Towards Clinical Practice in CT-Based Pulmonary Disease Screening: An Efficient and Reliable Framework

Computational efficiency is critical for clinical adoption: The paper identifies high computational costs from processing entire 3D CT volumes as a major barrier preventing AI screening tools from being used in real clinical settings, suggesting that making these systems faster is just as important as making them accurate.

Smart slice selection preserves diagnostic quality: The proposed Cluster-based Sub-Sampling method reduces the number of CT images a model must analyze while maintaining diagnostic accuracy by intelligently selecting representative slices, rather than randomly discarding images which can lose important information about diseases.

The system knows when it's uncertain: The Ambiguity-aware Uncertainty Quantification mechanism helps the AI flag cases where it lacks confidence—particularly those with subtle lesions or visual artifacts—allowing radiologists to focus their attention on genuinely difficult cases rather than trusting potentially unreliable automated decisions.

Balancing speed and reliability is essential for responsible deployment: By addressing both computational efficiency and diagnostic reliability simultaneously, the framework demonstrates that responsible AI in healthcare requires solving practical implementation challenges alongside technical accuracy concerns.