Community bacterial meningitis: current challenges and use of artificial intelligence in diagnosis, management and treatment

Abstract

Community-acquired bacterial meningitis is a severe central nervous system infection characterized by rapid progression and high morbidity and mortality rates. Early diagnosis remains a challenge due to the nonspecificity of symptoms and limitations of conventional laboratory methods. In recent years, technological advancements, including artificial intelligence (AI) and advanced molecular testing, have improved diagnostic accuracy and optimized clinical management. This study reviews the main challenges in the diagnosis, management, and treatment of bacterial meningitis, emphasizing the application of emerging technologies. A systematic literature review was conducted using the PRISMA methodology, selecting articles published between 2018 and 2025 from indexed databases. Studies addressing clinical, epidemiological, microbiological, and therapeutic aspects of the disease were included. The results indicate that techniques such as multiplex PCR have enhanced pathogen detection, while AI algorithms have aided in predicting clinical outcomes and identifying antimicrobial resistance. However, challenges persist, including the need for large-scale validation of these technologies and expanded access to advanced diagnostic methods in low- and middle-income countries. It is concluded that integrating new technological approaches into the diagnosis and treatment of bacterial meningitis can significantly improve patient outcomes and mitigate the impact of antimicrobial resistance.