Human–machine trust has shifted over the past three decades from trust in automation to trust in AI, while research paradigms, disciplines, and problem spaces have expanded. Centered on AI in healthcare, this narrative review offers a longitudinal synthesis that traces and compares phase-specific changes in theory and method, providing design guidance for human-AI systems at different stages of maturity. From a cross-disciplinary view, we introduce an Interdisciplinary Human-AI Trust Research (I-HATR) framework that aligns explainable AI (XAI) with human–computer interaction/human factors engineering (HCI/HFE). We distill three core categories of determinants of human-AI trust in healthcare, user characteristics, AI system attributes, and contextual factors, and summarize the main measurement families and their evolution from self-report to behavioral and psychophysiological approaches, with growing use of multimodal and dynamic evaluation. Finally, we outline key trends, opportunities, and practical challenges to support the development of human-centered, trustworthy AI in healthcare, emphasizing the need to bridge actual trustworthiness and perceived trust through shared metrics, uncertainty communication, and trust calibration.

Understanding how users perceive and attend to the anthropomorphic appearance and empathic responses of artificial intelligence medical conversational agents (AIMCAs) can help reveal the key judgment cues underlying trust formation and use decisions, while also informing interface and dialog design. To this end, this study employs a 3 (appearance anthropomorphism: high, medium, low) × 2 (empathic response: present, absent) within-subject eye-tracking experiment, combined with subjective scales and brief post-task open-ended feedback. During a static prototype viewing task based on hypothetical consultation scenarios, we concurrently recorded trust, behavioral intention, and visual measures for key areas of interest (AOIs; appearance area, conversational content area, and overall interface area). Eye-tracking measures were normalized by AOI coverage proportion to improve cross-AOI comparability. The results show that both anthropomorphic appearance and empathic response significantly increased users’ trust in AIMCAs and their behavioral intention. An interaction between these two types of social cues was also observed, suggesting that when visual embodiment and linguistic style are aligned at the social level, users are more likely to form favorable overall judgments. At the level of visual processing, however, no interaction effect was found, and the eye-tracking measures showed only partial main effects, indicating that subjective synergy does not necessarily correspond to synergistic changes in attentional allocation. Overall, anthropomorphic appearance and empathic response exerted consistent facilitating effects on outcome variables, but displayed different patterns of attentional allocation and information prioritization at the visual level. Accordingly, AIMCA design should emphasize consistency between appearance cues and conversational strategies, optimize users’ initial judgments and interface comprehension, and use intention through verifiable information organization and clear boundary cues.

Evaluations of medical artificial intelligence (AI) explanations often rely on self-reported trust, perceived usefulness, acceptance, or final decision outcomes, while less directly characterizing whether users check evidence around AI outputs during decision making. In AI-assisted chest radiograph interpretation, a critical process-level question is whether users return from the AI output to the original image evidence when further scrutiny is needed. To address this question, we examined whether verifiable explanations—explanations designed to make AI recommendations checkable against the original image evidence—are associated with process markers of visual evidence checking in AI-assisted chest radiograph interpretation using eye-tracking and human-factors process measures. A 2 × 2 between-subjects experiment manipulated verifiable explanations (present vs. absent) and risk context (high vs. low), with AI recommendation correctness embedded at the trial level. Fifty-six clinically trained participants each completed 24 interpretation trials. Analyses focused primarily on gaze transitions between the AI output and the original image and dwell time on the original image, with response time and exploratory verification-related behavioral states used as auxiliary process measures. Verifiable explanations did not simply increase acceptance of AI recommendations. Instead, when AI recommendations were incorrect, they were most clearly associated with more frequent AI–image transitions and longer absolute dwell time on the original image evidence. Exploratory state-based analyses further suggested a lower tendency toward no-verify adopt under incorrect AI recommendations, but these findings were treated as complementary rather than primary evidence. Overall, the value of verifiable explanations lies not only in final decisions but in whether they make AI recommendations more inspectable against the original evidence. These findings provide eye-tracking evidence consistent with visual evidence checking in AI-assisted diagnostic interfaces and underscore the value of process-sensitive human-factors measures in medical AI evaluation.

Traditional furniture design methods often fall short in revealing users’ deeper psychological and emotional needs. This study innovatively introduced the Zaltman Metaphor Elicitation Technique (ZMET) as an exploratory tool in the field of furniture design to bridge the cognitive gap between latent user needs and practical design. Through image collection, laddering interviews, and Kelly Repertory Grid Technique (RGT), cognitive modeling was conducted with six high-involvement users. A total of 119 concepts were identified, covering ten design attributes (e.g., “ergonomic fit,” “warm color tones”), seven emotional needs (e.g., “stress relief,” “immersive experience”), and five value hierarchies (e.g., “sense of belonging,” “self-actualization”). The resulting Hierarchical Value Map (HVM) illustrated a three-level structure of “attribute–consequence–value,” clearly mapping the psychological pathway from product features to core user values. The findings revealed that users expect more than basic functionality from wooden lounge chairs, seeking emotional resonance, lifestyle alignment, and identity expression. ZMET was shown to be effective in uncovering non-verbalized user needs and offers a theoretical and methodological framework for value-driven, user-centered furniture design.