Magrabi, F, Ammenwerth, E, McNair, JB, De Keizer, NF, Hyppönen, H, Nykänen, P, Rigby, M, Scott, PJ, Vehko, T, Wong, ZS-Y and Georgiou, A (2019) Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications. Yearbook of Medical Informatics, 28. pp. 128-134.

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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.

Item Type: Article
Additional Information: © Thieme Publishing 2019.
Uncontrolled Keywords: artificial intelligence, machine learning, clinical decision support, evaluation studies, program evaluation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Humanities and Social Sciences > School of Social Science and Public Policy
Related URLs:
Depositing User: Symplectic
Date Deposited: 20 Aug 2019 14:43
Last Modified: 20 Aug 2019 14:57
URI: http://eprints.keele.ac.uk/id/eprint/6714

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