Principled Uncertainty in Clinical AI: End-to-End Bayesian Modelling and Algorithmic Equity Auditing Across Multimodal Patient Data

• URL: http://arxiv.org/abs/2606.09789v1
• Date: Mon, 08 Jun 2026 17:44:15 GMT
• Title: Principled Uncertainty in Clinical AI: End-to-End Bayesian Modelling and Algorithmic Equity Auditing Across Multimodal Patient Data
• Abstract summary: This paper addresses two interconnected problems: (1) the development of a fully end-to-end Bayesian uncertainty modelling framework, and (2) the application of calibrated uncertainty estimates as a formal measure of algorithmic equity.<n>We construct a probabilistic deep learning architecture comprising modality-specific variational encoders, a precision-weighted late fusion mechanism, and a decomposed uncertainty output.<n>We conduct a equity audit across socioeconomic status type, age group, and biological sex on a dataset of 1,000 simulated patients.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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. 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.

Related papers

• Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts [1.2407987412519395]
  NITROGEN is an imputation-free transformer that jointly models within-patient feature dependencies and between-patient relational structure.<n>We trained NITROGEN on ADNI, and evaluated it on two independent cohorts.
  arXiv  Detail & Related papers  (2026-07-13T15:05:26Z)
• RISED: A Pre-Deployment Safety Evaluation Framework for Clinical AI Decision-Support Systems [0.0]
  We propose a five-dimension pre-deployment evaluation covering Reliability, Inclusivity, Sensitivity, Equity, and Deployability.<n> RISED is released as an open-source Python package that supplies the quantitative verdicts existing clinical AI reporting standards require.
  arXiv  Detail & Related papers  (2026-05-13T02:17:13Z)
• Towards Trustworthy Depression Estimation via Disentangled Evidential Learning [50.22167852149165]
  We propose EviDep, an evidential learning framework that jointly quantifies depression severity.<n>EviDep enforces strict information integrity to guarantee robust evidence synthesis.<n>It achieves state-of-the-art predictive accuracy and superior uncertainty calibration, delivering a robust fail-safe mechanism for trustworthy clinical screening.
  arXiv  Detail & Related papers  (2026-04-17T13:27:11Z)
• Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models [56.29123284262618]
  This work proposes leveraging the universal representation capabilities of visual foundation models to estimate inherent data uncertainty.<n>We analyze the feature diversity of the model's decoded representations and quantify their singular value energy to define the semantic perception scale for each class.<n>Based on this foundation, we design two uncertainty-driven application strategies: (1) the aleatoric uncertainty-aware data filtering mechanism to eliminate potentially noisy samples and enhance model learning quality; and (2) the dynamic uncertainty-aware optimization strategy that adaptively adjusts class-specific loss weights during training based on the semantic perception scale, combined with a label denoising mechanism to improve training stability
  arXiv  Detail & Related papers  (2026-04-13T03:59:54Z)
• Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model [95.41752463487008]
  Parkinson's disease affects over ten million people worldwide.<n>Inter-individual variability limits empirical treatment selection, increasing non-negligible surgical risk and cost.<n>We bridge this gap with a pretraining-finetuning framework to predict outcomes directly from resting-state fMRI.
  arXiv  Detail & Related papers  (2026-03-31T02:36:10Z)
• Diagnostics for Individual-Level Prediction Instability in Machine Learning for Healthcare [0.0]
  We propose an evaluation framework that quantifies individual-level prediction instability by using two complementary diagnostics.<n>We apply these diagnostics to simulated data and GUSTO-I clinical dataset.
  arXiv  Detail & Related papers  (2026-02-27T03:42:28Z)
• ClinNet: Evidential Ordinal Regression with Bilateral Asymmetry and Prototype Memory for Knee Osteoarthritis Grading [3.337151338735509]
  Knee osteoarthritis (KOA) grading based on radiographic images is a critical yet challenging task.<n>In this work, we propose ClinNet, a novel trustworthy framework that addresses KOA grading as evidential ordinal regression problem.
  arXiv  Detail & Related papers  (2026-01-24T05:49:41Z)
• Conformal uncertainty quantification to evaluate predictive fairness of foundation AI model for skin lesion classes across patient demographics [8.692647930497936]
  We use conformal analysis to quantify the predictive uncertainty of a vision transformer based foundation model.<n>We show how this can be used as a fairness metric to evaluate the robustness of the feature embeddings of the foundation model.
  arXiv  Detail & Related papers  (2025-03-31T08:06:00Z)
• SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
  Sepsis is the leading cause of in-hospital mortality in the USA. Existing predictive models are usually trained on high-quality data with few missing information. For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
  arXiv  Detail & Related papers  (2024-07-24T04:47:36Z)
• A personalized Uncertainty Quantification framework for patient survival models: estimating individual uncertainty of patients with metastatic brain tumors in the absence of ground truth [4.665141823455397]
  We developed and evaluated our approach based on a dataset of 1383 patients treated with stereotactic radiosurgery for brain metastases. Our results show that all models had the lowest uncertainty on ICP (2.21%) and the highest uncertainty (17.28%) on ICPD.
  arXiv  Detail & Related papers  (2023-11-28T19:07:30Z)
• Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty [57.023423137202485]
  Concerns regarding the reliability of medical image segmentation persist among clinicians.<n>We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks.<n>By leveraging subjective logic theory, we explicitly model probability and uncertainty for medical image segmentation.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.