ReviewAAPS PharmSciTech2025
Artificial Intelligence in Ocular Drug Delivery: Precision Drug Delivery's New Horizon.
Review in AAPS PharmSciTech, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial 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. AREA COVERED: 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. EXPERT OPINION: 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.
Indexed as
Identifiers
41388211What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.