ARTIFICIAL INTELLIGENCE IN HEALTHCARE: ACCEPTANCE, UTILIZATION, AND CHALLENGES AMONG MEDICAL PROFESSIONALS IN THE PHILIPPINES
Keywords:
Artificial Intelligence, Technology Acceptance Model, UTAUT, Healthcare Digitization, Healthcare Policy, Philippines, Clinical Decision Support SystemsAbstract
Artificial Intelligence (AI) represents a paradigm-shifting force in global healthcare, offering critical solutions to clinical and administrative burdens; however, in low- and middle-income countries (LMICs) such as the Philippines, its translation into clinical practice remains structurally and culturally constrained. This study investigates the determinants of AI acceptance, utilization, and challenges among medical professionals in the Philippines using an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework incorporating Perceived Risk and Trust. A cross-sectional explanatory research design was conducted among 384 physicians and nurses from tertiary healthcare institutions in Metro Manila, with structural equation modeling and hierarchical multiple regression used to examine the relationships among Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Perceived Risk, Trust, Behavioral Intention, and Actual Utilization. Findings revealed a substantial acceptance-utilization gap, characterized by high Perceived Risk (M = 3.97, SD = 0.75), moderate Behavioral Intention (M = 3.61, SD = 0.74), and low Actual Utilization (M = 2.88, SD = 0.80). The extended model explained 64.9% of the variance in Behavioral Intention and 55.4% in Actual Utilization. Trust was significantly suppressed by Perceived Risk (β = -0.3129, p < 0.001) but emerged as the strongest predictor of Behavioral Intention (β = 0.3292, p < 0.001), while Facilitating Conditions exerted a strong direct effect on Actual Utilization (β = 0.3032, p < 0.001). These findings indicate that although Filipino medical professionals are receptive to AI, systemic deficits in data interoperability, institutional policies, digital infrastructure, and ethical guidance hinder meaningful adoption. Healthcare institutions should therefore transition from ad hoc AI adoption toward structured, competency-based training, strengthened digital infrastructure, and localized ethical guidelines that reflect contextual values such as pakikiramdam.
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