Meningoencephalitis Diagnosis Using Contemporary Diagnostic Advancements

Abstract

Graphical Abstract JOURNAL/neuin/04.03/02223311-202507000-00004/figure1/v/2025-07-24T100922Z/r/image-tiff By enhancing meningitis diagnosis, monitoring, and treatment, artificial intelligence (AI) and machine learning (ML) are transforming healthcare and resulting in job automation and worker displacement. In order to create better diagnostic techniques, this paper evaluated the predictive and diagnostic potential of ML and AI algorithms for meningoencephalitis. Reputable scientific databases like Embase, ScienceDirect, PubMed, Web of Science, and Medline are used in this paper's systematic reviews and meta-analyses. While systematic reviews and meta-analyses are carried out using the PRISMA flow chart, studies focused on the diagnosis and prediction of meningitis using ML are carried out in English. The included studies in the systematic review satisfied the inclusion criteria. Data were gathered from a number of databases, the most pertinent of which being Science Direct. To choose the study papers, a PRISMA chart was employed. Approximately 309,995 papers were chosen, while 295,629 duplicates were eliminated. After 14,366 data were screened, 31 original publications were removed, leaving 34 studies chosen for assessment. One was published in 2016 (2.9%) until 2024, eight in 2021 (23.5%), four in 2022 (11.76%), nine in 2023 (26.47%), six in 2024 (17.64%), and two in 2019 (5.8%). The study found that AI and ML enhance clinical processes and decentralization by enhancing meningoencephalitis diagnosis, risk assessment, and resource efficiency. Future research should focus on advanced diagnostics and metanalyses.

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