# Review of Polymer Drug Therapy for Cancer Driven by Artificial Intelligence

## TL;DR

This paper reviews how AI is transforming polymer-based cancer treatments by improving drug delivery and modeling.

## Contribution

It introduces an integrated framework combining AI with polymer science for precision oncology.

## Key findings

- AI algorithms enable data-driven optimization of polymer drug carriers for precise drug release.
- The review identifies challenges like 'small data' and regulatory issues in AI-driven cancer therapy.
- A roadmap is proposed to shift focus toward interpretability and in vivo validation in AI-guided oncology.

## Abstract

This review systematically evaluates the interdisciplinary convergence of artificial intelligence (AI) and polymer science in cancer therapy. Beyond mere description, we provide an integrated framework spanning synthetic optimization, biocompatibility prediction, and the design of tumor microenvironment (TME)-responsive carriers. We highlight how AI algorithms (ML, DL, and RNNs) transform traditional trial-and-error methods into a data-driven paradigm, enabling precise spatiotemporal drug release and individualized pharmacokinetic modeling. Crucially, this work addresses the critical gap between computational modeling and clinical realization by providing a balanced critical analysis of current bottlenecks, including the “small data” challenge, publication bias, and regulatory hurdles. We conclude with a roadmap for AI-guided precision oncology, shifting the focus from predictive accuracy to mechanistic interpretability and prospective in vivo validation.