Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI

High accuracy doesn't automatically mean clinical readiness: While the Xception model achieved 96.21% accuracy, the paper doesn't discuss real-world performance metrics like false positive rates for serious conditions (e.g., misdiagnosing tuberculosis as normal lungs), which are critical for responsible deployment in healthcare settings.

Explainability is essential for medical AI trust: The paper emphasizes that XAI methodologies help clinicians understand why the model makes specific diagnoses. This transparency is crucial for responsible AI in healthcare, as doctors need to verify the model's reasoning before trusting it with patient care decisions.

Model selection matters for different diseases: The research tests multiple architectures (CNNs, transformers, ensembles, Big Transfer), suggesting that different lung diseases may require different approaches. This finding highlights the importance of careful model selection rather than assuming one solution fits all medical conditions.

Rigorous validation methods strengthen reliability: The use of stratified k-fold cross-validation and transfer learning with fine-tuning demonstrates methodological rigor that helps ensure the model's performance is genuine rather than overfitted—an important safeguard for responsible AI deployment in clinical practice.