Samenvatting
Background: Artificial Intelligence (AI) has the potential to revolutionize healthcare and enhance patient outcomes. Addressing the user's concerns towards AI is crucial for its appropriate development and implementation. Such as trustworthiness of AI, data privacy or accuracy of AI results. This study examines to what extent concerns and additional factors are associated with accepting AI in healthcare applications.
Methods: A cross-sectional online survey study involving 1,250 participants from the Netherlands will be conducted in April 2024 using Flycatcher survey sampling company. Participants will be randomly assigned to different real life AI applications within healthcare. Their general attitudes towards AI will be measured along with their personality traits and AI literacy, followed by a description of the scenarios. Afterwards they will express their levels of concern towards one of the AI applications and their acceptance towards it. The Unified Theory of Acceptance of Technology will be used to measure the acceptance towards AI. The AI applications involve newborn screening, preventive interventions through wearables, chatbots supporting doctor-patient communication, and augmentation of data for AI training in imaging.
Findings: Regression analysis will be used to examine which concerns and additional factors are associated with acceptance within each AI application. In addition, we will perform moderator analysis to examine whether factors associated with acceptance differ between AI applications.
Conclusion: This research will contribute to human-centered AI development by identifying concerns and factors that influence acceptance to consider when implementing AI tools in healthcare. This can increase the likelihood of adoption of AI in healthcare.
Work-in-progress
Methods: A cross-sectional online survey study involving 1,250 participants from the Netherlands will be conducted in April 2024 using Flycatcher survey sampling company. Participants will be randomly assigned to different real life AI applications within healthcare. Their general attitudes towards AI will be measured along with their personality traits and AI literacy, followed by a description of the scenarios. Afterwards they will express their levels of concern towards one of the AI applications and their acceptance towards it. The Unified Theory of Acceptance of Technology will be used to measure the acceptance towards AI. The AI applications involve newborn screening, preventive interventions through wearables, chatbots supporting doctor-patient communication, and augmentation of data for AI training in imaging.
Findings: Regression analysis will be used to examine which concerns and additional factors are associated with acceptance within each AI application. In addition, we will perform moderator analysis to examine whether factors associated with acceptance differ between AI applications.
Conclusion: This research will contribute to human-centered AI development by identifying concerns and factors that influence acceptance to consider when implementing AI tools in healthcare. This can increase the likelihood of adoption of AI in healthcare.
Work-in-progress
| Originele taal-2 | English |
|---|---|
| Status | Published - sep.-2024 |
| Evenement | 38th Annual Conference of the European Health Psychology Society: Health Psychology for a Sustainable Future - Cascais, Portugal Duur: 3-sep.-2024 → 6-sep.-2024 https://2024.ehps.net/ |
Conference
| Conference | 38th Annual Conference of the European Health Psychology Society |
|---|---|
| Verkorte titel | EHPS2024 |
| Land/Regio | Portugal |
| Stad | Cascais |
| Periode | 03/09/2024 → 06/09/2024 |
| Internet adres |