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Machine Learning and Artificial Intelligence in Nanomedicine.

Wei-Chun Chou^1,2, Alexa Canchola¹, Fan Zhang³

¹Department of Environmental Sciences, University of California, Riverside, California, USA.

Wiley Interdisciplinary Reviews. Nanomedicine and Nanobiotechnology

|August 14, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) accelerate nanomedicine development by optimizing nanoparticle design and predicting efficacy. Challenges remain in data standardization and regulatory frameworks for clinical integration.

Keywords:

artificial intelligence drug delivery machine learning nanomedicine pharmacokinetics

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Area of Science:

Nanomedicine
Artificial Intelligence
Machine Learning

Background:

Nanomedicine utilizes nanoparticles for targeted delivery in various diseases.
Clinical translation of nanomedicine faces challenges in optimization and predicting human efficacy.
Artificial intelligence (AI) and machine learning (ML) offer solutions to development hurdles.

Purpose of the Study:

To review the application of AI and ML in nanomedicine development.
To highlight successes and persistent obstacles in AI-driven nanomedicine.
To propose a path for efficient clinical integration of nanomedicine.

Main Methods:

AI/ML models for screening, formulation rationalization, and biodistribution prediction.
High-throughput data collection techniques (DNA barcoding, automated liquid handling).
Modeling of protein corona formation and its impact on nanoparticle behavior.

Main Results:

AI accelerates discovery and optimizes nanoparticle design, reducing trial-and-error.
AI improves prediction of biodistribution and protein corona effects.
AI aids in understanding nanoparticle immunogenicity and cellular uptake.

Conclusions:

AI and ML are transformative tools in nanomedicine, enhancing design and preclinical prediction.
Data standardization, model generalizability, and regulatory clarity are crucial for clinical translation.
Harmonized data, validation, and guidelines are needed for AI-driven nanomedicine integration.