Integrating Artificial Intelligence and Machine Learning Into Cancer Clinical Trials

Area of Science:

• Oncology
• Medical Informatics
• Artificial Intelligence

Background:

• Oncology practice involves complex decision-making based on extensive patient data.
• Machine learning (ML) and artificial intelligence (AI) offer significant potential to enhance data analysis in cancer care.
• AI has rapidly progressed from a promising concept to application in prospective clinical trials within the last five years.

Purpose of the Study:

• To review recent applications of AI in oncology clinical trials.
• To discuss the interpretation of AI models compared to traditional statistical models.
• To explore future opportunities for AI, particularly unsupervised and generative models, in oncology.

Main Methods:

• Review of recent literature on AI applications in oncology clinical trials.
• Analysis of AI's role in predicting actionable outcomes.
• Discussion on conceptualizing and interpreting AI models in clinical practice.

Main Results:

• AI models have demonstrated success in predicting outcomes like acute care visits, short-term mortality, and extranodal extension.
• Recent AI efforts in clinical trials show a move towards improved prediction of actionable results.
• AI models pose different questions and require different interpretation frameworks than traditional statistical methods.

Conclusions:

• AI is increasingly vital in oncology, enhancing predictive capabilities for patient outcomes.
• Understanding how to interpret AI models is crucial for their effective clinical implementation.
• Future AI in oncology should leverage unsupervised and generative models for data-driven insights rather than predefined functions.