IMPLEMENTING AI IN HEALTHCARE: A PRAGMATIC FRAMEWORK FOR EVALUATION AND INTEGRATION

Abstract

The rapid proliferation of artificial intelligence (AI) and machine learning applications in healthcare settings has generated significant enthusiasm about their potential to transform patient care. However, the gap between theoretical benefits and demonstrated real-world improvements remains substantial. This article presents a comprehensive framework for healthcare organizations considering AI implementation, addressing critical aspects from initial evaluation through full-scale deployment. The article examines key considerations in the build-versus-buy decision matrix, infrastructure requirements, workflow integration, and long-term sustainability planning. Through analysis of existing implementation challenges and organizational requirements, the article provides structured guidance for healthcare leaders and practitioners to evaluate, select, and implement AI solutions effectively. The framework emphasizes the importance of systematic evaluation processes, stakeholder engagement, and measurable outcome assessment. Additionally, the article explores emerging trends and future directions in healthcare AI, including federated learning approaches, edge computing applications, and the convergence of AI with other emerging technologies such as IoMT and quantum computing. This article contributes to the growing body of knowledge on healthcare AI implementation by offering practical, evidence-based recommendations that balance technological innovation with clinical efficacy and organizational sustainability, while also providing insights into future technological trajectories that will shape the healthcare AI landscape.