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Should artificial intelligence have lower acceptable error rates than humans?
Anders Lenskjold, Janus Uhd Nybing, Charlotte Trampedach
1Charlie Tango, Copenhagen, Denmark.
BJR Open
|June 30, 2023
Summary
Employees found acceptable error rates for artificial intelligence (AI) diagnostic algorithms significantly lower than for human clinicians. This highlights a need to build trust in AI through transparency and explainability in healthcare settings.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Healthcare
- Radiology
Background:
- A new knee osteoarthritis artificial intelligence (AI) algorithm was implemented at Bispebjerg-Frederiksberg University Hospital.
- An initial patient misclassification by the AI algorithm prompted an investigation into acceptable error rates.
Purpose of the Study:
- To determine the acceptable error rate for a low-risk AI diagnostic algorithm in a clinical setting.
- To explore the discrepancy between acceptable error rates for AI and human clinicians.
Main Methods:
- External validation of the AI algorithm.
- A survey conducted among Department of Radiology employees to assess acceptable error rates for AI versus human diagnoses.
Main Results:
- Employees indicated significantly lower acceptable error rates for AI (6.8%) compared to human clinicians (11.3%).
- A potential general mistrust of AI, stemming from its perceived lack of social capital and likeability, may explain this discrepancy.
Conclusions:
- Further research into the fear of unknown AI errors is crucial for enhancing AI trustworthiness in clinical practice.
- Development of benchmark tools, transparency, and explainability is essential for evaluating AI algorithm performance and ensuring patient safety.
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