2.7 KiB
title, source, authors, affiliation, year, category, published
| title | source | authors | affiliation | year | category | published |
|---|---|---|---|---|---|---|
| Principled Uncertainty in Clinical AI: End-to-End Bayesian Modelling and Algorithmic Equity Auditing Across Multimodal Patient Data | arXiv:2606.09789v1 | Oladimeji Anthonio, Dimeji Abdulsobur Olawuyi, Oloruntoba Ajayi, Temiloluwa Aderemi, Joseph Odamo | Centre for Algorithmic Health Equity, University of Ibadan, FUTA | 2026 | cs.CY | 2026-06-08 |
Principled Uncertainty in Clinical AI: End-to-End Bayesian Modelling and Algorithmic Equity Auditing Across Multimodal Patient Data
Authors: Oladimeji Anthonio*, Dimeji Abdulsobur Olawuyi, Oloruntoba Ajayi, Temiloluwa Aderemi, Joseph Odamo arXiv: 2606.09789v1 [cs.CY] Published: 2026-06-08 Affiliation: Centre for Algorithmic Health Equity, Ìyàwó, Ibadan; University of Ibadan; FUTA Akure
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 equity across patient subgroups.
The architecture comprises 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, KL divergence regularisation, and an uncertainty calibration penalty.
Key Results:
- ECE = 0.096 (well-calibrated)
- Primary/rural facility patients: 15.3% uncertainty equity gap (p < 0.001, r = 0.698)
- Low SES patients: 6.8% gap (p < 0.001, r = 0.617)
- Elderly patients: 3.9% gap (p < 0.001)
- No significant sex-based disparity detected
Key Concepts
- epistemic-uncertainty — reducible, model-knowledge uncertainty
- aleatoric-uncertainty — irreducible, data-noise uncertainty
- uncertainty-quantification — probabilistic prediction framework
- bayesian-deep-learning — variational inference in neural networks
- expected-calibration-error — calibration metric (ECE)
- uncertainty-equity-gap — UEG equity metric
- uncertainty-disparity-ratio — UDR equity metric
- precision-weighted-fusion — multimodal late fusion
- mc-dropout — Monte Carlo Dropout for uncertainty
- algorithmic-equity — algorithmic fairness
- clinical-ai — clinical artificial intelligence
- variational-autoencoder — VAE foundation