Abstract IA-15: Platforms to improve reproducibility in artificial intelligence research

Abstract

Abstract As artificial intelligence (AI) becomes a method of choice to analyze biomedical data, the field is facing multiple challenges around research reproducibility and transparency. Given the proliferation of studies investigating the applications of AI in research and clinical studies, it is essential for independent researchers to be able to scrutinize and reproduce the results of a study using its materials, and build upon them in future studies. Computational reproducibility is achievable when the data can easily be shared and the required computational resources are relatively common. However, the complexity of AI algorithms and their implementation, the need for specific computer hardware and the use of sensitive biomedical data represent major obstacles in healthy-related AI research. In this talk, I will describe the various aspects of an AI biomedical study that are necessary for reproducibility and the platforms that exist for sharing these materials with the scientific community. Citation Format: Benjamin Haibe-Kains, Anthony Mammoliti, Minoru Nakano. Platforms to improve reproducibility in artificial intelligence research [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-15.

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