Abstract
First consumer-facing apps for medical self-diagnosis through Artificial Intelligence have hit the market only recently. These promise to detect malicious skin changes from photos or respiratory diseases from cough noises captured by the smartphone microphone, for example. While there is a large body of research on HCI-related aspects of mobile health applications, knowledge about the user perceptions of such novel AI-driven self-diagnosis apps and factors affecting their acceptance and adoption is scarce. In an online survey, we investigated the participants´ overall willingness-to-use (considering four types of captured and processed data) and identified trust factors and desirable features. We found that more than half of the participants would use AI-driven self-diagnosis apps, yet mainly integrated into prevailing general practitioner care. Based on the results, we draw conclusions which can guide the design, development, and launch of AI-driven self-diagnosis apps.
Original language | English |
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Title of host publication | Proceedings 19th International Conference on Mobile and Ubiquitous Multimedia (MUM 2020) |
Pages | 167-178 |
Number of pages | 12 |
DOIs | |
Publication status | Published - 2020 |
Event | MUM2020 - 19th International Conference on Mobile and Ubiquitous Multimedia - Duration: 22 Nov 2020 → 25 Nov 2020 |
Conference
Conference | MUM2020 - 19th International Conference on Mobile and Ubiquitous Multimedia |
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Period | 22/11/20 → 25/11/20 |
Research Field
- Former Research Field - Capturing Experience
Keywords
- mhealth
- health assessment
- self-diagnosis
- artificial intelligence