Development and validation of a scale to assess AI dependency in healthcare professionals and students: a mixed-method study

Abstract Background Artificial Intelligence (AI) has revolutionized healthcare, offering opportunities to improve decision-making and patient outcomes. However, dependency on AI among health professionals and students raises concerns regarding its role in healthcare delivery. This study aimed to develop and validate a scale to assess AI dependency among healthcare professionals and students, exploring its dual role as a “Helping Hand” or “Barrier”. Methodology A mixed-methods study was conducted in two steps. The qualitative phase in-depth interviews and group discussions were conducted with healthcare professionals and students to identify factors influencing AI dependency. The findings helped to develop a preliminary scale with 7 items. In the quantitative phase, the scale was tested on a larger sample ( n = 374) for psychometric evaluation. The Reliability and validity was assessed using Cronbach’s alpha and conformatory factor analysis. Results The outcome measure consists of excellent reliability (Cronbach’s alpha = 0.799). The factor analysis (ANOVA, t test) is confirmed to be a good model with a significant p value of 0.000. The study findings show that the outcome measure is reliable and valid to evaluate the AI dependency in students and healthcare professionals. Conclusion The newly developed scale is a reliable and valid tool for assessing AI dependency among health professionals and students. Insights from this scale can guide strategies to balance AI integration, ensuring it serves as a helping hand rather than a barrier in healthcare.