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https://www.nationalacademies.org/trb/events

AI-Enabled Aeromedical Safety Assessment Decision Support

This project will evaluate how artificial intelligence can support the Federal Aviation Administration's (FAA’s) Aeromedical Qualitative Hazard Assessment Framework while preserving human oversight, scientific rigor, transparency, traceability, and regulatory defensibility. The framework provides a structured method for assessing how medical conditions, treatments, and operational mitigations may affect pilot performance and aviation safety, but its application requires substantial expert effort. The research will identify assessment activities suitable for artificial intelligence (AI) assistance, including evidence identification, impairment-pathway mapping, operational scenario development, bowtie modeling, barrier evaluation, confidence assessment, and report preparation. Candidate technologies—including large language models, knowledge-retrieval systems, and automated reasoning tools—will be compared with expert-led assessments using representative aeromedical policy cases. Evaluation will address accuracy, completeness, consistency, reproducibility, traceability, workload reduction, and timeliness. The project will also examine potential biases, failure modes, evidence-quality concerns, and governance requirements. It will define the human-review responsibilities, technical architecture, data resources, validation methods, and controls needed for responsible operational use. Deliverables will include an assessment of AI applicability across the framework, a prototype AI-assisted workflow and decision-support architecture, case-study performance results, and an implementation roadmap. The findings will guide future FAA investments while ensuring that AI augments, rather than replaces, accountable expert judgment in aeromedical safety decisions.