Bridging the artificial intelligence gap in oncology: A national survey of Cameroonian oncologists’ perspectives, readiness, and barriers to artificial intelligence adoption.

623 Background: Artificial intelligence (AI) has demonstrated significant potential in oncology, improving diagnostics, treatment planning, and personalized medicine. However, its adoption in low- and middle-income countries (LMICs) like Cameroon remains underexplored. This study assesses the perceptions, barriers, and readiness of medical, surgical, and radiation oncologists in Cameroon toward AI integration in oncology practice. Methods: We conducted a cross-sectional survey among oncologists practicing in Cameroon, distributing a structured questionnaire both electronically and in person. The survey assessed AI familiarity, perceived benefits, concerns, barriers, and willingness to adopt AI-based technologies. We used Python and SPSS Version 30 to analyze the data using descriptive statistics, chi-square tests, and logistic regression. Results: A total of 29 oncologists participated, with the majority aged 31–40 years (82.4%) and predominantly specializing in medical oncology (55.2%). AI familiarity was moderate (36%), with 44% reporting limited knowledge. While 80-90% of respondents recognized AI's potential to improve diagnostic accuracy and treatment planning, concerns included ethical/privacy issues (50-60%), reduced doctor-patient interaction (40-50%), and risks of misdiagnosis (15%). Despite these concerns, 82.1% expressed moderate-to-high willingness to adopt AI, citing the need for structured AI training (89.7%) and regulatory guidelines. Barriers included cost (69%), lack of training (65.5%), and infrastructure constraints (62.1%). The majority (96.2%) were willing to participate in AI training programs. Conclusions: While oncologists in Cameroon acknowledge AI’s potential benefits in the field of oncology, significant barriers related to training, ethics, and infrastructure hinder adoption. Tackling these issues with AI education, clear policies, and better digital healthcare systems is essential for making the most of AI, which can improve patient care by making diagnoses more accurate, personalizing treatment, streamlining clinical processes, and supporting data-based decisions in low- and middle-income countries.