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All Journal Mikailalsys Journal of Advanced Engineering International
Sani, Habibu Aminu
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

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Artificial Intelligence in Early Disease Detection: Trends, Applications, and Challenges Abba, Dadi Jonathan; Dudari, Mafeng Jamima; Jakawa, Jimmy Nirat; Sani, Habibu Aminu; Yona, Kudyo Deborah
Mikailalsys Journal of Advanced Engineering International Vol 3 No 2 (2026): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v3i2.9226

Abstract

Artificial intelligence (AI) is transforming healthcare by improving diagnostic precision, reducing clinician workload, and supporting early disease detection. Early diagnosis is essential for improving patient outcomes, reducing mortality, and lowering healthcare costs. This study examines current developments in AI-assisted diagnostics, with particular attention to applications in cancer, cardiology, neurology, infectious diseases, and personalized medicine. It discusses how AI, through machine learning, deep learning, and predictive analytics, can process large-scale medical datasets, analyze medical images, and support physicians in clinical decision-making. The findings indicate that AI offers substantial benefits for healthcare practice, including improved diagnostic accuracy, enhanced patient monitoring, reduced clinical errors, and more efficient decision support. However, major barriers remain, including algorithmic bias, high implementation costs, data privacy concerns, inadequate physician training, and unresolved ethical issues. The study concludes that the effective adoption of AI in early disease diagnosis requires collaborative research, robust policy frameworks, ethical governance, and practical integration strategies. These insights contribute to current discussions on AI-enabled healthcare by highlighting both its diagnostic potential and the institutional, technical, and ethical conditions needed to optimize its implementation in healthcare delivery.
Co-Authors Abba, Dadi Jonathan Dudari, Mafeng Jamima Jakawa, Jimmy Nirat Yona, Kudyo Deborah