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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 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’ workload when interpreting medical images. Two (2) AI models-a classification model and detection model indicating potential areas of interest-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-a Radiologist and Radiology Resident-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 indicate 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 experimentally demonstrates the potential of utilising AI for the semi-automated interpretation of medical images in low-resource settings.

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Evaluating Perceived Workload, Usability and Usefulness of Artificial Intelligence Systems in Low-Resource Settings: Semi-automated Classification and Detection of Community Acquired Pneumonia

  • 会议论文

  • 发表日期：

    2025年

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  • Malaizyo G. Muzumala,
  • Ernest O. Zulu,
  • Peter Chibuta,
  • Mayumbo Nyirenda,
  • Lighton Phiri

摘要

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 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’ workload when interpreting medical images. Two (2) AI models-a classification model and detection model indicating potential areas of interest-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-a Radiologist and Radiology Resident-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 indicate 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 experimentally demonstrates the potential of utilising AI for the semi-automated interpretation of medical images in low-resource settings.