Bayesian approaches are frequently used in clinical diagnoses
Bayesian methods and probabilistic networks are widely applied in clinical medicine, medical imaging, and automated diagnostic support tools to calculate individualized disease probabilities and manage diagnostic uncertainty.
The retrieved literature consistently demonstrates that Bayesian approaches, networks, and probabilistic updating methods are frequently employed across diverse clinical diagnostic and prognostic settings, supporting the claim.
Glick M, Khuong QL, Wong JJ, Carrasco-Labra A. A practitioner's guide to developing critical appraisal skills: How to understand and interpret frequentist and Bayesian approaches applied to serial and parallel diagnostic testing.. 2026. https://doi.org/10.1016/j.adaj.2025.12.019
Paper 0 explicitly highlights that Bayesian approaches are utilized in clinical practice to incorporate prior beliefs and update posterior disease probabilities for individualized patient diagnosis.
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Wang MH, Qin S. Explainable neuro-symbolic artificial intelligence for automated interpretation of corneal topography and early keratoconus detection.. 2026. https://doi.org/10.3389/frai.2026.1713747
Paper 1 demonstrates the practical application of Bayesian probabilistic inference to estimate disease likelihoods in automated ophthalmic diagnostics.
O'Flynn C, Wright H, O'Rourke A, Harding A, Williams T, Wallis C, Harvey C, Constantaras M, Fenton N. Risk assessment for canine periodontal disease using a hybrid causal Bayesian network.. 2026. https://doi.org/10.3389/fvets.2026.1781228
Paper 2 utilizes a hybrid Bayesian network to assess disease probabilities and support clinical decision-making in veterinary medicine.
Reijnen C, Pijnenborg JMA, Hoskin P, Mcwilliam A, Lucas PJF, Hommersom A, Kwisthout J, Choudhury A. Bayesian networks as prognostic models in oncology: a systematic review and recommendations for clinical practice.. 2026. https://doi.org/10.1136/bmjonc-2025-001040
Paper 3 reviews the use of Bayesian networks as prognostic and predictive tools in clinical oncology.
Pal R, Kumar S, Bhatnagar G. Uncertainty-aware multi-class brain tumor segmentation using Bayesian U-Net variants.. 2026. https://doi.org/10.1088/2057-1976/ae5ca9
Paper 4 integrates Bayesian inference frameworks to provide uncertainty-aware, interpretable diagnostic segmentations for brain tumor imaging.
Dahan F, Shah JH, Alfakih TM, Farooq H, Aloqaily M, Alshammari M. Uncertainty-Aware adaptive neuro-fuzzy transformer framework for robust multi-center lung disease classification.. 2026. https://doi.org/10.1038/s41598-026-49096-9
Paper 5 incorporates Bayesian predictive uncertainty modeling within an advanced diagnostic classification framework for lung diseases.
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