AI-Empowered assistive technology for optimizing specimen submission in obstetrics and gynecology: integrating DeepSeek with the ADDIE model

This study leverages AI-empowered assistive technology to enhance augment clinician training through AI-assisted education clinical workflows by integrating DeepSeek's data-driven intelligence with the ADDIE educational model. We aimed to optimize the obstetrics and gynecology specimen submission process, reduce errors, and improve operational efficiency through predictive analytics, interactive training, and automated feedback. By analyzing 444 specimen return events (2024), DeepSeek identified error patterns and enabled targeted interventions, while the ADDIE framework facilitated structured nurse training and process refinement. Post-intervention, the specimen return rate significantly decreased from 2.28% to 0.87% (p < 0.001), nurses' knowledge scores improved from 82.2 ± 6.1 to 93.5 ± 5.1, and response time shortened by 35%. This synergy demonstrates how AI-assisted big data analytics can transform clinical workflows, offering a scalable model for intelligent healthcare systems.