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