Under-Graduate Students

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Browsing Under-Graduate Students by Author "Abass Himida Hamouri Mohamed"

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    Knowledge, attitude and practice of medical students in Sudan about Artificial Intelligence in Radiological Image Analysis in 2024–2025
    (Napata College, 2025) Omar Hamed Ali; Faiha Taha Musa Musa Almagbol; Abass Himida Hamouri Mohamed; Abass Himida Hamouri Mohamed; Manahil Ahmed Musa Khoglay
    Background: Now days, Artificial Intelligence is one of the increasingly integrated areas in the field of image analysis, pathology and ophthalmology, offering enhanced diagnostic accuracy and efficiency. Despite the advantage of AI, the readiness of future healthcare professionals to use and accept those changes, and the fear of human labor been replaced with AI, remains unclear especially in area of low resource like Sudan. Objective: This study aims to assess the knowledge, attitudes and practices (KAP) of medical students in Sudan regarding the use of AI in radiological image analysis. Methods: A cross-sectional study was conducted among the medical students at Sudanese universities, Data was collected using a structured questionnaire forum shared with the willing students to participate, to assess their KAP exposure to AI in radiology. Results: the surveyed 311 participants, with 206 (66.45%) are female and 105 (33.55%) are male. Ranged in the age of above 21 to the age of 44, in the 4th, 5th and graduate years, showed a significant variety and association between Gender and Familiarity with AI and as well as between Education and Familiarity, AI was perceived as non beneficial in about 70.21% of the participant answering “No” and only 29.79% answered “YES”, 44.4% were concerned about cost implementation while 25% about privacy, as only 26.9% reported that it help in early detection and 25% in reducing overload from radiologist. Conclusion: Even though Participant recognize the AI as a valuable tool in Radiology but still the uncertainty in the area of cost of implementing (44.4%) and data privacy (25%) was a big concern letting about 70% to say NO to the beneficial aspect of AI, with The Likert scale questions showed a neutral to slightly positive response about the utility safety and integration of AI in medical image analysis, however internal consistency was very low about Cronbach’s alpha=0.26, indicating a divergent view across individual items. Key Words: Artificial Intelligence, Radiology, Medical Students, Knowledge, Attitude, Practice, Sudan.