Artificial intelligence (AI) and machine learning are rapidly transforming healthcare, particularly through the development of Clinical Decision Support Systems (CDSSs) and symptom checkers. AI-enabled CDSS leverage neural networks and machine learning algorithms to provide healthcare professionals with evidence-based recommendations, improving diagnostic accuracy and decision-making processes. Symptom checkers, used by patients for self-triage, offer guidance on appropriate care levels based on user input. While these tools show potential in improving care quality and efficiency, challenges remain, including concerns about accuracy, safety, lack of personalization, the risk of overtriage, and potential for misdiagnosis anxiety. Overtriage can lead to increased healthcare utilization, overtreatment, and costs, which might reduce overall system efficiency. Despite these challenges, AI-enabled CDSSs and symptom checkers can play a valuable role in primary healthcare, particularly by directing patients to the right place of treatment at the right time, easing clinician workloads and supporting patient engagement. The integration of these tools has been accelerated by the COVID-19 pandemic, which highlighted the need for innovative healthcare solutions. Key barriers to adoption include human factors, such as digital literacy, concerns about professional autonomy, and technological issues like system complexity. However, research has demonstrated that when thoughtfully integrated and used, these systems can improve clinical workflows and patient outcomes. Looking ahead, the future of AI in healthcare is promising, with the potential for the incorporation of advanced virtual assistants and mixed reality technologies to enhance patient care. As the technology evolves, it will be crucial to continue research on improving the safety, accuracy, and usability of AI-driven tools to ensure their effective integration into healthcare systems, while maintaining the essential human aspects of patient care.