EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) FOR MEDICAL DIAGNOSIS: A Comprehensive Review of Interpretability Frameworks, Clinical Integration, and Trust Metrics | IJCT Volume 12 – Issue 6 | IJCT-V12I6P6

Computers that use deep learning are becoming very helpful in medicine. They can study scans, lab results, or heart readings and help doctors find diseases faster and more accurately. But many of these systems work like a mystery box — they give answers without showing how they made them. This lack of clarity makes it hard for doctors and patients to fully trust their results and also creates problems for safety and legal approval . This paper reviews many ways scientists are trying to make these smart systems easier to understand, known as Explainable Artificial Intelligence (XAI). We compare different types of XAI methods, such as models that explain their own decisions and others that explain their decisions afterward (like LIME, SHAP, and Grad-CAM). We also look at how these explanations can fit smoothly into hospital workflows so that doctors can use them easily. Our study shows that for AI to be truly useful in healthcare, it must not only give accurate answers but also explain its thinking in a way people can trust. The framework we propose helps developers create medical AI tools that are both powerful and transparent—turning them from confusing “black boxes” into reliable partners for doctors.

While the results are promising, several limitations remain: 1.Dataset scope: Only two imaging datasets were analyzed; future studies should incorporate larger and more diverse cohorts. 2.Model dependency: Grad-CAM works mainly with CNN models, which means it doesn’t easily adapt to newer model types like transformers or models that handle multiple kinds of data together. 3.Human evaluation scale: The clinician survey sample size was limited; larger, multi-institutional studies are necessary to validate subjective interpretability scores. 4.Computational cost: SHAP and LIME take a lot of computing power and time to run, which makes them hard to use in real-time situations like hospitals where quick results are needed. Recognizing these constraints provides a clear roadmap for refining and extending the current work.

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