<![CDATA[Artificial Intelligence (AI) is increasingly recognized as a transformative enabler of early disease detection, with the potential to improve diagnostic accuracy, support predictive risk stratification, and advance preventive healthcare. Despite rapid methodological progress, many existing reviews remain performance-centric, offering limited insight into generalizability, ethical governance, and real-world implementation constraints. This paper presents a narrative and integrative review with an adoption-focused, translational perspective, synthesizing recent developments in AI-driven early disease detection across oncology, cardiology, neurology, and infectious disease surveillance. Drawing on peer-reviewed literature published primarily between 2016 and 2025, the review examines reported performance gains alongside persistent limitations related to data heterogeneity, population bias, explainability, and regulatory fragmentation. Through cross-sectional synthesis, we identify three recurring gaps in prior reviews: (i) overgeneralization of AI’s diagnostic superiority, (ii) insufficient consideration of ethical and legal accountability, and (iii) a lack of actionable guidance for scalable clinical implementation. Integrating technical, ethical, and policy dimensions into a unified conceptual framework, this review demonstrates that while AI systems can consistently enhance diagnostic accuracy and early risk stratification in well-defined tasks, sustained clinical adoption depends on aligning technical performance with governance readiness, interpretability, and workflow integration. The analysis further highlights how implementation mechanisms—such as explainable AI, continuous post-deployment monitoring, and clinician-centered deployment strategies—mediate the translation of algorithmic innovation into real-world healthcare impact. Overall, this review provides a critical reference for researchers, clinicians, and policymakers seeking to translate AI innovation into safe, equitable, and trustworthy clinical practice.]]>
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