Table 5.
Nine-variable multivariable generalized linear regression model for AI comfort level
| Predictor | Beta coeff. (95% CI) | p value |
|---|---|---|
| Age (continuous) | 0.0084 (− 0.0036, 0.020) | 0.1708 |
| Gender | 0.0260 | |
| Female | Reference | – |
| Male | 0.50 (0.13, 0.88) | 0.0085 |
| Other/Unknown | − 0.18 (− 1.52, 1.16) | 0.7957 |
| Education | 0.8069 | |
| Less than HS/unknown | − 0.31 (− 1.11, 0.49) | 0.4503 |
| High school/equivalent | Reference | – |
| Associate degree | − 0.24 (− 0.84, 0.36) | 0.4256 |
| Bachelor’s degree | 0.046 (− 0.45, 0.54) | 0.8564 |
| Graduate degree | − 0.12 (− 0.61, 0.38) | 0.6395 |
| Experience with AI/ML | 0.6608 | |
| Work(ed) in a field relevant/directly related to AI/ML | 0.27 (− 0.49, 1.04) | 0.4819 |
| Understand how AI/ML function | 0.38 (− 0.12, 0.88) | 0.1379 |
| Have researched terms | 0.10 (− 0.47, 0.67) | 0.7358 |
| Have heard of terms | Reference | – |
| Unknown/do not know what AI/ML mean | 0.055 (− 0.40, 0.51) | 0.8129 |
| Perceived impact of AI in orthopaedic care | < .0001 | |
| Positive | Reference | – |
| Negative | − 4.01 (− 4.75, − 3.27) | < .0001 |
| Not sure/Unknown | − 2.02 (− 2.42, − 1.62) | < .0001 |
| Perceived impact of AI on healthcare costs | 0.2275 | |
| Increase | Reference | – |
| Decrease | 0.37 (− 0.12, 0.86) | 0.1367 |
| Not sure/unknown | 0.28 (− 0.11, 0.67) | 0.1623 |
| Would refuse AI if increased healthcare costs | 0.0002 | |
| Yes | Reference | – |
| No | 1.02 (0.53, 1.52) | < .0001 |
| Not sure/unknown | 0.37 (− 0.061, 0.79) | 0.0930 |
| Acceptable for doctor to sell health data to third party for building intelligent computers for healthcare | 0.2045 | |
| Yes | 0.40 (− 0.047, 0.84) | 0.0793 |
| No | Reference | – |
| Not sure/unknown | 0.20 (− 0.22, 0.61) | 0.3515 |
| Survey format | 0.0001 | |
| REDCap | Reference | – |
| Tablet | − 1.52 (− 2.22, − 0.82) | < .0001 |
| Paper | − 0.053 (− 0.86, 0.76) | 0.8968 |