Keywords
Modality Risk
Expert Routing
Missing Data
Calibration
Clinical Safety
Abstract
Research spanning trustworthy multimodal biomedical AI and biomedical signal representation increasingly joins methods that were developed for different objects and decisions. Here, trust-informed conditional routing among modality experts in biomedical classification is compared with adaptive Fourier decomposition combined with deep learning for spirometry interpretation to determine which claims can travel across those boundaries and which remain context dependent. The analysis combines two focal publications with 12 previously verified sources and organizes the evidence around modality risk, expert routing, missing data, calibration, and clinical safety. Rather than pooling incompatible outcomes, it compares research questions, representations, controls, and validation envelopes. The synthesis shows that modality risk cannot be interpreted independently of expert routing, while missing data determines whether an apparent improvement remains meaningful outside the original setting. The strongest claims are therefore those that expose sensitivity, failure conditions, and residual uncertainty. The article concludes with a research agenda built around transparent comparators, targeted stress tests, and evidence records that can be reused without overstating causal or practical reach.
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Copyright (c) 2026 Carson Benson, Chase Norton, Dalton Walsh (Author)