This study presents a comparative analysis between expert systems and AI-powered chatbots in the
realm of healthcare diagnostics. Expert systems, traditionally rule-based, have long been used to
assist in medical decision-making, while AI-powered chatbots represent a more recent innovation,
leveraging advanced technologies such as machine learning and Natural Language Processing
(NLP). The comparison is drawn across multiple performance metrics, including accuracy, response
time, adaptability, user satisfaction, and scalability. Our results indicate that AI-powered chatbots
significantly outperform expert systems, particularly in areas like diagnostic accuracy (92% vs. 85%),
response time (10 seconds vs. 45 seconds), and adaptability to new conditions (85% vs. 20%). Chatbots
also demonstrated superior scalability, handling 10,000 users simultaneously compared to 500 for
expert systems. These findings suggest that AI-powered chatbots, with their real-time interaction, cost
efficiency, and continuous learning capabilities, offer a more robust and scalable solution for healthcare
diagnostics, making them better suited for modern telemedicine and patient engagement.
