@misc{74,
  keywords = {Cervical Cancer, Enterprise Medical Imaging},
  author = {Kangwa Mukuka and Festus Mwape},
  title = {Enhancing Radiology Workflows: Semi-automated Cervical Cancer Reporting at the Cancer Diseases Hospital in Zambia},
  abstract = {Cervical cancer remains the most prevalent form of cancer among women in Zambia, contributing significantly to delayed treatment and high mortality rates. At the Cancer Diseases Hospital (CDH-UTH), radiologists face substantial workflow challenges, including prolonged turnaround times for report generation. This project proposes a semi-automated software solution to streamline radiology workflows and reduce reporting delays. The system integrates a structured checklist interface, FIGO staging support, and an editable AI-assisted report generator, all deployed via a web-based platform built with ReactJS and Express.js. A pilot evaluation involving radiologists at CDH demonstrated strong usability and workflow alignment, with positive feedback on interface clarity and reduced manual effort. The solution also incorporates metadata extraction, standardized reporting formats, and plans for integrating a fine-tuned machine learning model. By enhancing reporting efficiency and supporting clinical decision-making, the system aims to improve patient outcomes and contribute to scalable cancer care innovation in low-resource settings.},
  year = {2025},
  pages = {66},
  month = {2025},
  publisher = {University of Zambia},
  address = {Lusaka, Zambia},
}