AI-based augmentation of oncology clinical trials

Area of Science:

• Oncology
• Clinical Trials
• Artificial Intelligence

Background:

• Oncology clinical trials face challenges like slow patient accrual, high failure rates, and limited generalizability due to biological complexity and operational inefficiencies.
• Advances in artificial intelligence (AI), powered by large-scale electronic health records and machine learning, present opportunities to address these issues throughout the clinical trial lifecycle.

Purpose of the Study:

• To review the applications of AI across the clinical trial lifecycle, from pre-trial design to post-trial inference.
• To highlight how AI can improve trial feasibility, patient engagement, and the generalizability of findings.
• To discuss challenges related to equity, data quality, transparency, and regulatory oversight in AI implementation.

Main Methods:

• Review of current AI applications in oncology clinical trials.
• Analysis of AI's role in pre-trial design, trial conduct, and post-trial generalization.
• Discussion of operational workflows, patient identification, eligibility assessment, data extraction, and trial monitoring.

Main Results:

• AI shows immediate promise in augmenting operational workflows, such as patient identification and data extraction, with current implementation in select cancer centers.
• AI applications for replacing clinical evidence generation (e.g., synthetic control arms, digital twins) are in early development stages with limited validation.
• Cross-cutting challenges include ensuring equity, data quality, transparency, and regulatory standardization.

Conclusions:

• AI can significantly enhance operational aspects of oncology trials under human oversight.
• Widespread adoption requires rigorous prospective validation, harmonized regulatory standards, and multi-stakeholder collaboration.
• AI's potential to revolutionize cancer clinical trials necessitates careful development and implementation.