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

Farah Magrabi¹, Elske Ammenwerth², Jytte Brender McNair³

¹Macquarie University, Australian Institute of Health Innovation, Sydney, Australia.

Yearbook of Medical Informatics

|April 26, 2019

Summary

This summary is machine-generated.

Evaluating artificial intelligence (AI) in healthcare requires careful consideration of its design, use, and ongoing surveillance. Rigorous evaluation is crucial for the safe and effective integration of AI clinical decision support systems.

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Area of Science:

Health Informatics
Clinical Decision Support Systems
Artificial Intelligence in Healthcare

Background:

Existing frameworks for evaluating artificial intelligence (AI) in healthcare provide a foundation for current needs.
The dynamic nature of AI, utilizing vast health data, presents unique evaluation challenges.
Expert perspectives from international informatics organizations inform the discussion.

Purpose of the Study:

To highlight key considerations for evaluating AI-enabled clinical decision support.
To examine the challenges and practical implications of AI in healthcare.
To discuss the lifecycle of AI, from design to ongoing surveillance.

Main Methods:

A narrative review of existing research and evaluation methodologies.
Incorporation of expert insights from the International Medical Informatics Association (IMIA) and European Federation for Medical Informatics (EFMI).

Main Results:

Historical context of AI evaluation in healthcare is presented.
Challenges in evaluating AI-enabled clinical decision support across its lifecycle (design, development, selection, use, surveillance) are detailed.
Practical approaches and monitoring indicators for AI in healthcare are discussed.

Conclusions:

Consistent and thorough evaluation is essential for the safe and effective implementation of AI in healthcare.
The integration of AI into complex sociotechnical systems necessitates robust evaluation.
Practical application will drive necessary enhancements for next-generation AI clinical decision support.