Realizing the promise of machine learning in precision oncology:expert perspectives on opportunities and challenges

Abstract

Abstract The application of machine learning in precision oncology is an emerging field. To capture the status quo, challenges, opportunities, ethical implications, and future directions, we conducted semi-structured interviews with academic and clinical experts. Our participants agreed that machine learning in precision oncology is in infant stages, with clinical integration still rare. Overall, participants equated ongoing developments with better clinical workflows and improved treatment decisions for more cancer patients. They underscored the ability of machine learning to tackle the dynamic nature of cancer, break down the complexity of molecular data, and support decision-making. Our participants emphasized obstacles related to molecular data access, clinical utility, and guidelines. Availability of reliable and well-curated data to train and validate machine learning algorithms, as well as integration of multiple data sources, were described as constraints, yet necessary for future clinical integration. Frequently mentioned ethical challenges included privacy risks, equity, explainability, trust, and incidental findings, with privacy being the most polarizing. While participants recognized the issue of hype surrounding machine learning in precision oncology, they agreed that , in an assistive role, it represents the future of precision oncology.

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