A 2025 structured narrative review published in Springer Nature's International Journal of Computer Assisted Radiology and Surgery evaluates AI integration across full diagnostic imaging workflows — from patient scheduling and image acquisition to reporting and billing. Key findings: 70% of MRI and 64% of CT workflow steps now have AI solutions available; AI achieves 94% segmentation accuracy and 95% nodule detection sensitivity; scan times are reduced by 30–75% and report turnaround by 30–50%. These gains mirror the transformational role that a modern Diagnostic Management System plays in automating patient registration, multi-investigation scheduling, radiologist workflows, and automated report delivery in busy diagnostic centres.

Bangladesh's private diagnostic sector — imaging centres, pathology labs, and multi-modality health check-up chains — has expanded rapidly across Dhaka, Chattogram, and district towns over the past decade, driven by rising demand for faster diagnosis and DGHS licensing requirements for private diagnostic facilities. As competition grows, centres increasingly differentiate on turnaround time and report accuracy rather than price alone. A purpose-built DMS — covering registration, multi-investigation scheduling, radiologist/pathologist workflow, and SMS/print report delivery — is becoming the operational backbone that lets diagnostic centres scale from a single branch to a multi-branch chain without a proportional increase in administrative overhead.