Pragmatic considerations for fostering reproducible research in artificial intelligence.
Where this comes from
- Record sourced from PubMed, PMID 31304388.
- Also identified by DOI 10.1038/s41746-019-0120-2 and PMC identifier 6550149.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Artificial intelligence and deep learning methods hold great promise in the medical sciences in areas such as enhanced tumor identification from radiographic images, and natural language processing to extract complex information from electronic health records. Scientific review of AI algorithms has involved reproducibility, in which investigators share protocols, raw data, and programming codes. Within the realm of medicine, reproducibility introduces important challenges, including risk to patient privacy, challenges in reproducing results, and questions regarding ownership and financial value of large medical datasets. Scientific review, however, mandates some form of resolution of these inherent conflicts. We propose several approaches to permit scientific review while maintaining patient privacy and data confidentiality.