Combining Human Expertise with Artificial Intelligence: Experimental Evidence from Radiology
Open accessAbstract
Full automation using Artificial Intelligence (AI) predictions may not be optimal if humans have information not available to the AI (contextual information). We study human-AI collaboration using an information experiment with professional radiologists. Results show that providing (i) AI predictions does not improve performance on average, whereas (ii) contextual information does. Radiologists do not realize the gains from AI assistance because of errors in belief updating – they underweight AI predictions and treat their own information and AI predictions as statistically independent. Unless these mistakes can be corrected, the optimal human-AI collaboration design delegates cases either to humans or to AI, but rarely to AI assisted humans.
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Doctors did not understand how to use the AI tool effectively; when AI offered confident predictions, doctors frequently overrode those predictions with their own, and when AI offered uncertain predictions, doctors frequently overrode their own better predictions with those supplied by the machine.
“That's because doctors did not understand how to use the AI tool effectively; when AI offered confident predictions, doctors frequently overrode those predictions with their own.When AI offered uncertain predictions, doctors frequently overrode their own better predictions with those supplied by the machine.”
From Applying AI to Rebuild Middle Class Jobs (2024) · cites this paper as Agarwal et al., 2023
Agarwal et al. found that AI did not improve the quality of radiologists' diagnoses even though AI's predictions were at least as accurate as nearly two-thirds of the doctors studied.
“In an experiment that randomly allocated the availability of AI assistance to professional radiologists, Nikhil Agarwal of MIT and his coauthors have found that AI did not improve the quality of radiologists' diagnoseseven though AI's predictions were at least as accurate as nearly two-thirds of the doctors studied (Agarwal et al., 2023).”
From Applying AI to Rebuild Middle Class Jobs (2024) · cites this paper as Agarwal et al., 2023
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- Authors
- Agarwal, N., Moehring, A., Rajpurkar, P., & Salz, T.
- Affiliations
- National Bureau of Economic Research (US)
- Corresponding author
- Agarwal, Nikhil
- Venue
- National Bureau of Economic Research
- Publisher
- National Bureau of Economic Research
- Published
- 2023-07
- OpenAlex
- W4383744587
- Cited by
- 161
- References
- 17
- Citations by year
- 2026: 43 · 2025: 66 · 2024: 37 · 2023: 15
- Type
- report
- Language
- en
- Open access
- green
- doi.org
- Locations
- National Bureau of Economic Research (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Alfred P. Sloan Foundation (2022-17182)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- human-AI collaboration, radiologists, AI-assisted diagnosis, contextual information, belief updating, Bayesian updating, diagnostic accuracy, radiology, clinical decision-making
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