Srivastava Vaibhavi, Yadav Pragya, Yadav Abhishek, Parashar Poonam
Lloyd School of Pharmacy, Plot No.-3, Knowledge Park-II, Greater Noida, Uttar Pradesh, 201306, India.
Lloyd Institute of Management and Technology, Plot No.-11, Knowledge Park-II, Greater Noida, Uttar Pradesh, 201306, India.
AAPS PharmSciTech. 2025 Dec 12;27(1):55. doi: 10.1208/s12249-025-03271-9.
Artificial intelligence is emerging as a transformative force in pharmaceutical sciences by enabling data-driven decision-making, automation, and predictive modeling. In ocular drug delivery, where therapeutic efficacy is hindered by complex anatomical and physiological barriers, AI presents significant opportunities to overcome these challenges. Its ability to optimize drug combinations, design smart delivery systems, and personalize therapies underscores its relevance in advancing ophthalmic care.
This review explores the intersection of AI and ophthalmic therapeutics, highlighting its role in formulation design, disease prediction, patient-specific treatment strategies, and smart delivery platforms, and outlines future research directions to bridge current gaps. Machine learning is advancing ocular drug delivery by optimizing nano-formulations, predicting release kinetics, and modeling pharmacokinetics. Alongside AI-powered diagnostics and integration with biosensors, contact lenses, and implants, these innovations are driving real-time monitoring and truly personalized ocular therapy and early detection and monitoring ocular diseases such as glaucoma, diabetic retinopathy, and macular degeneration. Challenges including limited clinical validation, model interpretability, data security, and regulatory complexities are highlighted. Furthermore, current gaps such as the lack of comprehensive studies on AI-assisted stimuli-responsive carriers and integration with patient-specific data are identified. Future directions emphasize explainable AI, smart biomaterials, and robust ethical-regulatory frameworks for clinical translation.
AI integration in ocular therapeutics marks a paradigm shift toward precision drug delivery and personalized care. Despite progress, challenges in explainability, regulation, and validation remain, yet innovations in AI-driven nanocarriers, smart systems, and real-time monitoring hold the potential to revolutionize ocular pharmacology overcoming limitations of conventional therapies.
人工智能正成为药物科学中的一股变革力量，它能够实现数据驱动的决策、自动化和预测建模。在眼部药物递送领域，复杂的解剖和生理屏障阻碍了治疗效果，而人工智能为克服这些挑战提供了重大机遇。它优化药物组合、设计智能递送系统以及个性化治疗的能力凸显了其在推进眼科护理方面的相关性。
本综述探讨了人工智能与眼科治疗学的交叉点，强调了其在制剂设计、疾病预测、针对患者的治疗策略以及智能递送平台中的作用，并概述了弥合当前差距的未来研究方向。机器学习通过优化纳米制剂、预测释放动力学和模拟药代动力学来推进眼部药物递送。除了人工智能驱动的诊断以及与生物传感器、隐形眼镜和植入物的整合之外，这些创新正在推动实时监测以及真正个性化的眼部治疗，并实现对青光眼、糖尿病性视网膜病变和黄斑变性等眼部疾病的早期检测和监测。文中强调了包括临床验证有限、模型可解释性、数据安全和监管复杂性等挑战。此外，还确定了当前存在的差距，例如缺乏关于人工智能辅助刺激响应载体的全面研究以及与患者特定数据的整合。未来的方向强调可解释人工智能、智能生物材料以及用于临床转化的强大伦理监管框架。
人工智能在眼科治疗中的整合标志着向精准药物递送和个性化护理的范式转变。尽管取得了进展，但在可解释性、监管和验证方面仍存在挑战，不过人工智能驱动的纳米载体、智能系统和实时监测方面的创新有可能彻底改变眼科药理学，克服传统疗法的局限性。
