that require specialist review, and establishing structured escalation pathways.
For example, an agent may detect a potential medication-related problem, gather relevant laboratory results and medication history, and prepare an evidence summary for a pharmacist or clinician. The system supports the decision without automatically assuming responsibility for resolving every clinical uncertainty. Effective collaboration also requires clear handovers. The receiving professional should understand the problem, supporting evidence, unresolved questions, and actions already completed.
For agentic systems, routing should reflect clinical risk, task scope, and authorization requirements. Not every task requires escalation, but consequential decisions should not proceed beyond the system's validated capabilities. The objective is to build AI that coordinates expertise effectively rather than simply attempting to do everything itself.
Call to action: AI engineers, clinical informaticians, and healthcare professionals, what approaches have you found effective for integrating specialist review into AI-enabled workflows?