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While software using artificial intelligence and machine learning (AI/ML) is pervasive in many areas of society today, the use of these technologies to diagnose and treat medical conditions is limited due to a number of challenges associated with the trustworthiness of the results. This may include the inability to fully explain how an algorithm works inherent to the black-box nature of the system. Additionally, AI/ML may create a potential for bias and artifacts that cannot be validated due to the same limitations. In a medical application, the lack of transparency in how the system operates may lead to a loss of trust by users. Bayesian approaches that use computational modeling to quantify the level of uncertainty in a given result may provide a path towards improved confidence and use. In this paper, evidence from studies in a range of medical applications is presented and discussed, showing how Bayesian approaches can help to foster trust. A retrospective study using a publicly available dataset explored the feasibility of creating predictive models for early intervention in a Type 1 diabetes population. Creating the perfect model was not the goal of the exercise, rather the study aimed to demonstrate how Bayesian methods could be used to identify areas of uncertainty during model development. Feature selection was based on analytical assessment of various patterns found in the data. Models were trained, validated, and tested, generating uncertainty estimates. A two-feature Gaussian Naïve Bayes (GNB) model, using the previous five minutes and ten minutes of blood glucose values, showed similar results for predictive accuracy as a threefeature model that included average change over the preceding 30 minutes. The two-feature model was selected because it allowed for a more easily understood visualization of uncertainty. The 2-feature GNB achieved an AUC = .94. The model showed good sensitivity for exceeding the < 180 mg/dl limit, obtaining threshold prediction = 89.8% and normal range prediction = 90.8%. The sensitivity was lower for the < 70 mg/dl limit, attaining a sensitivity = 77.5%. Posterior probabilities showed differing levels of uncertainty in the prediction of high and low out-of-range conditions. The model demonstrated the feasibility of providing robust parameter estimates. Bayesian machine learning approaches to model uncertainty may improve the transparency, explainability, and applicability of AI/ML in medical treatment, realizing the promise to improve patient safety and outcomes.
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