Artificial Intelligence in Medicine: Survey Insights, Practical Implementation, and Future Prospects
Abstract: Artificial Intelligence (AI) is increasingly transforming healthcare through data-driven diagnostics, predictive analytics, and personalized treatment strategies. This study investigates current applications of AI in medicine by integrating a multi-dimensional approach: an extensive literature review, a survey analysis of $N=500$ participants (comprising healthcare professionals and the general public), and a prototype implementation of an AI-assisted monitoring system. The methodology combines qualitative survey evaluation with a system-level design involving sensor-based data acquisition and machine learning-based prediction using a Random Forest architecture. Results indicate a growing acceptance of AI-with a significant majority of professionals supporting adoption?alongside persistent concerns regarding algorithmic reliability, bioethics, and data privacy [1, 2]. The prototype system demonstrates the feasibility of multi-parameter analysis for supporting clinical decision-making, achieving a 92% classification accuracy, although challenges related to signal noise and system calibration remain [3, 4]. The findings suggest that AI can significantly enhance diagnostic efficiency and healthcare delivery provided there is robust validation and high-quality data integration.
Keywords: Artificial Intelligence, Healthcare, Machine Learning, Medical Diagnostics, Predictive Medicine, Survey Analysis, Embedded Systems
How to Cite?: Samriddhi Yadav, "Artificial Intelligence in Medicine: Survey Insights, Practical Implementation, and Future Prospects", Volume 15 Issue 4, April 2026, International Journal of Science and Research (IJSR), Pages: 160-163, https://www.ijsr.net/getabstract.php?paperid=SR26330155555, DOI: https://dx.doi.org/10.21275/SR26330155555