Advancing Early Disease Detection with AI: Innovations in Medical Imaging, EHR Analytics, and Wearable Technologies
1 Department of Supply Chain Management, Marketing, and Management, Wright State University, Ohio, USA.
2 Department of Biological Sciences, University of Camerino, Italy.
3 Department of Healthcare Management, University of New Orleans, Luisiana, USA.
4 Department of Pharmacy, University of Benin, Benin city, Nigeria.
5 Department of Computer Science, Kwame Nkrumah University of Science and Technology, Ghana.
6 Department of Internal Medicine (Consulting group), Obafemi Awolowo University Health Center, Ile-Ife, Nigeria.
7 Department of Computer Engineering; Olabisi Onabanjo University, Ago-iwoye, Nigeria.
Review
International Journal of Life Science Research Archive, 2025, 09(01), 120-140.
Article DOI: 10.53771/ijlsra.2025.9.1.0049
Publication history:
Received on 01 July 2025; revised on 17 August; accepted on 20 August 2025
Abstract:
Artificial intelligence (AI) has transformed early disease detection by leveraging medical imaging, electronic health record (EHR) analytics, and wearable technologies, enabling timely interventions for high-burden conditions such as cancer, cardiovascular diseases, and neurodegenerative disorders. This review takes a deep look into recent research advancements, highlighting AI’s role in enhancing diagnostic precision and shifting healthcare toward prevention. Medical imaging systems, powered by deep learning, achieve high sensitivity in detecting malignancies like breast and lung cancer, while EHR analytics predict chronic diseases such as diabetes and heart failure years in advance using structured and unstructured clinical data. Wearable devices, equipped with sensors for heart rate and oxygen saturation, provide real-time monitoring for conditions like atrial fibrillation and respiratory distress, demonstrating global impact in applications like COVID-19 screening. Multi-modal AI systems integrate these technologies to deliver comprehensive risk assessments, improving accuracy and personalization. Real-world case studies, including large-scale cancer screening in Asia and wearable-based monitoring in the US, underscore AI’s potential to reduce healthcare costs and improve outcomes. However, challenges such as data bias, privacy concerns, interoperability issues, and regulatory hurdles limit widespread adoption. Future directions include developing transparent, interoperable AI systems and inclusive datasets to ensure equitable access, particularly in low-resource settings. By addressing these barriers through interdisciplinary collaboration, AI-driven early detection can revolutionize preventive healthcare, delivering accurate, accessible, and equitable diagnostics worldwide.
Keywords:
Artificial Intelligence; Early Disease Detection; Medical Imaging; EHR Analytics; Wearable Technologies; Multi-Modal AI; Healthcare Equity; Preventive Healthcare
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Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0