This is a national survey of doctors in Sudan who are involved in providing surgical care. The aim is to understand their awareness, experiences, and opinions about using artificial intelligence (AI) in surgery. The survey includes all cadres-house officers, medical officers, registrars, residents, specialists, consultants, and general practitioners who perform surgical procedures-working in public, private, NGO, and military hospitals across Sudan.

Participants are asked about their familiarity with AI, barriers to its use (such as poor infrastructure, lack of training, or cost), interest in training, and possible uses in Sudan's health system, especially in conflict-affected areas. The study does not test any AI tools but collects information to help design future AI solutions that work offline, in low-bandwidth conditions, and meet the needs of surgeons and surgical teams in Sudan.

Inclusion Criteria:

• Sudanese doctors currently practicing in Sudan. Provides surgical care in any capacity (house officer/intern, medical officer, registrar/resident, specialist/consultant, general practitioner performing surgical procedures).
• Works in any sector: public, private, NGO, or military hospital/clinic.

Exclusion Criteria:

• Not currently practicing in Sudan.
• Non-clinicians, students without surgical patient care roles.
• Duplicate or incomplete survey responses.

All Sudanese doctors actively providing surgical care across Sudan, including house officers, medical officers, registrars/residents, specialists, consultants, and general practitioners who perform surgical procedures. Participants may work in public, private, NGO, or military facilities, in both rural and urban settings. The population represents a wide range of surgical experience levels and facility types, providing a comprehensive national perspective on artificial intelligence readiness in surgical practice.

Individual participant data will not be shared because the dataset contains sensitive information from doctors working in conflict-affected regions of Sudan. Even with anonymization, the small and specialized population could allow re-identification. Only aggregated, de-identified results will be reported publicly.