AeroSpectra Sentinel: An Auditable LLM Prompt-Chaining Decision-Support Workflow for Acute Asthma Risk Assessment from Respiratory Sounds and Clinical Signals
arXiv:2606.08247v1 Announce Type: cross Abstract: Acute asthma risk assessment requires rapid interpretation of respiratory sounds, oxygenation, airflow limitation, speech ability, work of breathing, mental status, and response to reliever therapy. Conventional audio-only classifiers can detect wheeze-like patterns but often lack transparent clinical reasoning and safe escalation logic. This paper presents AeroSpectra Sentinel, a client-side research prototype and decision-support workflow that combines short-time Fourier transform (STFT) respiratory sound analysis, lightweight machine-learnin
The proliferation of advanced AI models and the increasing availability of multimodal biometric data, including respiratory sounds, are converging to enable new diagnostic and decision-support tools in healthcare.
This development indicates a tangible step towards AI-powered diagnostic augmentation in critical care, improving efficiency and potentially reducing misdiagnosis rates in acute medical conditions.
The explicit focus on auditable prompt-chaining in decision-support changes how AI models might be integrated safely and transparently into clinical workflows, addressing a key barrier to adoption.
- · Healthcare AI developers
- · Patients with acute respiratory conditions
- · Pulmonologists and emergency physicians
- · Medical device companies
- · Traditional audio-only diagnostic systems
- · Healthcare providers resistant to AI integration
Improved early detection and management of acute asthma episodes using AI-assisted tools.
Accelerated development and regulatory approval for similar auditable AI decision-support systems across other medical specialties.
Increased public and clinician trust in AI diagnostics due to transparency and auditability, leading to broader adoption and integration into standard medical practice.
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