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Volume 1 , Issue 1 , PP: 01-16, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
El-Sayed M. El-kenawy 1 *
Doi: https://doi.org/10.54216/MOR.010101
Artificial intelligence (AI) is revolutionizing the problem solving of medical diagnosis, which has enduring issues, including early-stage disease, insufficient voluminous data, and diagnosis process ineffectiveness. This review demonstrates considerable progress in developing ML technologies, including monkeypox detection, Tuberculosis, and cancer diagnosis. CNNs have shown high efficiency in diagnostics; even InceptionV3, a transfer-learning model for clinicians, can reach 99.87% diagnostics. As privacy-preserving solutions, federated learning models work to improve diagnostic accuracy without increasing the exposure of individual data, and synthetic datasets derived from high-resolution techniques such as HiP-CT help deal with data scarcity by improving model construction and assessment. The hybrids of genome and metabolome integration helped enhance diagnostic accuracy measures, particularly for complex diseases like COVID-19, due to increased prognostic performance metrics using multiple biological information. However, few issues crop up even in modern society: Generalization of the model is an issue due to a lack of data, especially for rare conditions, and increased computational power requirements for most ML models pose a problem for implementation in low-resource environments. Prominent ethical issues incorporating algorithm prejudices and the ‘black box’ concept spotlight the requisite of an explainable AI (XAI) framework to provide visibility and credence in the medical facility. Possible directions in development, such as the standardization of frameworks, enhancing computational support, and integration of different fields, provide ways to address these challenges. When tackled, these challenges create the possibility of revamping global healthcare through suitable and scalable approaches informed by ML technologies that align with the patient’s needs, leading to better practices and, consequently, better health.
Machine learning , Diagnosis , Deep learning , Privacy-enhancing AI , Synthetic data , Healthcare advancement
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