I, AI-Doctor: Developing and Validating an Optimal Design Combination for Mobile Healthcare User Interfaces Through a Hybrid Approach

AI doctor interfaces can facilitate personalized dialogues and humanized interactions. To maximize user engagement, interface design elements and strategies should be tailored to specific applications rather than relying on generic design recommendations. This study used a hybrid approach to identify factors affecting user perceptions and behavioral intentions toward AI-mediated mHealth interface design and determine the optimal design combination. The results indicated that the level of social identity cues, conversation clues, loading visual types, and loading duration significantly affected users’ experiences with the mHealth interface. A post hoc multiperformance characteristic index analysis confirmed that one identity cue (level of social identity cues) and one dialogue cue (conversation clues) were critical elements in AI doctor interface design. This study not only provides a practical and efficient approach for optimizing both single and multiple performance characteristics of AI doctor interfaces but also offers comprehensive theoretical guidance for designing AI-mediated healthcare solutions.