Transparent Medical Diagnosis Explainable AI XAI

Abstract

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.