AI and Lyme Disease: Pioneering Advances in Diagnostic Accuracy

Abstract

Introduction and Purpose: Accurate diagnosis of Lyme disease remains challenging due to its varied manifestations and the limitations of current diagnostic tests. This review examines the emerging role of artificial intelligence (AI) in enhancing the diagnostic accuracy for Lyme disease, aiming to understand how these technologies can be integrated into clinical practice. State of Knowledge: AI and machine learning techniques are increasingly applied to improve diagnostic processes. In Lyme disease, AI models have been developed to identify patterns in clinical data, enhancing early detection and accuracy. Studies have focused on using AI to interpret complex serological results and clinical symptoms more effectively than traditional methods. Additionally, AI has been utilized to analyze geographical and epidemiological data to predict Lyme disease risk areas, aiding in preventive strategies. Summary: AI holds significant promise in transforming Lyme disease diagnostics by increasing the speed and accuracy of detection. These technologies not only help in overcoming the limitations of current serological testing but also provide a framework for predictive analytics in epidemiology. As AI models continue to evolve, their integration into healthcare systems requires careful consideration of ethical implications and validation on broader scales. Future research should focus on refining AI algorithms, improving data inclusivity, and enhancing interoperability with existing medical systems to fully realize AI's potential in battling Lyme disease.

Keywords

• Artificial Intelligence
• Lyme Disease
• Machine Learning