Would you like a side of AI with your mammogram? An experimental study of patient willingness to pay for AI.
Authors
Affiliations (5)
Affiliations (5)
- Brown University Health, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Department of Radiology, Brown Radiology Human Factors Lab, Providence, RI.
- Seton Hall University School of Law, Providence, RI, Newark, NJ.
- Duke School of Medicine, Duke University Medical Center, Durham, NC.
- Department of Radiology, University of Washington, Seattle, Washington.
- Department of Radiology, Duke University Medical Center, Durham, NC. Electronic address: [email protected].
Abstract
Some radiology practices ask patients to pay out of pocket for supplemental artificial intelligence (AI) interpretations of screening mammograms. To determine if different price points and information conditions influence patient willingness to pay for AI. Women aged 40+ with a prior mammogram were recruited from an online research platform and asked if they would pay for supplemental AI interpretations after being randomized to different price points ($50, $200, $500) and information conditions (no AI information, two AI accuracy rates, two AI error rates) versus a no AI condition. If respondents declined, they were asked why (cannot afford it vs. don't think it is worth it). Statistical analysis assessed permutations of the price points and information conditions. There were 2,534 respondents (median age, 53 years (IQR: 46-61). Among information conditions, respondents were most likely to pay for AI when shown an advertisement (26.5%) or good AI accuracy rates (25.3%) and least likely to pay when shown good (14.2%) or poor (7%) AI error rates (p<0.0001). Among price conditions, respondents were more likely to pay for less expensive AI (p<0.0001): 24.4% at $50, 17.1% at $200, and 13.4% at $500. Reasons for declining AI use varied by information condition and price point (p<.0001). A women's willingness to pay for AI to interpret her mammogram varies according to price and the type of AI information presented. Radiology practices may wish to consider these findings when presenting patients with an option to pay for AI interpretation.