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Artificial Intelligence in Health

ORIGINAL RESEARCH ARTICLE
Artificial intelligence within medical

diagnostics: A multi-disease perspective

Zarif Bin Akhtar*
Department of Computing, Institute of Electrical and Electronics Engineers, Piscataway, United States
of America

Abstract
Artificial intelligence (AI) has become a transformative technology in medical
diagnostics, enabling enhanced analysis of complex clinical data and supporting
precise, efficient decision-making across diverse disease areas. This study explores
the multi-disease application of AI in diagnosing cancer, cardiovascular diseases,
neurological disorders, and infectious diseases, focusing on its role in improving
diagnostic accuracy, speeding diagnostic processes, and facilitating early disease
detection. By employing machine learning, deep learning, and neural network
models, this study critically examines the performance of specific models – such
as recurrent neural networks and support vector machines – in diverse healthcare
contexts. Challenges addressed include data privacy, annotated dataset needs,
overfitting risks, and ethical concerns such as AI bias and transparency, all of which
are fundamental to ensuring patient safety and health equity. In addition, this
study integrates security considerations, such as fault detection in cryptographic
*Corresponding author: architectures, providing insights into the resilience of AI systems in healthcare. Future
Zarif Bin Akhtar
(zarifbinakhtar@ieee.org) research directions, including the potential of AI in real-time patient monitoring,
personalized medicine, and multispectral imaging, are proposed to expand AI’s
Citation: Akhtar ZB. Artificial
intelligence within medical utility in diagnostics. A comparative evaluation with traditional clinical diagnostics
diagnostics: A multi-disease underscores AI’s validation potential, emphasizing its need for robust regulatory
perspective. Artif Intell Health. frameworks, particularly concerning global health standards (e.g., TRIPOD-AI and
2025;2(3):44-62.
doi: 10.36922/aih.5173 CONSORT-AI) and data privacy regulations such as Health Insurance Portability and
Accountability Act and General Data Protection Regulation. Ultimately, AI-driven
Received: October 16, 2024
diagnostic systems show strong promise to revolutionize medical practice and
1st revised: November 19, 2024 improve patient outcomes, contingent on addressing the technical, ethical, and
2nd revised: December 9, 2024 regulatory challenges involved. This research supports AI’s growing role in healthcare,
providing a foundational understanding of both its current contributions and future
Accepted: December 12, 2024
potential across disease-specific applications.
Published online: January 6, 2025
Copyright: © 2025 Author(s). Keywords: Artificial intelligence; Biomedical applications; Data informatics; Deep
This is an Open-Access article
distributed under the terms of the learning; Healthcare informatics; Machine learning; Medical informatics
Creative Commons Attribution
License, permitting distribution,
and reproduction in any medium,
provided the original work is
properly cited. 1. Introduction
Publisher’s Note: AccScience The rapid advancements in artificial intelligence (AI) are profoundly reshaping industries
Publishing remains neutral with worldwide, with healthcare emerging as a critical field poised to benefit substantially from
regard to jurisdictional claims in
published maps and institutional these innovations. Among the various domains in healthcare, medical diagnostics holds
affiliations.*Corresponding author: exceptional promise for transformation through AI-driven technologies. Diagnostic

Volume 2 Issue 3 (2025) 44 doi: 10.36922/aih.5173