---
title: "Principled Uncertainty in Clinical AI: End-to-End Bayesian Modelling and Algorithmic Equity Auditing Across Multimodal Patient Data"
source: "arXiv:2606.09789v1"
authors: "Oladimeji Anthonio, Dimeji Abdulsobur Olawuyi, Oloruntoba Ajayi, Temiloluwa Aderemi, Joseph Odamo"
affiliation: "Centre for Algorithmic Health Equity, University of Ibadan, FUTA"
year: 2026
category: "cs.CY"
published: "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
