Evidence synthesis, digital scribes, and translational challenges for artificial intelligence in healthcare

Enrico Coiera*, Sidong Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
15 Downloads (Pure)


Healthcare has well-known challenges with safety, quality, and effectiveness, and many see artificial intelligence (AI) as essential to any solution. Emerging applications include the automated synthesis of best-practice research evidence including systematic reviews, which would ultimately see all clinical trial data published in a computational form for immediate synthesis. Digital scribes embed themselves in the process of care to detect, record, and summarize events and conversations for the electronic record. However, three persistent translational challenges must be addressed before AI is widely deployed. First, little effort is spent replicating AI trials, exposing patients to risks of methodological error and biases. Next, there is little reporting of patient harms from trials. Finally, AI built using machine learning may perform less effectively in different clinical settings.
Original languageEnglish
Article number100860
Pages (from-to)1-11
Number of pages11
JournalCell Reports Medicine
Issue number12
Publication statusPublished - 20 Dec 2022

Bibliographical note

Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • algorithmic transportability
  • clinical trial registries
  • deep learning
  • evidence synthesis
  • evidence-based medicine
  • machine learning
  • patient safety
  • research replication


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