Objective: To develop and validate a two-scale instrument assessing beliefs and behavioral intention toward AI-assisted diagnosis, grounded in the Theory of Planned Behavior (TPB). Methods: Following a literature-based item pool and a three-round Delphi consultation with 12 experts, two sequential studies were conducted (Study 1, n = 561; Study 2, n = 721) to evaluate the scales’ psychometric properties using exploratory and confirmatory factor analyses, among others. Study 2 further tested hypotheses using structural equation modeling and PREMANOVAs. Results: The 10-item AI-Assisted Diagnostic Beliefs Scale (AI-ADBS) and 4-item AI-Assisted Diagnostic Behavioral Intention Scale (AI-ADBIS) were developed and validated. Study 1 supported a three-factor structure for the AI-ADBS (Behavioral Attitudes, Subjective Norms, Perceived Behavioral Control) and a unidimensional structure for the AI-ADBIS. Both scales demonstrated high internal consistency and satisfactory convergent validity. Study 2 confirmed the TPB structural relationships, supporting theoretical coherence. PREMANOVA analyses indicated higher acceptance among healthcare professionals and more positive behavioral intentions among individuals with greater AI knowledge. Overall, the scales showed strong content and construct validity, high reliability, and meaningful criterion validity. Conclusion: The AI-ADBS and AI-ADBIS provide reliable, theory-driven tools for assessing beliefs and behavioral intention toward AI-assisted diagnosis, supporting research and practice in AI integration in healthcare.

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artificial intelligence,diagnostic assistance,technology acceptance,behavioral intention,scale development

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