Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications.

Publication date: Aug 01, 2019

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.

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.

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.

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.

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Magrabi, F., Ammenwerth, E., McNair, J.B., De Keizer, N.F., Hypp”onen, H., Nyk”anen, P., Rigby, M., Scott, P.J., Vehko, T., Wong, Z.S., and Georgiou, A. Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications. 06551. 2019 Yearb Med Inform (28):1.

Concepts Keywords
AI Academic disciplines
Artificial Intelligence Articles
Biomarker Historical perspective healthcare
Healthcare Healthcare evaluators
Phenotype Healthcare
Surveillance Artificial intelligence
Artificial intelligence

Semantics

Type Source Name
disease MESH development
drug DRUGBANK Nonoxynol-9
disease MESH diagnosis
disease MESH pathology
disease MESH community
drug DRUGBANK Polyethylene glycol
drug DRUGBANK Pentaerythritol tetranitrate
disease MESH complications
drug DRUGBANK Cysteamine
drug DRUGBANK Trestolone
drug DRUGBANK Indoleacetic acid
drug DRUGBANK Spinosad
disease MESH satisfaction
drug DRUGBANK Coenzyme M
disease MESH privacy
drug DRUGBANK Tricyclazole
disease MESH Diabetic Retinopathy
drug DRUGBANK L-Valine
drug DRUGBANK Serine

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