Artificial intelligence in clinical decision support

challenges for evaluating AI and practical implications

Farah Magrabi, Elske Ammenwerth, Jytte Brender McNair, Nicolet F. De Keizer, Hannele Hyppönen, Pirkko Nykänen, Michael Rigby, Philip J. Scott, Tuulikki Vehko, Zoie Shui Yee Wong, Andrew Georgiou

Research output: Contribution to journalArticle

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Abstract

Objectives: This paper draws attention to: i) key considerations for evaluating artificial intelligence (AI) enabled clinical decision support; and ii) challenges and practical implications of AI design, development, selection, use, and ongoing surveillance. Method: A narrative review of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems. Results: There is a rich history and tradition of evaluating AI in healthcare. While evaluators can learn from past efforts, and build on best practice evaluation frameworks and methodologies, questions remain about how to evaluate the safety and effectiveness of AI that dynamically harness vast amounts of genomic, biomarker, phenotype, electronic record, and care delivery data from across health systems. This paper first provides a historical perspective about the evaluation of AI in healthcare. It then examines key challenges of evaluating AI-enabled clinical decision support during design, development, selection, use, and ongoing surveillance. Practical aspects of evaluating AI in healthcare, including approaches to evaluation and indicators to monitor AI are also discussed. Conclusion: Commitment to rigorous initial and ongoing evaluation will be critical to ensuring the safe and effective integration of AI in complex sociotechnical settings. Specific enhancements that are required for the new generation of AI-enabled clinical decision support will emerge through practical application.

Original languageEnglish
Pages (from-to)128-134
Number of pages7
JournalYearbook of Medical Informatics
Volume28
Issue number1
DOIs
Publication statusPublished - 1 Aug 2019

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Copyright the Publisher 2019. 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.

Keywords

  • Artificial intelligence
  • machine learning
  • clinical decision support
  • evaluation studies
  • program evaluation

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