Prediction of Medical Pathologies: A Systematic Review and Proposed Approach

Abstract

Healthcare is essential in every society, and the adoption of innovative technologies such as artificial intelligence (AI), big data, machine learning (ML), and deep learning (DL) is revolutionizing medical practices by enabling innovative approaches to pathology prediction and clinical decision-making. This systematic review examines 61 key articles published between 2018 and 2024 to evaluate the state of the art in medical data processing and pathology prediction. Based on this review, we identify critical challenges in current methodologies, including data integration and interpretability. To address these issues, we propose an integrated framework combining data collection, pre-processing, mapping, and clustering with advanced analytics. This approach aims to streamline the medical data pipeline, enhance diagnostic processes, and provide a foundation for future research and clinical implementation.