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Artificial intelligence (AI) is transforming healthcare delivery, with telemedicine emerging as one of its most viable and socially impactful applications, raising important questions about how public trust is formed and sustained in AI-enabled healthcare environments. Yet, despite rapid technological adoption, public trust in AI-enabled telemedicine remains insufficiently understood, particularly across the diverse online environments where perceptions are collectively shaped. This study employs a sociotechnical, multi-platform big data analytics approach to quantify and interpret public trust in large-scale digital discourse. A total of 25,396 online mentions collected between 14 September and 14 October 2025 from news, social media, blogs, video, and web platforms were analyzed using a hybrid framework combining lexicon-based sentiment analysis ( VADER ), transformer-based contextual modeling ( RoBERTa ), and topic modeling ( BERTopic ). The results indicate a consistently positive sentiment orientation ( aggregate Trust Index = +0.84 ), suggesting a favorable public outlook toward AI-enabled telemedicine during the observation period. Affective trust is more prominent in visual and community-based media, while cognitive and structural trust are more salient in institutional and professional contexts. These results inform an integrated conceptual model of public trust formation in AI-enabled telemedicine, conceptualizing trust as a multidimensional sociotechnical construct. Rather than introducing a novel methodology, this study demonstrates the integrative application of established computational techniques to capture public sentiment at scale. These insights provide practical guidance for healthcare providers, policymakers, and AI developers to design more transparent, patient-centered, and trustworthy telemedicine systems.
Binsar et al. (Tue,) studied this question.