AI-Powered Predictive Modeling for Controlled Release Drug Delivery Systems in Cancer Therapy - Rahul Jha, Ruhi Saxena (Journal of Neonatal Surgery, 2025)
Background: The research evaluates how AI-powered predictive modeling can be used in cancer therapy drug delivery systems with controlled release parameters. A comprehensive evaluation of user opinions and system reliability combined with implications on cancer therapy enables researchers to understand the positive aspects and drawbacks of AI-based clinical drug delivery. Methods: The researchers collected data from participants who rated their feedback using a Likert scale which showed the essential elements affecting AI model functioning. The model receives positive user feedback because users find it accurate and easy to use with quick outcome generation but some users doubt its value for complex cancer situations. Results: Participants responded positively about the model reliability although inconsistent ratings demonstrate that additional validation testing and improvement of the model are required. Study participants believe AI holds significant potential to boost treatment effectiveness while tailoring therapies to individual patients although more research must happen to yield its complete advantages. Conclusion: The adoption of this AI system faces three main obstacles because extensive research data needs to be accessible while system integration requires simplification and the model must prove effective with different patient types and therapy plans. The survey participants expressed enthusiastic support for AI development in cancer therapy because they predicted positive outcomes in the long run and believed research on AI models for clinical implementation should continue. This evidence shows that AI can revolutionize cancer therapy yet current obstacles must be solved to fulfill its full potential.