Integrating Artificial Intelligence into Primary Care: Implications for Diagnostic Accuracy and Patient Safety

Abstract

implementation in primary care. Its capabilities—ranging from automated diagnostics to predictive analytics—offer new opportunities to enhance diagnostic accuracy and improve patient safety. However, concerns regarding algorithmic bias, data reliability, and clinical overdependence on AI systems remain prevalent. This article analyzes the dual nature of AI adoption in primary care, focusing on benefits, patient safety implications, ethical risks, and implementation barriers. Using reported performance analyses, conceptual models, and comparative metrics, the study reveals that AI can reduce diagnostic errors by up to 50% but requires strong safeguards to ensure equitable and safe usage. The paper concludes with recommendations for responsible AI integration in low- and middle-income healthcare systems.