Paper Detail
Oladimeji Anthonio, Dimeji Abdulsobur Olawuyi, Oloruntoba Ajayi, Temiloluwa Aderemi, Joseph Odamo
Clinical artificial intelligence (AI) systems routinely produce predictions without principled quantification of uncertainty, limiting their trustworthiness in high-stakes medical environments. This paper presents an integrated research programme addressing two interconnected problems: (1) the development of a fully end-to-end Bayesian uncertainty modelling framework for multimodal clinical data, and (2) the application of calibrated uncertainty estimates as a formal measure of algorithmic equity across patient subgroups. We construct a probabilistic deep learning architecture comprising modality-specific variational encoders, a precision-weighted late fusion mechanism, and a decomposed uncertainty output head that separates aleatoric from epistemic uncertainty. The system is trained with a composite Bayesian loss incorporating binary cross-entropy, Kullback-Leibler divergence regularisation, and an uncertainty calibration penalty. We evaluate model calibration using Expected Calibration Error (ECE = 0.096) and conduct a subgroup equity audit across facility type, socioeconomic status, age group, and biological sex on a dataset of 1,000 simulated patients. Results demonstrate that epistemic uncertainty systematically identifies underserved populations: primary/rural facility patients show a 15.3% uncertainty equity gap (p < 0.001, effect size = 0.698), low socioeconomic status patients exhibit a 6.8% gap (p < 0.001), and elderly patients show a 3.9% gap (p < 0.001), whilst no significant sex-based disparity is detected. These findings establish that calibrated uncertainty is not merely a technical property of probabilistic models but constitutes an actionable equity signal with direct clinical relevance.
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@article{anthonio2026principled,
title = {Principled Uncertainty in Clinical AI: End-to-End Bayesian Modelling and Algorithmic Equity Auditing Across Multimodal Patient Data},
author = {Oladimeji Anthonio and Dimeji Abdulsobur Olawuyi and Oloruntoba Ajayi and Temiloluwa Aderemi and Joseph Odamo},
year = {2026},
abstract = {Clinical artificial intelligence (AI) systems routinely produce predictions without principled quantification of uncertainty, limiting their trustworthiness in high-stakes medical environments. This paper presents an integrated research programme addressing two interconnected problems: (1) the development of a fully end-to-end Bayesian uncertainty modelling framework for multimodal clinical data, and (2) the application of calibrated uncertainty estimates as a formal measure of algorithmic equit},
url = {https://arxiv.org/abs/2606.09789},
keywords = {cs.CY},
eprint = {2606.09789},
archiveprefix = {arXiv},
}
{}