Pragmatic considerations for fostering reproducible research in artificial intelligence.
Rickey E CarterZachi I AttiaFrancisco Lopez-JimenezPaul A FriedmanPublished in: NPJ digital medicine (2019)
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.
Keyphrases
- artificial intelligence
- big data
- deep learning
- machine learning
- electronic health record
- convolutional neural network
- healthcare
- case report
- health information
- clinical trial
- clinical decision support
- oxidative stress
- autism spectrum disorder
- optical coherence tomography
- adverse drug
- single cell
- anti inflammatory
- rna seq
- bioinformatics analysis