Harnessing Artificial Intelligence for Early Disease Detection: Opportunities and Challenges in Modern Healthcare

Achile Solomon Egbunu; Akindele Michael Okedoye

doi:10.62411/jcta.15367

Authors

Achile Solomon Egbunu University of Alabama in Huntsville
Akindele Michael Okedoye Federal University of Petroleum Resources

DOI:

https://doi.org/10.62411/jcta.15367

Keywords:

Artificial Intelligence, Clinical Adoption, Early Disease Detection, Explainable AI, Healthcare Governance, Machine Learning, Predictive Analytics, Translational Review

Abstract

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.

Author Biographies

Achile Solomon Egbunu, University of Alabama in Huntsville

Department of Computer Science, University of Alabama in Huntsville, 301 Sparkman, AL 35899, United States

Akindele Michael Okedoye, Federal University of Petroleum Resources

Department of Mathematics, College of Science, Federal University of Petroleum Resources, Effurun 330102, Nigeria

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