A Deep Learning Framework for Automated Triage of Breast Cancer Biopsies in Malaysia: A Simulation Study to Reduce Resource Consumption and Diagnostic Turnaround Time

These in-silico findings project potential efficiency gains, though real-world validation is required. By expediting critical case reporting, reducing workload, and conserving resources, this approach offers a promising simulation-informed framework to address diagnostic bottlenecks in resource-constrained healthcare systems.