Knowledge and Perception of Artificial Intelligence in Improving Diagnostic Accuracy Among Healthcare Professionals in Atbara and Port Sudan teaching hospital 2026
Knowledge and Perception of Artificial Intelligence in Improving Diagnostic Accuracy Among Healthcare Professionals in Atbara and Port Sudan teaching hospital 2026
No Thumbnail Available
Date
2026
Authors
Ola Husham Elnaeem Ahmed
Mohamed Elsarrag Mohamed
Muawia MohamedAhmed Ismail Mohamed
Mohamed Yousif Mohamed Abubar
Journal Title
Journal ISSN
Volume Title
Publisher
Napata College
Abstract
Title: Knowledge and Perception of Artificial Intelligence in Improving Diagnostic
Accuracy Among Healthcare Professionals in Sudan - 2026
Background: In the context of Sudan's current healthcare crisis and the significant
"brain drain" of medical specialists, Artificial Intelligence (AI) has emerged as a
potential tool to bridge the diagnostic expertise gap. Understanding the readiness of the
remaining workforce to adopt these technologies is critical for future healthcare
reconstruction.
Objectives: This study aimed to evaluate the knowledge, technical understanding, and
perceptions of Sudanese healthcare professionals regarding the role of AI in improving
diagnostic accuracy in 2026.
Methodology: A descriptive, hospital-based cross-sectional study was conducted
between March and April 2026. Data were collected from 146 healthcare professionals
and final-year medical students at Atbara and Port Sudan Teaching Hospitals using a
structured digital questionnaire. Statistical analysis was performed using SPSS version
26.0, employing Chi-square tests to determine associations.
Results: The study revealed a high baseline awareness, with 85%–90% of participants
having heard of AI in medical practice and 82% recognizing its capabilities in medical
imaging. While general perception was highly positive—with 78.1% trusting AI's
diagnostic accuracy and 82.2% supporting its integration to reduce workload—a
"Paradox of Readiness" was identified, where high acceptance was contrasted by low
technical literacy in Machine Learning and Deep Learning. A statistically significant
association was found between higher knowledge levels and positive perceptions
toward AI application (p = 0.004).
Conclusion: Sudanese healthcare professionals demonstrate a robust readiness to adopt
AI as a tool for "diagnostic resilience." However, to transition from theoretical
acceptance to safe clinical implementation, there is an urgent need for formal
curriculum integration, technical training, and the establishment of clear regulatory
frameworks.
Keywords: Artificial Intelligence, Diagnostic Accuracy, Sudan, Healthcare
Professionals, Knowledge and Perception, Medical Education.
Description
Napata College