AI-Based Computational Pathology for Precision Lung Cancer Management: A Systematic Review and Meta-Analysis of Diagnostics and Prognostic Algorithms

The global prevalence of lung cancer calls for innovative methods to improve diagnosis and treatment, particularly in developing nations where there is a critical shortage of onco-pathologists. This systematic review examines the role of artificial intelligence (AI) in lung cancer diagnosis, focusing on machine learning and deep learning ap-proaches as potential solutions to this challenge. The evaluation utilized PRISMA guidelines to include 14 studies for conducting a meta-analysis that measured AI-based tool effectiveness in lung cancer pathology diagnosis. Key performance metrics ana-lyzed included sensitivity, specificity, and predictive values. Advanced AI architec-tures, particularly convolutional neural networks (CNNs) and generative adversarial networks (GANs), were identified as instrumental in enhancing diagnostic accuracy and enabling large-scale screening. The review also investigates AI-based identification of promising biomarkers which enhance medical diagnosis while modernizing clinical procedures for improved workflow success. AI has demonstrated success in reducing clinical workloads, thereby optimizing healthcare operations. This review further ad-dresses critical barriers to implementation, including limited generalizability across diverse populations and ethical concerns related to clinical deployment. Unlike pre-vious reviews, this work evaluates AI technologies across multiple imaging modalities, including histopathology, computed tomography (CT), and X-rays. The findings sug-gest that AI has significant potential as a transformative tool for achieving more accu-rate diagnoses, facilitating personalized treatment strategies, and ultimately improving patient outcomes worldwide.