# Towards a Transparent and Interpretable AI Model for Medical Image Classifications

arXiv: https://arxiv.org/abs/2509.16685 · HTML: https://mlsanity.com/paper/2509.16685/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 38 of its 129 references that are in the map.
- First posted on arXiv 20 Sep 2025.

## Builds on (most cited first, 30 of 38)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [Understanding Neural Networks Through Deep Visualization](https://mlsanity.com/paper/1506.06579/) (2015)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models](https://mlsanity.com/paper/1708.08296/) (2017)
- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [What do we need to build explainable AI systems for the medical domain?](https://mlsanity.com/paper/1712.09923/) (2017)
- [Explainable deep learning models in medical image analysis](https://mlsanity.com/paper/2005.13799/) (2020)
- [On Interpretability of Artificial Neural Networks: A Survey](https://mlsanity.com/paper/2001.02522/) (2020)
- [Explainable artificial intelligence model to predict acute critical illness from electronic health records](https://mlsanity.com/paper/1912.01266/) (2019)
- [Explainable Artificial Intelligence Approaches: A Survey](https://mlsanity.com/paper/2101.09429/) (2021)
- [Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysis](https://mlsanity.com/paper/2202.10553/) (2022)
- [Evaluating Explanation Without Ground Truth in Interpretable Machine Learning](https://mlsanity.com/paper/1907.06831/) (2019)
- [An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery](https://mlsanity.com/paper/2008.00238/) (2020)
- [Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research](https://mlsanity.com/paper/2211.15343/) (2022)
- [Explainable Security](https://mlsanity.com/paper/1807.04178/) (2018)
- [Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making](https://mlsanity.com/paper/2204.05030/) (2022)
- [Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization](https://mlsanity.com/paper/2010.05759/) (2020)
- [Outlining the Design Space of Explainable Intelligent Systems for Medical Diagnosis](https://mlsanity.com/paper/1902.06019/) (2019)

## Abstract (arXiv)

The integration of artificial intelligence (AI) into medicine is remarkable, offering advanced diagnostic and therapeutic possibilities. However, the inherent opacity of complex AI models presents significant challenges to their clinical practicality. This paper focuses primarily on investigating the application of explainable artificial intelligence (XAI) methods, with the aim of making AI decisions transparent and interpretable. Our research focuses on implementing simulations using various medical datasets to elucidate the internal workings of the XAI model. These dataset-driven simulations demonstrate how XAI effectively interprets AI predictions, thus improving the decision-making process for healthcare professionals. In addition to a survey of the main XAI methods and simulations, ongoing challenges in the XAI field are discussed. The study highlights the need for the continuous development and exploration of XAI, particularly from the perspective of diverse medical datasets, to promote its adoption and effectiveness in the healthcare domain.