ArticleFrontiers in public health2026
Mapping applications and evaluations of LLM-enabled AI chatbots for health purposes: a scoping review.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Large Language Model (LLM)-enabled artificial intelligence (AI) chatbots are increasingly shaping health communication by mediating how patients, health professionals, researchers, and health institutions seek, interpret, produce, and act on health information. Existing reviews have largely focused on a single stakeholder group or clinical domain, leaving unclear how these systems are applied and evaluated across stakeholder groups and health purposes. Objective: This scoping review mapped empirical studies of LLM-enabled AI chatbot applications and evaluations for health purposes across four stakeholder groups: the public or patients, health professionals, health researchers and students, and health institutions. We characterized the purposes and evidence patterns associated with these applications and evaluations. Methods: We searched nine databases for peer-reviewed, English-language empirical studies available through July 2025. After screening, 286 articles were coded for study characteristics, methodology, evidence type, chatbot modality, LLM type, health topic, stakeholder group, and health purpose. Results: The included articles were published between 2023 and 2025. Most examined general-purpose LLMs ( Conclusion: This review provides a stakeholder-purpose mapping of AI chatbot applications and evaluations in health care. The evidence base is concentrated in text-based, lower-acuity, and information-oriented contexts and derives primarily from output evaluations and self-reported perceptions or use. Key gaps concern institutional integration and governance, higher-stakes and longitudinal contexts, and evidence across populations, languages, and interaction modalities.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.