AeroSpectra Sentinel: An Auditable LLM Prompt-Chaining Decision-Support Workflow for Acute Asthma Risk Assessment from Respiratory Sounds and Clinical Signals
Researchers have developed AeroSpectra Sentinel, a novel decision-support workflow that uses a five-stage large language model (LLM) prompt-chaining process for acute asthma risk assessment. This system integrates respiratory sound analysis, machine learning screening, and clinical feature fusion to provide auditable clinical reasoning. Evaluations showed that the LLM workflow with guardrails and FHIR schema validation achieved the strongest simulated safety and documentation consistency, though it is intended as a research prototype. AI
IMPACT Demonstrates a novel application of LLM prompt-chaining for complex medical decision support, potentially improving diagnostic accuracy and audibility in clinical settings.