Artificial Intelligence in Clinical Healthcare: A Comprehensive Analysis of Applications in Diagnosis, Treatment Optimization, and Decision Support Systems

Abstract

With the rapid advancement of artificial intelligence (AI) technologies such as deep learning and multi-modal data fusion, AI has emerged as a transformative force in modern clinical healthcare. This paper systematically explores the applications of AI in three core clinical domains: auxiliary diagnosis, clinical decision support, and workflow optimization. Through a combination of literature review, case analysis, and comparative research, we examine the technical principles and practical effects of AI-based tools—including medical imaging diagnosis (radiology, pathology), multi-modal early disease screening, personalized treatment planning, medication selection assistance, clinical pathway standardization, and patient triage scheduling. The results indicate that AI can significantly improve diagnostic accuracy (e.g., 95%+ accuracy in lung nodule detection via chest CT), enhance treatment individualization, and optimize clinical efficiency (reducing emergency triage time by 30% in typical cases). However, challenges remain, such as data quality/ privacy issues, AI model interpretability, and regulatory gaps. This study provides insights for healthcare practitioners, researchers, and policymakers to promote the safe and effective integration of AI into clinical practice, ultimately advancing the goal of precision and efficient healthcare.