Electronic Theses and Dissertation

Penggunaan artificial intelligence (AI) dalam radiologi merupakan inovasi
penting yang berpotensi meningkatkan efisiensi dan akurasi diagnosis medis.
Namun, implementasi AI di Indonesia, khususnya Banda Aceh, masih
menghadapi tantangan seperti keterbatasan infrastruktur, literasi digital, dan
kesiapan sumber daya manusia. Penelitian ini bertujuan menggambarkan tingkat
pengetahuan dan sikap peserta didik Fakultas Kedokteran Universitas Syiah Kuala
terhadap penggunaan AI dalam radiologi. Penelitian kuantitatif deskriptif dengan
desain cross-sectional ini melibatkan 112 responden, terdiri dari 89 dokter muda
dan 23 residen radiologi. Pengambilan sampel menggunakan purposive sampling
untuk dokter muda dan residen. Data dikumpulkan menggunakan kuesioner
pengetahuan dan sikap masing-masing 16 butir yang telah diuji validitas dan
reliabilitasnya. Analisis univariat digunakan untuk mendeskripsikan distribusi
pengetahuan dan sikap responden. Hasil penelitian menunjukkan mayoritas
responden berusia 21–30 tahun (80,36%) dan sebagian besar belum pernah
mengikuti seminar AI (81,3%). Tingkat pengetahuan residen (86,95%) dan dokter
muda (79,77%) berada pada kategori tinggi. Sikap residen (87%) dan dokter muda
(55,1%) berada pada kategori netral. Penelitian ini menunjukkan perlunya
peningkatan edukasi, pelatihan teknis, dan kurikulum yang mengutilisasi AI untuk
mempersiapkan peserta didik menghadapi transformasi digital di bidang radiologi.

The use of artificial intelligence (AI) in radiology is an important innovation with the potential to improve the efficiency and accuracy of medical diagnosis. However, the implementation of AI in Indonesia, particularly in Banda Aceh, still faces challenges such as limited infrastructure, digital literacy, and human resource readiness. This study aims to describe the level of knowledge and attitudes of students at the Faculty of Medicine, Syiah Kuala University, regarding the use of AI in radiology. This descriptive quantitative study with a cross-sectional design involved 112 respondents, consisting of 89 medical interns and 23 radiology residents. Sampling was conducted using purposive sampling for medical interns and residents. Data were collected using 16 item for each questionnaires, knowledge and attitude that had been tested for validity and reliability. Univariate analysis was used to describe the distribution of respondents’ knowledge and attitudes. The results showed that most respondents were aged 21–30 years (80.36%) and the majority had never attended an AI seminar (81.3%). The knowledge levels of residents (86.95%) and medical interns (79.77%) were categorized as high. The attitudes of residents (87%) and medical interns (55.1%) were categorized as neutral. This study highlights the importance of using AI to enhance education, technical training, and curriculum which will help student prepare upcoming transformations in radiology.