Physicians' perceptions of artificial intelligence in clinical and academic practice: a mixed-methods study from Erbil City

Abstract

Background and objective: Integration of artificial intelligence (AI) in healthcare and medical research represents significant potential; however, it also introduces practical and ethical challenges. This study analyzes physicians' perceptions of AI implementation in clinical and academic settings, exploring their awareness, perceptions, barriers, concerns, and insights into ethical and regulatory considerations. Methods: A mixed-methods approach was used. The quantitative component involved standardized surveys completed by 390 physicians (241 clinical, 149 academic) in Erbil City from November 2024 to June 2025. Participants were selected using systematic sampling, and surveys were self-administered. The qualitative component included two focus group discussions, involving 6 clinical physicians and 8 academic physicians, to explore in-depth perceptions and concerns regarding AI in healthcare and research. Results: Of 390 physicians, the majority of participants were male (69.2%). Mean age was 46.7±7 years. Familiarity with AI was higher among younger, male, and academic physicians, with age showing a negative association (p <0.001). Clinical physicians reported more concerns regarding privacy in healthcare (p=0.003) and research (p=0.041), as well as algorithmic bias (p=0.030). Despite these concerns, most physicians valued AI in healthcare and academic research and demonstrated strong interest in training and collaboration. Qualitative findings showed optimism and frequent use of AI tools such as ChatGPT and DeepSeek, often described as “Google replacements.” However, physicians also highlighted ethical issues and the need for local training and infrastructure improvement. Conclusion: Physicians generally welcome the use of AI in healthcare and research but emphasize the necessity of ethical guidelines, peer collaboration, and formal education. To ensure responsible adoption, addressing infrastructural and trust barriers will be essential for guaranteeing that AI technologies improve rather than undermine patient care and research integrity.