Early Cancer Detection Using AI

Cancer remains a major global health challenge, and outcomes are often improved when disease is identified at an early stage.Artificial intelligence (AI) can support screening and clinical decision-making by analysing medical images, pathology slides,laboratory measurements, and structured clinical records. This paper reviews a conceptual framework for AI-assisted earlycancer detection, focusing on data preparation, model development, clinical validation, explainability, and safe integration intohealthcare workflows. It outlines how machine-learning and deep-learning methods may help identify suspicious patterns andprioritize cases for expert review. The proposed workflow emphasizes that AI output is decision support rather than areplacement for qualified clinicians. Data quality, population representativeness, privacy, bias, and external validation aretreated as core requirements. The paper also presents an evaluation plan using sensitivity, specificity, precision, F1-score,calibration, and subgroup analysis. No clinical trial or original patient-data experiment is claimed; the framework is intended asa structured basis for future implementation and validation