Agentic design patterns, read through a healthcare AI lens (jenniferjiangkells.com)

A recent exploration of agentic design patterns, as outlined in Anthropic's guide on Building Effective AI Agents, presents fascinating insights specific to the healthcare sector. The author began by analyzing common agentic patterns but found himself pivoting towards a more profound inquiry regarding verifiability in healthcare AI applications. This shift underscores a critical challenge in the field: determining which healthcare problems can be effectively addressed through AI and how to maintain transparency and simplicity in their implementations. The author's core takeaway emphasizes that while LLMs (Large Language Models) can operate with varying levels of autonomy, the complexity of healthcare demands careful considerations of verifiability for effective deployment. Various practical use cases were identified, such as generating structured clinical documents from unstructured text and triaging medical inquiries using AI. The discussion also highlighted the potential for agentic systems to autonomously reconcile healthcare data in standardized formats like FHIR, enhancing efficiency while maintaining safety protocols. Importantly, the relationship between verifiability and autonomy was scrutinized, suggesting that low-risk, self-verifiable tasks are more suited for fully autonomous AI agents, while critical clinical decisions still require human oversight. This examination ultimately invites the AI/ML community to rethink the landscape of healthcare AI, particularly concerning where trust and reliability can be established, aligning technical accuracy with ethical considerations in patient care.