Radiologists’ Views on AI and the Future of Radiology: Insights from a U.S. National Survey

Abstract Objectives To evaluate U.S. radiologists’ attitudes toward artificial intelligence (AI) in radiology, identify demographic factors influencing these perceptions, and analyze the potential challenges and opportunities AI integration presents in radiological practice. Methods A cross-sectional survey of 322 board-certified radiologists was conducted using Amazon Mechanical Turk (MTurk) and Qualtrics. The survey collected demographic data (age, gender, experience, and subspecialty) and assessed attitudes toward AI integration in radiology. Pearson’s chi-square tests were used to evaluate correlations between demographic variables and perceptions of AI’s impact, confidence in its role, and anticipated adoption timelines. Results The majority of radiologists (82.9%) indicated that AI would significantly impact radiology. Younger radiologists (40 years) displayed higher optimism and greater familiarity with AI tools compared to their older counterparts. Statistical analysis revealed significant correlations between age and optimism (χ2 = 47.551, p 0.001) and between gender and confidence in AI’s role (χ2 = 21.982, p 0.001). Subspecialty differences emerged, with 87.5% of emergency radiologists anticipating AI adoption within 3-5 years, whereas 26.3% of pediatric radiologists predicted adoption within 6-10 years. Notably, younger radiologists showed increased susceptibility to errors when evaluating misleading AI-generated outputs, underscoring the necessity for structured training programs. Conclusions The integration of AI in radiology holds transformative potential but poses challenges, including overreliance, varying familiarity levels, and subspecialty-specific disparities. Structured education and robust regulatory frameworks are critical to optimize AI’s adoption and minimize associated risks. Advances in knowledge This study highlights significant demographic variations in radiologists’ attitudes toward AI and underscores the importance of targeted training and interventions to support effective AI integration. These findings add to the existing research by emphasizing the necessity for structured AI training tailored to demographic and subspecialty needs.