ASSESSMENT OF KNOWLEDGE OF RADIOLOGISTS ABOUT ARTIFICIAL INTELLIGENCE IN DIAGNOSTIC RADIOLOGY

Ghazala Wahid; Mahnoor Rehman Khan; Faryal Bashir; Rabeea Ihtesham; Shaista Khan; Fatima Nasir

doi:10.46903/gjms/23.4.2010

ASSESSMENT OF KNOWLEDGE OF RADIOLOGISTS ABOUT ARTIFICIAL INTELLIGENCE IN DIAGNOSTIC RADIOLOGY

Ghazala Wahid, Mahnoor Rehman Khan, Faryal Bashir, Rabeea Ihtesham, Shaista Khan, Fatima Nasir

Abstract

Background: Artificial intelligence (AI) has recently been widely used in clinical settings to assist in medical diagnosis and improve patient outcomes. AI has been utilized in diverse imaging techniques, such as CT, MRI, and mammography, to enhance image processing and improve diagnostic accuracy. Our study aimed to explore the knowledge of radiologist of Peshawar about artificial intelligence and assess their attitude towards AI in diagnostic radiology.

Materials & Methods: A cross-sectional study was conducted in the main hospitals of Peshawar from 8th August 2024 to 8th November 2024. An online questionnaire was designed using Google Form software to assess the knowledge of AI in diagnostic radiology of three groups: (i) first- to fourth-year trainees in radiology programs; (ii) Radiologists and Radiology fellows; and (iii) Radiology consultants. About 200 radiologists are currently working in different hospitals in Peshawar. Estimating the anticipated frequency of participation to be 50% and an absolute precision of 5% sample size of 130 was calculated. 130 responses were completed. Statistical analysis was done using SPSS software.

Results: A total of 130 Radiologists completed the questionnaire. About 32% were consultant radiologists, 11% were radiology fellows, and 58% were radiology residents. Twenty (20) % of the radiologists were currently using AI software, of which 13 were from government hospitals and 5 from private hospitals. 56.9% had a positive attitude towards the integration of AI in radiology. A significant association was found between current position (resident, radiology fellow, or consultant) and attitude toward AI integration (χ² = 14.884, p = 0.005). Significant associations were found between perceived benefits and both current position (χ² = 14.392, p = 0.026) and place of work (χ² = 29.667, p = 0.003). Both current position (χ² = 21.177, p = 0.007) and place of work (χ² = 39.835, p = 0.001) showed significant associations with perceived challenges.

Conclusion: Radiologists in Peshawar are positive about integrating AI into diagnostic imaging; however, there is a lack of use of AI in imaging. Several barriers need to be addressed for its successful integration.

Keywords

Artificial intelligence; Diagnostic radiology; Medical imaging; Machine learning.

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DOI: https://doi.org/10.46903/gjms/23.4.2010