Artificial Intelligence (AI)-Powered Intelligent Systems for Disease Prognosis: A Bibliometric Study

Today, we face enormous challenges in predicting various life-threatening diseases such as infectious diseases, cancer, neurodegenerative diseases, osteoarthritis, and many other non-communicable diseases. Predicting diseases using unconventional architectures that can predict patterns and medical images about the immediacy of diseases that cannot be traced by manual inspection is possible with artificial intelligence. Intelligent systems powered by AI can transform from disease management to precision medicine and intersect with another larger trend that is redefining healthcare—personalized, precision, preventive, and predictive care by capturing biological, clinical, and behavioral data that refines early-stage disease detection and predicts the likely course of disease so that the patient receives care at the right time to achieve maximum health. To address the main objective: (i) Early warning and intervention, assessment of recovery and outcomes, and evaluation of prognosis, which may ultimately lead to longer life expectancy. To this end, a bibliometric analysis of 3024 journal articles listed in the Web of Science (WoS) was performed. This revealed a significant increase in research activity and also the broad, multidisciplinary nature of the research. However, there is still room for further collaboration between disciplines as well as exploration of how different social groups and institutions contribute to social cohesion. The expected outcome for researchers is the realization that AI has the potential to drive current health practices toward a more individualized and precision-based approach in the coming years, and that this can serve as the foundation for other AI-driven technological innovations.