Artificial Intelligence Applications in Healthcare: A State-of-the-Art Review Relevant Track: (Artificial Intelligence)

Abstract

The modern healthcare setting has been transformed by Artificial Intelligence (AI), which has allowed correct diagnosis of disease, individual treatment, and predictive analytics. This is an up-to-date review that covers the recent developments in how AI has been used in different fields of healthcare, namely the diagnosis of diseases, treatment planning, epidemiological forecasting, and the prediction of mortality. In the research, the authors conduct a systematic literature review of the reputable databases, including IEEE, Springer, Elsevier, and MDPI, including the works published between 2019 to 2025. Machine learning, deep learning, and natural language processing are considered the main AI technologies that have shown a great improvement in cancer detection, diabetes management, and the monitoring of people's health. In addition, AI-based models have improved mortality and risk prediction, which have led to more effective clinical decision-making and patient care. Despite such impressive results, there are some difficulties associated with data heterogeneity, model interpretability, ethical concerns, and clinical integration. To address these gaps, this paper highlights the developments in Explainable AI (XAI), Federated Learning, and AI-IoT integration into the smart healthcare systems from the perspective of future study. The innovations are meant to guarantee transparency, security, and scalability in real-world clinical settings. All in all, this review underscores the ongoing transformation of AI in the healthcare system, which has the potential to transform healthcare in terms of disease prevention, diagnosis, and management, and sets the stage for intelligent, data-oriented, and patient-based healthcare solutions.

Ähnliche Arbeiten