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Article Type

Review

Abstract

Artificial Intelligence (AI) is becoming the cornerstone of the future of healthcare diagnostics, that has to ability to change the healthcare diagnostic landscape in terms of diagnostic accuracy, speed, and availability. This systematic review investigates the basic methods, tools, applications, and challenges involved in the integration of AI in diagnostic medicine. It emphasizes the using of machine learning models, deep learning networks (e.g., CNNs), NLP for clinical documentation, and smart computing infrastructures, such as edge device and IoMT. They are making possible real-time, data-driven decision making that is already at human-expert-level performance or, in some cases, even better (in the analysis of medical images, pathology and biosignal, for example). Despite these breakthroughs, a number of significant challenges remain, such as data diversity and heterogeneity, lack of high-quality labeled data, model interpretability and ethical issues (e.g. algorithmic bias and patient privacy). In addition, there are strong compatibility, clinician reliance, regulation validation (these are the most commonly neglected part of technical development) factors, which will keep AI supported systems tightly engrafted to the existing health systems. This paper further attempts to compare AI based and conventional diagnostic methods, presents recent literature insights and scopes the research gaps in the ongoing research endeavors. The aim of the survey is, within the broader picture of the underpinning principles of the ‘fitness for purpose’ of predictive models, to capture these foundations fully in order to assist future developments that are driven by a desire for transparency, fairness, and clinical relevance. It emphasizes the necessity of interdisciplinary cooperation and of standard assessment methods to achieve a safe and effective application. As AI transforms diagnostics, the future of healthcare will rely on creating AI systems that are inclusive, interpretable and ethically grounded, and that can tackle global health problems and respond flexibly to the demands of clinical practice.

Keywords

Artificial Intelligence, Healthcare diagnostics, Machine learning, Deep learning, Medical imaging, Clinical decision support, Ethical challenges

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