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The following is a summary of “AI-Driven Innovations in Smart Multifunctional Nanocarriers for Drug and Gene Delivery: A Mini-Review,” published in the March 2025 issue of Critical Reviews in Oncology/Hematology by Noury et al.

The integration of artificial intelligence (AI) with nanomedicine has revolutionized the development of smart multifunctional nanocarriers (SMNs) for drug and gene delivery, enabling unprecedented precision, efficiency, and personalization in therapeutic applications. AI-driven methodologies play a pivotal role in accelerating the design of these nanocarriers by optimizing drug loading capacities, fine-tuning release kinetics, improving biocompatibility, and predicting interactions with biological barriers. This review provides a comprehensive analysis of AI’s transformative impact on the fabrication and functionalization of SMNs, particularly in overcoming critical challenges associated with targeted drug delivery, controlled release mechanisms, and theranostic applications. 

The discussion encompasses AI-enhanced approaches in the design of advanced nanomaterials, including polymeric, lipidic, and inorganic nanoparticles, with a specific focus on their implications in oncology and hematology. Furthermore, researchers examine recent preclinical and clinical case studies that highlight AI-assisted nanocarrier development for personalized medicine, demonstrating its potential to refine therapeutic strategies based on patient-specific data. By leveraging AI algorithms, researchers can predict the pharmacokinetics and biodistribution of nanocarriers, enabling more precise dosing and minimizing off-target effects. This synergy between AI and nanotechnology lays the foundation for next-generation precision therapeutics, addressing fundamental limitations in conventional drug delivery systems. However, significant challenges remain, including the standardization of training datasets, regulatory compliance for AI-assisted drug development, and the translational scalability of AI-designed nanocarriers for clinical use. 

The successful implementation of AI in nanomedicine requires interdisciplinary collaboration among computational scientists, bioengineers, and clinicians to bridge the gap between theoretical advancements and real-world applications. This review underscores the immense potential of AI in advancing SMN technologies, ultimately paving the way for safer, more effective, and highly individualized treatment strategies in modern healthcare.

Source: sciencedirect.com/science/article/abs/pii/S1040842825000897