Long diagnostic turnaround times (TAT), equipment obsolescence, infrastructural inadequacies, and a lack of personnel are some of the ongoing issues facing resource-constrained healthcare systems. By integrating into current digital workflows, Software as a Medical Device (SaMD), especially AI-enabled systems, provides a scalable solution without necessitating changes to the underlying medical hardware. This study investigates whether SaMD may significantly increase turnaround time and diagnostic decision consistency in resource-constrained public healthcare settings. The results indicate that SaMD can greatly improve workflow efficiency and lower inter-operator variability. However, the main implementation obstacles continue to be algorithmic drift, regulatory fragmentation, cybersecurity concerns, and reimbursement constraints. The study concludes that SaMD is a workable, scalable solution for bolstering diagnostic systems in resource-poor areas when implemented through organised regulatory, financial, and cloud-based approaches.