Machine Learning and Deep Learning for Early Disease Detection Across Modalities: A Comprehensive Review of Methods, Applications, and Clinical Translation.

Early disease detection is not a single classification problem. It includes population screening, presymptomatic risk estimation, identification of early pathological change, and detection of disease before conventional clinical recognition. Machine learning and deep learning systems approach these targets through medical images, physiological signals, longitudinal health records, molecular measurements, wearable sensors, and multimodal combinations. This survey organizes the evidence around the complete decision pathway linking the clinical target, data generation process, preprocessing, representation, model, validation design, reported performance, methodological limitation, and level of clinical translation. The analysis distinguishes early detection from ordinary diagnosis, prognosis, and progression prediction, as merging these tasks can make apparently strong results clinically misleading. Across modalities, deep learning is most useful when spatial, temporal, or cross-modal representations must be learned from sufficiently large and representative data. Traditional machine learning remains competitive when cohorts are modest, clinically meaningful variables are available, or transparent feature-level reasoning is required. The evidence also shows that performance is strongly influenced by patient-level separation, cohort construction, prevalence, label definition, missingness, external validation, calibration, and the handling of uncertain cases; therefore, high discrimination on an internal split cannot establish clinical utility. The survey further examines the gap between retrospective model development and deployment, emphasizing temporal and external validation, prospective workflow evaluation, decision thresholds, false negative consequences, demographic robustness, interpretability, and post-deployment monitoring. The resulting synthesis provides a modality-aware and clinically grounded framework for deciding not only which algorithms perform well but also what their results mean and what evidence remains necessary before they can support early clinical action.