A Survey on Artificial Intelligence Techniques for Early Disease Prediction Across Multiple Medical Domains

Artificial Intelligence is rapidly transforming pre- dictive and preventive healthcare by enabling early disease detection before clinical symptoms reach irreversible stages. The convergence of classical machine learning, deep learning, neuroimaging, structured electronic health records, and symptom intelligence has established a disruptive paradigm of AI-assisted precision medicine. This survey critically examines representative methodologies in cardiovascular risk stratification using struc- tured clinical variables, Alzheimer’s disease assessment using neuroimaging, liver disease screening from laboratory biomark- ers, and symptom-based diagnostic triage systems. We revisit dominant design choices, compare technical approaches, and outline scientific, ethical, validation and deployment challenges associated with operationalizing AI-driven early screening across medical domains.