School of Medicine (Sch. of Med.) Collection

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https://dissertations.mak.ac.ug/handle/20.500.12281/6618

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Browsing School of Medicine (Sch. of Med.) Collection by Subject "Artificial Intelligence"
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    Knowledge, attitudes and perception of medical radiography students in Makerere University towards the implementation of artificial intelligence (ai) in medical radiology imaging.
    (Makerere University, 2025-05) Natuhwera, Bruno
    Background As the science of artificial intelligence (AI) continues to grow, there is an increasing adoption across medical practices worldwide. The increasing use of AI in medical imaging has created worldwide interest about its effects on radiological practice and education. The workforce implications of AI in radiography remain unclear especially for low-resource settings such as Uganda. The future practitioners who will work with AI need to be understood because they represent the student population of radiography. The situation demands research into how radiography students view AI. Objective To explore the knowledge, attitudes, and perceptions of medical radiography students in Uganda regarding the implementation of AI in medical radiology imaging. Methods A qualitative study was conducted among medical radiography students at Makerere University in two focus group discussions to explore their views on AI in medical radiology imaging. Open-ended questions were used as a guide in both FGDs and the recorded audios were transcribed and analyzed using thematic analysis to identify similar patterns in the students’ responses. Results Most students showed large learning gaps and limited exposure to AI technologies. However, they recognized AI’s potential for improvements in image quality, diagnostic speed, and workflow efficiency. Respondents cited ethical considerations such as data protection, algorithmic bias and trust and feared replacement by AI though some felt that human expertise would remain essential. Even an overreliance on AI could potentially affect patient–provider relationships. Limited infrastructure, high costs, and insufficient local expertise or training resources to support AI systems were the cited barriers to AI implementation. All participants strongly expressed the need to integrate AI topics into the radiography curriculum to prepare them for an AI-augmented workplace and their future career advancements. Conclusion While AI can improve radiological services, there are concerns about ethics, jobs, and patient interaction highlight the human factors that cannot be overlooked. Equipping students with AI-related competencies and ensuring adequate resources will empower future radiographers to confidently and safely work alongside AI.