AI-Powered Diagnostic Tools for Infectious Diseases in Low-Resource Settings: A Comprehensive Review (Preprint)

<sec> <title>UNSTRUCTURED</title> Artificial Intelligence (AI) is transforming various industries, including healthcare, by improving diagnostic accuracy and treatment planning. AI techniques such as machine learning (ML), deep learning (DL), and natural language processing (NLP) have shown great promise in automating medical diagnostics, predicting disease outcomes, and optimizing healthcare services. This review explores the current state of AI in medical diagnostics, highlighting its applications, challenges, and opportunities, particularly in low-resource settings. AI-driven diagnostic tools have been successfully applied in imaging analysis, disease prediction, and clinical decision support systems, demonstrating improved efficiency and accuracy. Additionally, AI is being utilized in drug discovery, vaccine development, and personalized treatment approaches. However, challenges such as infrastructure limitations, data privacy concerns, and ethical considerations hinder AI's full integration into healthcare systems, especially in low- and middle-income countries (LMICs). This review provides insights into emerging AI technologies, their potential impact on medical diagnostics, the challenges of implementation, and recommendations for improving AI adoption in healthcare, particularly in resource-limited environments. </sec>