Generative AI - Assisted Adaptive Cancer Therapy

Area of Science:

• Oncology
• Control Theory
• Artificial Intelligence

Background:

• Adaptive combination therapy aims to overcome cancer resistance by dynamically adjusting treatments based on cancer evolution.
• Current models require improved accuracy, reliability, and clinical feasibility, especially with new therapeutics and multimodal data.

Purpose of the Study:

• To review the potential of Generative AI (GenAI) in enhancing adaptive cancer therapy.
• To explore how GenAI can predict treatment response and recommend therapies within a nonlinear control framework.

Main Methods:

• Integration of nonlinear system identification, control theory, and deep learning.
• Application of these methods within an adaptive cancer control framework to utilize expanding cancer data.
• Leveraging GenAI for learning and inferencing capabilities.

Main Results:

• A framework for GenAI-enhanced adaptive cancer therapy is proposed.
• The analysis provides conceptual clarity on research and translational pathways for GenAI in oncology.
• Identifies critical challenges including clinical data access, deep learning interpretability, and validation.

Conclusions:

• GenAI offers a promising approach to improve adaptive cancer therapy by enhancing treatment response prediction and recommendation.
• Addressing challenges in data access, model transparency, and clinical validation is crucial for integrating GenAI into oncology.
• Further research is needed to realize the full potential of GenAI-assisted cancer treatments in clinical practice.