Application of Artificial Intelligence Enabled Targeted Clinical Diagnosis and Treatment

Diagnosis and treatment, the core pillars of the clinical healthcare system, remain critical to safeguarding patient health. The long-accumulated clinical expertise in these areas demonstrates significant value in preventing and treating major diseases. Traditional clinical diagnosis and treatment practices face several core challenges, including inefficient data processing, inadequate standardization of diagnosis and treatment aligned with standardized intelligent diagnosis and treatment models, and insufficient personalized adaptation, which are increasingly exposed by the pressing need for precise, efficient, and personalized medicine. With its targeted breakthroughs and mature applications in medical data processing and intelligent decision-making modeling, AI technology has emerged as the only viable and indispensable core technological solution to overcome these challenges. This paper examines how AI tackles these issues by focusing on the core challenges of traditional clinical diagnosis and treatment systems, including data processing efficiency, standardization of diagnosis and treatment, and personalized adaptation capabilities. It systematically expounds how artificial intelligence (AI) technologies—including federated learning architectures, multimodal data processing technology (for integrating multi-source heterogeneous medical data), and personalized modeling technologies—can specifically address these challenges and create clinical value. Meanwhile, it analyzes the practical challenges faced by AI in clinical applications, such as the balance between data privacy and quality, insufficient interpretability, ambiguous definition of ethical responsibilities, and commercialization difficulties. Beyond these hurdles, this paper concludes by outlining future development pathways, including advancing multimodal data processing technology, building trustworthy systems, promoting ecological collaboration, and expanding universal access. It ultimately demonstrates how AI can drive high-quality healthcare development (laying the groundwork for digital therapeutics via personalized interventions) and provides a roadmap for medical system upgrades.