Patient-centered insights: Unraveling the drivers of AI acceptance in healthcare - E Sobaih, Abu Elnasr; Chaibi, Asma; Nagara, Zeineb Al Hadi; Elshaer, Ibrahim A (International Journal of Innovative Research and Scientific Studies, 2025)
The research study delved into the nuanced human aspect of artificial intelligence (AI) in health care, focusing on what is fundamentally important to patients in accepting this radical technology. With patients at the center of the research, it explored how social influence, individual backup choices, and trust influence the acceptance of AI healthcare services. The survey, which used 450 participants, tested Structural Equation Modeling (SEM) using AMOS and found the powerful role of such factors. Social influence (what do others think or say about AI) comes out strongly to shape patients’ perceptions. Personal backup desire (the need to know or feel secure in human support being always an option) is another crucial variable. Last, and most importantly, trust in the reliability and safety of AI systems is the bedrock of acceptance. This study did not just deal with numbers but speaks a human story where trust, reliability, and social connection can drive AI adoption. These insights are a guide for practical recommendations to healthcare providers and policymakers on not only how to nurture trust but also engage with patients in meaningful ways and balance this with the human touch. This is how health care is transformed by AI, not as a replacement but in a way that patients can embrace with confidence and satisfaction.