Pragmatic considerations for fostering reproducible research in artificial intelligence

Research output: Contribution to journalReview articlepeer-review


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.

Original languageEnglish (US)
Article number42
Journalnpj Digital Medicine
Issue number1
StatePublished - Dec 1 2019

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Health Informatics
  • Computer Science Applications
  • Health Information Management


Dive into the research topics of 'Pragmatic considerations for fostering reproducible research in artificial intelligence'. Together they form a unique fingerprint.

Cite this