A unified multi-task framework enables interpretable chest radiograph analysis

Interpretability through structured workflow: The framework mimics how radiologists actually work by breaking diagnosis into three sequential stages (disease recognition → attribute characterization → report generation), making AI decision-making more transparent and auditable rather than operating as a "black box."

Strong clinical validation: In a blinded evaluation, radiologists rated 66% of AI-generated reports as comparable to or better than original clinical reports from four medical centers, suggesting the system produces clinically useful outputs, not just technically accurate predictions.

Traceable decision pathways: By connecting anatomical findings directly to diagnostic conclusions, the system creates documented reasoning trails that clinicians can review and verify, addressing a key trust concern in high-stakes medical AI applications.

Multi-task integration reduces fragmentation: Rather than separate isolated AI systems for different diagnostic tasks, this unified framework handles disease classification, lesion localization, segmentation, and report generation together, better reflecting how clinical diagnosis actually works as an interconnected process.

Balances performance with explainability: The system achieves competitive results across ten chest X-ray benchmarks while maintaining interpretability, demonstrating that trustworthy AI doesn't require sacrificing technical performance—a critical finding for responsible deployment in clinical settings.