Artificial intelligence (AI): using ai to assist in patient assessment, diagnostics, and clinical decision-making

Abstract

Artificial Intelligence (AI) has emerged as a transformative technology in modern healthcare, particularly in the areas of patient assessment, diagnostic accuracy, and clinical decision-making. Rapid advancements in machine learning, deep learning, natural language processing, and big data analytics have enabled healthcare professionals to analyze large volumes of clinical data and generate insights that support evidence-based medical practice. AI-driven systems can evaluate patient symptoms, medical histories, imaging findings, laboratory values, and physiological parameters to assist clinicians in identifying diseases earlier and more accurately. These technologies are increasingly integrated into electronic health records, diagnostic imaging platforms, wearable health devices, and hospital information systems. In nursing and medical practice, AI applications help improve clinical efficiency, reduce human error, enhance patient safety, and support personalized healthcare interventions. Nurses and physicians benefit from AI-assisted clinical decision support systems that recommend treatment plans, predict complications, and guide patient management strategies. The integration of AI also supports remote monitoring, telehealth services, and predictive analytics, which are particularly valuable in managing chronic diseases and improving healthcare accessibility. Despite its numerous advantages, AI adoption in healthcare presents ethical, legal, and technological challenges including data privacy concerns, algorithm bias, and the need for appropriate training among healthcare professionals. This article provides a comprehensive overview of the role of AI in patient assessment, diagnostics, and clinical decision-making by synthesizing evidence from recent literature published within the last decade. The discussion highlights the potential of AI to revolutionize healthcare delivery while emphasizing the importance of human oversight, ethical governance, and interdisciplinary collaboration in implementing AI-based healthcare systems

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