The healthcare industry has been revolutionized by the convergence of Artificial Intelligence of Medical Things (AIoMT), allowing advanced data-driven solutions to improve healthcare systems. With the increasing complexity of Artificial Intelligence (AI) models, the need for Explainable Artificial Intelligence (XAI) techniques become paramount, particularly in the medical domain, where transparent and interpretable decision-making becomes crucial. Therefore, in this work, we leverage a custom XAI framework, incorporating techniques such as Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-Cam), explicitly designed for the domain of AIoMT. The proposed framework enhances the effectiveness of strategic healthcare methods and aims to instill trust and promote understanding in AI-driven medical applications. Moreover, we utilize a majority voting technique that aggregates predictions from multiple convolutional neural networks (CNNs) and leverages their collective intelligence to make robust and accurate decisions in the healthcare system. Building upon this decision-making process, we apply the XAI framework to brain tumor detection as a use case demonstrating accurate and transparent diagnosis. Evaluation results underscore the exceptional performance of the XAI framework, achieving high precision, recall, and F1 scores with a training accuracy of 99% and a validation accuracy of 98%. Combining advanced XAI techniques with ensemble-based deep-learning (DL) methodologies allows for precise and reliable brain tumor diagnoses as an application of AIoMT.

通过结合医学人工智能和可解释人工智能技术，提出了一种自定义的可解释人工智能框架，应用于人工智能医疗物联网领域，以提高医疗系统的效果，并具备透明和可理解的决策能力。将该框架应用于脑肿瘤检测用例中，得出了准确透明的诊断结果，并且评估结果表明了其出色的性能，实现了高精确度、召回率和F1分数，训练准确率达到99%，验证准确率达到98%。这种结合高级可解释人工智能技术和基于集成的深度学习方法的框架能够实现精确可靠的脑肿瘤诊断作为医学物联网应用的一种。

医疗物联网的可解释人工智能框架