Awareness, attitudes, and perceived barriers to artificial intelligence adoption in Turkish clinical biochemistry laboratories: an exploratory cross-sectional survey.

BACKGROUND: Artificial intelligence (AI) is increasingly recognized as transformative in laboratory medicine, yet adoption lags behind radiology and cardiology. Limited data exist on AI-related attitudes and perceived barriers in Turkish clinical laboratories. This study evaluated professional awareness, attitudes, and perceived barriers to AI adoption among Turkish clinical laboratory professionals. METHODS: A cross-sectional survey was conducted among Turkish clinical biochemistry staff between May and June 2025. The 30-item questionnaire covered demographics, AI knowledge, training, current use, future perspectives, and ethical or legal concerns. In total, 170 eligible participants, including laboratory directors, physicians, residents, and technologists, completed the survey. RESULTS: Among the participants, 43% self-rated their AI knowledge as "basic," with no participant aged ≥60 years reporting good or expert-level knowledge. The greatest knowledge deficits were in image processing (49.4% reported "no knowledge") and predictive analytics (35.9%). More than half (57.6%) had never received AI-related training, and current use of AI tools in routine practice was minimal. Despite these gaps, attitudes toward AI implementation were overwhelmingly positive, with 75.3% believing increased AI use would improve job satisfaction and 59.4% expecting laboratory transition to AI-supported systems within 5 years. Major barriers included insufficient digital infrastructure (47%), absence of trained personnel (83.5%), lack of standard operating procedures (70%), and inadequate legal/ethical regulations (52.9%). CONCLUSIONS: AI implementation in Turkish clinical laboratories remains at an early stage but is met with strong professional interest. Overcoming structural barriers through standardized education, infrastructure investment, and clear legal frameworks is essential for sustainable AI implementation in clinical laboratories.