Arthroplasty today · 2026
Are Arthroplasty Patients Ready for Artificial Intelligence-Integrated Care?
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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Brutti Jonathan, Wong Andres, Garces Ivan, Grand Zachary, Lawrie Charles M
Аннотация
BACKGROUND: Total hip arthroplasty and total knee arthroplasty patients generate high volumes of perioperative communication, yet the capacity for timely, personalized responses is constrained by growing surgical volumes. Generative artificial intelligence (AI)-powered digital medical assistants (DMAs) may help address this gap, but patient perceptions and willingness to use them remain poorly understood. This study evaluated perceptions and preferences regarding a generative AI-powered DMA among total hip arthroplasty and total knee arthroplasty patients.
METHODS: A prospective cross-sectional survey was administered to 101 arthroplasty patients at a single institution. The 13-item questionnaire assessed prior generative AI experience, healthcare priorities, attitudes toward DMA usefulness and trustworthiness (Likert scale), and scenario-based preferences for addressing common postoperative concerns. Composite DMA attitude scores and subgroup differences were analyzed using nonparametric methods and logistic regression.
RESULTS: Patients prioritized accuracy of health information and provider relationships over rapid assistance. Willingness to use the DMA (44.5% agree/strongly agree) and trust in its accuracy (44.5%) were mixed, while preference for DMA over human staff was low (9.1% agree/strongly agree). Receptivity was highest for supportive functions such as reminders and encouragement (54.6%). Across postoperative scenarios, calling the office was the dominant choice (56.6%-72.7%). Prior generative AI experience was the strongest predictor of more favorable DMA attitudes. Older age was associated with a lower likelihood of choosing the DMA for pain and medication concerns.
CONCLUSIONS: Arthroplasty patients showed mixed but generally neutral-to-slightly positive attitudes toward a generative AI-powered DMA, with a consistent preference for human-mediated communication.
METHODS: A prospective cross-sectional survey was administered to 101 arthroplasty patients at a single institution. The 13-item questionnaire assessed prior generative AI experience, healthcare priorities, attitudes toward DMA usefulness and trustworthiness (Likert scale), and scenario-based preferences for addressing common postoperative concerns. Composite DMA attitude scores and subgroup differences were analyzed using nonparametric methods and logistic regression.
RESULTS: Patients prioritized accuracy of health information and provider relationships over rapid assistance. Willingness to use the DMA (44.5% agree/strongly agree) and trust in its accuracy (44.5%) were mixed, while preference for DMA over human staff was low (9.1% agree/strongly agree). Receptivity was highest for supportive functions such as reminders and encouragement (54.6%). Across postoperative scenarios, calling the office was the dominant choice (56.6%-72.7%). Prior generative AI experience was the strongest predictor of more favorable DMA attitudes. Older age was associated with a lower likelihood of choosing the DMA for pain and medication concerns.
CONCLUSIONS: Arthroplasty patients showed mixed but generally neutral-to-slightly positive attitudes toward a generative AI-powered DMA, with a consistent preference for human-mediated communication.