# Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis Systems

arXiv: https://arxiv.org/abs/2011.13169 · HTML: https://mlsanity.com/paper/2011.13169/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities](https://mlsanity.com/paper/2111.06420/) (2021)
- [A Review on Explainability in Multimodal Deep Neural Nets](https://mlsanity.com/paper/2105.07878/) (2021)
- [ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions](https://mlsanity.com/paper/2201.01249/) (2022)
- [IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in Histopathology](https://mlsanity.com/paper/2408.16395/) (2024)
- [DeepMediX: A Deep Learning-Driven Resource-Efficient Medical Diagnosis Across the Spectrum](https://mlsanity.com/paper/2307.00324/) (2023)
- [VL-MedGuide: A Visual-Linguistic Large Model for Intelligent and Explainable Skin Disease Auxiliary Diagnosis](https://mlsanity.com/paper/2508.06624/) (2025)
- [Towards a Transparent and Interpretable AI Model for Medical Image Classifications](https://mlsanity.com/paper/2509.16685/) (2025)

## Abstract (arXiv)

Remarkable success of modern image-based AI methods and the resulting interest in their applications in critical decision-making processes has led to a surge in efforts to make such intelligent systems transparent and explainable. The need for explainable AI does not stem only from ethical and moral grounds but also from stricter legislation around the world mandating clear and justifiable explanations of any decision taken or assisted by AI. Especially in the medical context where Computer-Aided Diagnosis can have a direct influence on the treatment and well-being of patients, transparency is of utmost importance for safe transition from lab research to real world clinical practice. This paper provides a comprehensive overview of current state-of-the-art in explaining and interpreting Deep Learning based algorithms in applications of medical research and diagnosis of diseases. We discuss early achievements in development of explainable AI for validation of known disease criteria, exploration of new potential biomarkers, as well as methods for the subsequent correction of AI models. Various explanation methods like visual, textual, post-hoc, ante-hoc, local and global have been thoroughly and critically analyzed. Subsequently, we also highlight some of the remaining challenges that stand in the way of practical applications of AI as a clinical decision support tool and provide recommendations for the direction of future research.