Current Opportunities for the Integration and Use of Artificial Intelligence and Machine Learning in Clinical Trials: Good Clinical Practice Perspectives

The integration of artificial intelligence and machine learning (AI/ML) in clinical trials offers immense potential to reshape drug development and research efficiency. This review explores the multifaceted landscape of AI/ML applications in clinical trials including seven use cases of AI/ML that aim at improving data quality and enhancing patient outcomes and clinical trial successes. We discuss good machine learning practices focusing on clear scope definition, transparent risk-proportionate management, and robust feature engineering. We also highlight privacy-preserving data-sharing techniques like federated learning and the role and the risk of using large language models in patient recruitment, data capture, clinical decision support, patient engagement, and trial design optimization. This review highlights the potential of aiding clinical trials through the responsible use of AI/ML, while recognizing the challenges (e.g., generalizability, transparency, and robustness) as well as ethical considerations including patient safety, privacy, and human rights. It also serves to guide interested parties towards the responsible and effective integration of these technologies with clinical trials.