(untitled) (应用人工智能研究, 2025)
This review systematically evaluates the current status, application scenarios, and implementation effectiveness of artificial intelligence (AI) technologies in pharmaceutical formulation. It analyzes existing challenges and future directions to provide a technical reference for rational formulation development. Methods: We retrieved literature published between 2020 and 2025 from core databases including PubMed, MEDLINE, Embase, and Cochrane Library, focusing on high-quality studies applying AI to formulation design, optimization, production, and quality control. A systematic review methodology was employed to summarize technical approaches, performance metrics, and comparative outcomes between AI and traditional methods across various formulation types. Results: Analysis of 67 qualified studies revealed significant advantages of AI technologies across multiple formulation development stages. In drug delivery system design, AI-assisted nanocarrier development reduced development time by 60%-70% on average. For formulation optimization, AI models achieved 85%-93% accuracy in predicting critical quality attributes, representing a 25%-40% improvement over traditional empirical methods. In manufacturing processes, AI control strategies improved product batch consistency by 30%-50%. Furthermore, AI-driven adaptive learning frameworks demonstrated capability for continuous formulation and process optimization through real-time data integration. Conclusion: AI is fundamentally transforming pharmaceutical formulation development paradigms through data-driven approaches that significantly enhance efficiency, reduce costs, and improve product quality. Despite challenges in data quality, model interpretability, and industrial implementation, ongoing algorithm innovations and multidisciplinary integration are expected to advance pharmaceutical formulation into a new era of precision, personalization, and intelligence.