Academic advances of AI models in high-precision domains, like healthcare,
need to be made explainable in order to enhance real-world adoption. Our past
studies and ongoing interactions indicate that medical experts can use AI
systems with greater trust if there are ways to connect the model inferences
about patients to explanations that are tied back to the context of use.
Specifically, risk prediction is a complex problem of diagnostic and
interventional importance to clinicians wherein they consult different sources
to make decisions. To enable the adoption of the ever improving AI risk
prediction models in practice, we have begun to explore techniques to
contextualize such models along three dimensions of interest: the patients'
clinical state, AI predictions about their risk of complications, and
algorithmic explanations supporting the predictions. We validate the importance
of these dimensions by implementing a proof-of-concept (POC) in type-2 diabetes
(T2DM) use case where we assess the risk of chronic kidney disease (CKD) - a
common T2DM comorbidity. Within the POC, we include risk prediction models for
CKD, post-hoc explainers of the predictions, and other natural-language modules
which operationalize domain knowledge and CPGs to provide context. With primary
care physicians (PCP) as our end-users, we present our initial results and
clinician feedback in this paper. Our POC approach covers multiple knowledge
sources and clinical scenarios, blends knowledge to explain data and
predictions to PCPs, and received an enthusiastic response from our medical
expert.

为了推广人工智能模型在高精度领域（如医疗保健）的实际应用，必须使其具有可解释性。本研究通过探索一种基于临床情况、疾病风险预测和算法解释的上下文定制方法，验证了这种方法的重要性。在对 2 型糖尿病（T2DM）中的慢性肾脏病（CKD）风险预测进行了实证分析后，我们获得了初步结果和来自医疗专家的热烈反响。