Abstract

Background

Artificial Intelligence (AI) is rapidly transforming healthcare systems worldwide. In primary care settings, AI-powered technologies are increasingly being used to support clinical decision-making, improve diagnostic accuracy, optimize treatment planning, and enhance patient outcomes. The growing integration of machine learning algorithms, predictive analytics, and natural language processing into clinical workflows presents both opportunities and challenges for healthcare professionals.

Objective

This study explores the impact of Artificial Intelligence on clinical decision-making in primary care by examining its effectiveness, benefits, limitations, and future implications.

Methods

A comprehensive review of current literature and healthcare implementation reports was conducted. Data from peer-reviewed articles, healthcare organizations, and AI-based clinical decision support systems were analyzed to evaluate the role of AI in diagnosis, treatment planning, risk prediction, and patient management.

Results

Findings indicate that AI-assisted decision support systems improve diagnostic accuracy by approximately 15–25%, reduce medical errors, enhance workflow efficiency, and support personalized patient care. However, concerns remain regarding algorithm bias, data privacy, transparency, physician trust, and regulatory compliance.

Conclusion

Artificial Intelligence has significant potential to augment clinical decision-making in primary care. Successful implementation requires robust governance frameworks, continuous validation, clinician training, and ethical oversight to ensure safe and equitable healthcare delivery.