Deep Learning Approach for Enhancing Oral Squamous Cell Carcinoma with LIME Explainable AI Technique

Deep learning can achieve high accuracy in cancer detection: The study found that EfficientNetB3 achieved 98.33% accuracy in distinguishing oral cancer from normal tissue samples, suggesting AI models may assist pathologists in diagnostic tasks.

Explainability matters for clinical trust: The researchers used LIME (Local Interpretable Model-agnostic Explanations) to show why the AI made specific predictions, addressing a key concern in responsible AI—that doctors and patients need to understand how diagnostic decisions are reached, not just accept a "black box" result.

Efficiency and resource use are important considerations: EfficientNetB3 outperformed other models (ResNet101, DenseNet121, VGG16) while using "remarkably less computing power," demonstrating that responsible AI development should balance accuracy with practical resource constraints and environmental impact.

Dataset composition affects real-world applicability: The study used 5,192 histopathology images with roughly equal representation of normal and cancer cases, but responsible deployment requires considering whether this balanced dataset reflects actual clinical populations where cancer is less common.

Explainability is essential but not sufficient: While LIME helps interpret individual predictions, the paper demonstrates that explainability techniques should be standard practice in medical AI to support clinical decision-making and maintain human oversight in healthcare settings.