# An Explainable AI Framework for Artificial Intelligence of Medical Things

arXiv: https://arxiv.org/abs/2403.04130 · HTML: https://mlsanity.com/paper/2403.04130/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Builds on (most cited first, 1 of 1)

- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)

## Built on by (most cited first, 8 of 8)

- [Empowering Healthcare through Privacy-Preserving MRI Analysis](https://mlsanity.com/paper/2403.09836/) (2024)
- [Advancing Healthcare: Innovative ML Approaches for Improved Medical Imaging in Data-Constrained Environments](https://mlsanity.com/paper/2410.12245/) (2024)
- [Science based AI model certification for untrained operational environments with application in traffic state estimation](https://mlsanity.com/paper/2403.14093/) (2024)
- [Enhancing UAV Security Through Zero Trust Architecture: An Advanced Deep Learning and Explainable AI Analysis](https://mlsanity.com/paper/2403.17093/) (2024)
- [Science based AI model certification for new operational environments with application in traffic state estimation](https://mlsanity.com/paper/2405.07893/) (2024)
- [Privacy-Preserving Federated Vision Transformer Learning Leveraging Lightweight Homomorphic Encryption in Medical AI](https://mlsanity.com/paper/2511.20983/) (2025)
- [SD-CGAN: Conditional Sinkhorn Divergence GAN for DDoS Anomaly Detection in IoT Networks](https://mlsanity.com/paper/2512.00251/) (2025)
- [Physics-Embedded Feature Learning for AI in Medical Imaging](https://mlsanity.com/paper/2603.28057/) (2026)

## Abstract (arXiv)

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.