Systematic review highlights trust as essential for adoption of AI technologies in primary care, suggesting implications for patient care.
Artificial intelligence beyond perceptions: Trust as the missing link in primary careLa inteligencia artificial más allá de las percepciones: la confianza como vínculo perdido en atención primaria Dear Editor,We read with great interest the systematic review by Prat-Carreño et al. examining the social effects and controversies surrounding artificial intelligence (AI) in primary care (PC). 1 Through the identification of ten themes ---including ethics, physician---patient relationships, governance, bias and equity, professional and patient perceptions, and future imaginaries ---the authors provide a valuable overview of the social dimensions of AI in primary care.Most notably, they reveal a considerable gap between studies focused on expectations and perceived potentialities and those examining the real-world social effects of AI implementation.We would like to expand this discussion by proposing that trust may represent the missing link between AI potential and AI adoption in primary care.The themes identified by Prat-Carreño et al. suggest that the success of AI is unlikely to depend solely on technical performance.Rather, it may depend on whether patients and healthcare professionals trust the systems that increasingly inform clinical decisions, influence healthcare delivery, and shape patient experiences.Trust is not merely an ethical consideration; it is a practical determinant of whether AI-supported recommendations are accepted, questioned, or integrated into routine clinical care.This perspective is particularly relevant because many current discussions surrounding AI continue to emphasize innovation, efficiency, and future possibilities.However, technological promises do not automatically translate into acceptance or meaningful use.Trust is built when AI systems are perceived as transparent, explainable, fair, accountable, and aligned with patient-centered values. 2---4 Conversely, concerns regarding algorithmic bias, privacy,
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Alngag et al. (2026) studied this question.
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