Adaptive Treatment of Metastatic Prostate Cancer Using Generative Artificial Intelligence

Area of Science:

• Oncology
• Artificial Intelligence
• Computational Biology

Background:

• Therapeutic resistance, recurrence, and metastasis remain significant challenges in cancer treatment.
• Generative Artificial Intelligence (GenAI) presents emerging capabilities with potential applications in advancing cancer therapies.

Purpose of the Study:

• To present a hypothetical case study on applying Generative Pre-trained Transformers (GPTs) to metastatic prostate cancer (mPC) treatment.
• To explore the design of GPT-supported adaptive intermittent therapy for mPC.

Main Methods:

• Hypothetical case study design for mPC treatment.
• Exploration of GPT configuration, training, and inferencing for adaptive therapy.
• Consideration of risk mitigation for GenAI hallucination in clinical settings.

Main Results:

• Outlined a framework for GPT-supported adaptive intermittent therapy for mPC.
• Addressed key considerations for integrating GenAI into clinical cancer treatment.
• Identified pathways for GenAI-assisted adaptive treatment strategies.

Conclusions:

• GenAI, specifically GPTs, holds promise for developing adaptive treatment strategies in metastatic prostate cancer.
• This case study provides a foundation for designing clinical trials of GenAI-supported cancer therapies.
• Mitigating GenAI-specific risks like hallucination is crucial for clinical integration.