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.

Ähnliche Arbeiten