Artificial Intelligence for Medical Diagnostics in IoT-Based Healthcare Networks: Foundations and Future Trends

Abstract

AI is quickly transforming the landscape of medical diagnostics, leading to remarkable gains in accuracy, speed, and availability. We perform a systematic review on the fundamental strategies, tools, applications, and challenges of AI enabled diagnostic in medicine with focus on medical diagnostics in IoT-based healthcare networks. The paper presents the use of machine learning algorithms, deep learning models, including CNN, and NLP to interpret clinical documentation. It also investigates the usage of smart computing infrastructures such as edge systems and the Internet of Medical Things (IoMT) that facilitates real-time, data-driven clinical decision-making consistently matching or exceeding human-level perception, notably in medical imaging, pathology and biosignal analysis. Nevertheless, there exist great challenges, such as data heterogeneity, lack of high-quality labeled datasets, model interpretability and ethics, such as algorithmic bias and patient privacy. In addition, questions on standards, clinical confidence, and regulation are often considered less important than technical performance but are central to the effective deployment of AI within health systems. This paper presents the comparison of AI-combated diagnostic approaches with the traditional ones, a recent literature review and some research gaps for future work. The study seeks to underpin the need to advance AI systems that can be understood, are clinically applicable and ethically justifiable. It promotes interdisciplinary cooperation and uniformed evaluation methodologies for the safe, efficacious and equitable utilization of AI for healthcare diagnostics.