Advanced Insights into AI-Powered Diagnostics: An Analytical project Framework for Healthcare Innovation

Abstract

This study explores which factors affect U.S. healthcare professionals’ familiarity with, adherence to, satisfaction with and perceived challenges of using AI diagnostic protocols depending on the role of employment and healthcare facility type. With AI technology supporting the diagnostic process more and more, it is important to understand the adoption barriers and satisfaction levels across different clinical environments, allowing potential for AI to be optimized in healthcare. The study was a cross-sectional survey of 100 US healthcare professionals from public hospitals, private hospitals and clinics. The survey evaluated levels of familiarity with AI protocols, levels of adherence, levels of satisfaction and challenges in the implementation of AI. Analysis of data was done by SPSS, descriptive statistics, Chi-Square tests, ANOVA, logistic regression and correlation analysis. Quantitative findings were contextualized through the open-ended responses about implementation challenges that were thematically analyzed. The study revealed a strong positive relationship between familiarity with AI diagnostic protocols and adherence, with healthcare professionals who were trained more adherent. AI protocols were equally well received across job roles, although experienced professionals (e.g. pediatricians) were more satisfied with AI protocols than residents and interns and they had more concerns about autonomy. While public hospitals were the most satisfied with AI diagnostics, smaller clinics faced more patient specific challenges and infrastructure limitations. We found that experienced professionals were more aware of protocol gaps and that clinician feedback is critical for refining AI diagnostic tools. The results of this study suggest that AI integration in U.S. healthcare requires tailored strategies, from role-specific training to facility-based resource allocation to active clinician participation in AI protocol development. Identified barriers to adoption of AI by healthcare professionals at all experience levels and how to address them and we identified how to increase support for healthcare professionals across the clinical spectrum in the U.S. to more effectively, equitably and efficiently adopt AI in a variety of clinical settings, leading to better patient outcomes and operational efficiency.

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