A decade’s overview of artificial intelligence in diagnosing: a scoping review

The impact of Artificial Intelligence (AI) in healthcare is undeniable, aiding physicians in diagnosing diseases. This study aims to synthesize the literature to examine the progress of AI in diagnosing Tuberculosis (TB), one of the deadliest diseases in the world. The review also provides a taxonomy for AI-based studies for TB diagnosis. A scoping review approach was adopted using the PRISMA-Scoping guidelines, focusing on types of studies, focus area, algorithms/models, and key results. Relevant articles published from 2013 to 2022 were sought using PubMed, Web of Science, and Scopus, resulting in 199 articles included in the review. The use of AI, especially deep learning, has increased since 2016, particularly for diagnosing TB using chest X-Rays (CXR). Most studies focused on diagnosing TB using CXR, Computed Tomography, biomarkers, sputum smear, and drug resistance and recovery. Convolutional Neural Networks (CNNs) and their variants were commonly used in deep learning, while Support Vector Machine (SVM), Decision Trees, and ensemble algorithms performed well in machine learning. CNN outperformed other variants for CXRs (accuracy from 80 to 100%) probably due to their ability to handle high-dimensional data and extract features. In contrast, simpler algorithms like Naive Bayes underperformed compared to other algorithms (accuracy range: 79–89%), showing their limitations in dealing with complex TB diagnostic data. The use of AI approaches, namely machine and deep learning are expected to increase in the future, hence promising a rapid and cost-effective (and potentially sustainable) alternative solution for efficient TB diagnosis.