The use of artificial intelligence tools in the perception of electroradiologists

Background Artificial intelligence (AI) is playing an increasingly important role in diagnostic imaging, helping specialists improve the quality and speed of medical services. Despite the potential of AI, electroradiologists express concerns about algorithm errors, overreliance on technology, and ethical issues. Further training of medical personnel is necessary to ensure the safe and informed implementation of AI in diagnostics. This study examines the use of AI tools as perceived by radiographers, focusing on their impact on work organization. Material and Methods The work was carried out using a diagnostic survey method in the form of questionnaires. The survey was conducted in the second quarter of 2025. The analysis used both descriptive statistics and statistical tests to assess the significance of differences and relationships between variables. Descriptive statistics analyzed quantitative variables. Statistical calculations were based on the χ 2 test. A significance level of p < 0.05 was adopted. In analyses considering respondents’ age, age groups were categorized accordingly. Results The study involved 202 professionally active electroradiologists (working in diagnostic imaging and interventional radiology) – 166 women (82.18%) and 36 men (17.82%), with an average age of 31.75 years. Analyses showed no statistical correlation between education, age, work experience, and level of knowledge about AI. However, correlations appeared in the implementation of these tools across medical facilities and their use in radiographers’ work. In-depth analyses revealed a positive attitude toward AI tools but also highlighted insufficient education and the need for training. Conclusions Most electroradiologists are familiar with the general concept of AI, but lack detailed knowledge, indicating a need for targeted education. Despite a positive attitude towards AI, a lack of training limits readiness for its implementation. Age and workplace influence the perception of AI, while education, gender, and seniority remain insignificant. The key barriers are competency- and organization-related, highlighting the need for consistent educational programs. Med Pr Work Health Saf. 2026;77(2):147–161