@conference{conference,
  keywords = {Artificial Intelligence, Detection, DICOM, Medical Imaging},
  author = {Malaizyo Muzumala and Ernest Zulu and Peter Chibuta and Mayumbo Nyirenda and Lighton Phiri},
  title = {Evaluating Perceived Workload, Usability and Usefulness of Artificial Intelligence Systems in Low-Resource Settings: Semi-automated Classification and Detection of Community Acquired Pneumonia},
  abstract = {The use of Artificial Intelligence (AI) techniques in radiological workflows is increasingly becoming mainstream. However, the uptake of AI techniques is still low in low-resource settings in places such as the Global South. This paper presents a study conducted in a setting with low AI uptake, to determine the impact of AI on Radiologists{\textquoteright} workload when interpreting medical images. Two (2) AI models{\textemdash}a classification model and detection model indicating potential areas of interest{\textemdash}were implemented to facilitate the semi-automated interpretation of medical images for Pneumonia. In addition, a Web-based DICOM Viewer was implemented to interface the AI models. To determine the appropriate model configuration, two (2) experts{\textemdash}a Radiologist and Radiology Resi- dent{\textemdash}participated in a focus group discussion aimed at determining how the AI models could facilitate interpretation processes. A comparative controlled experiment was subsequently conducted with 12 Radiology Residents at a large University Teaching Hospital, to assess the impact of AI on the workload and its perceived usefulness. NASA Task Load Index (TLX) and Technology Acceptance Model (TAM) 2 questionnaires were employed to measure the workload and usefulness. The results in- dicate that the perceived workload is significantly less when using the AI solution, with an overall NASA-TLX score of 1.86. Furthermore, the perceived usefulness of the AI solution is demonstrated through the positive responses for all the eight TAM 2 constructs. This study experimen- tally demonstrates the potential of utilising AI for the semi-automated interpretation of medical images in low-resource settings.},
  year = {2025},
  journal = {Applications of Medical Artificial Intelligence (AMAI 2024)},
  pages = {116-126},
  month = {02/2025},
  publisher = {Springer Nature Switzerland},
  address = {Marrakech, Morocco},
  isbn = {978-3-031-82007-6},
  doi = {10.1007/978-3-031-82007-6_12},
}