Transparent Medical Diagnosis Explainable AI XAI

The use of the machine-learning and deep-learning models has enabled the rapid evolution of the healthcare industry through the development of automated and correct medical diagnosis using the concept of Artificial Intelligence (AI). The use of AI models in interpreting medical imaging, predicting illnesses, and assisting a physician in making a clinical decision is becoming more widespread.
However, a vast number of high-performing AI models are black-box systems, the internal process of which is not easily understandable to human observers. This obscurity creates a lot of difficulties in such critical areas like healthcare where trust, accountability, and interpretability are essential to clinical uptake.
Explainable Artificial Intelligence (XAI) has become a key solution to this issue as it makes the AI models more transparent and understandable. XAI methods enable the medical proficiency to understand the reason behind AI predictability by accentuating the significant features, particular areas of medical images, or clinical signs that influence the model judgments.
We discuss a clear medical diagnosis model in the context of deep-learning models and explainability algorithms like SHAP, LIME, and Grad-CAM in this paper. The suggested solution aims to provide accurate diagnostic forecasts and accountable explanations that will help healthcare providers to verify AI decisions and validate them.
Explainable AI can further improve the trust in automated medical systems, improve collaboration between artificial-intelligence systems and clinicians, and promote safer and more reliable decision-making in healthcare by improving interpretability.

Medical, Diagnosis, deep-learning

1. Moe, M. T. Ribeiro, S. Singh and C. Guestrin, Why should I trust you? A description of the predictions of any classifier, Proceedings of the 22 nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1135 1144. [Google Scholar] [Crossref]

2. S. M. Lundberg and S. I. Lee, A unified approach to interpreting model predictions, in Advances in Neural Information Processing Systems (NeurIPS), 2017, pp. 4765 4774. [Google Scholar] [Crossref]

3. R. R. Selvaraju et al., Grad-CAM: Visual explanations by deep nets through gradient-based localization, in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 618626. [Google Scholar] [Crossref]

4. W. Samek, T. Wiegand, and K. R. Müller, explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models, in IEEE Signal Processing Magazine, vol. 34, no. 6, pp. 8089, 2017. [Google Scholar] [Crossref]

5. G. Litjens et al., “Deep learning survey: medical image analysis, Medical Image Analysis, vol. 42, pp. 6088, 2017. [Google Scholar] [Crossref]

6. A. Esteva et al., “Dermatologist-level skin cancer deep neural network classification, Nature, vol. 542, no. 7639, p. 115–118, 2017. [Google Scholar] [Crossref]

7. D. S. Kermany, M. Goldbaum, W. Cai et al., “Identifying medical diagnoses and treatable diseases by image based deep learning, Cell, vol. 172, no. 5, pp. 11221131, 2018. [Google Scholar] [Crossref]

8. T. Rajkomar, E. Oren, K. Chen et al., Scalable and accurate deep learning with electronic health records, npj Digital Medicine, vol. 1 no. 18, 2018. [Google Scholar] [Crossref]

9. Z. C. Lipton, The mythos of model interpretability Communications of the ACM, vol. 61, no. 10, pp. 3643, 2018. [Google Scholar] [Crossref]

10. J. Holzinger, G. Langs, H. Denk, K. Zatloukal, and H. Müller, Causability and explainability of artificial intelligence in medicine Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 9, no. 4, 2019. [Google Scholar] [Crossref]