User Intent to Use DeepSeek for Health Care Purposes and Their Trust in the Large Language Model: Multinational Survey Study
Notice bibliographique
Résumé
BACKGROUND: Generative artificial intelligence (Gen-AI)-particularly large language models (LLMs)-has generated unprecedented interest in applications ranging from everyday Q&A to health-related inquiries. However, little is known about how everyday users decide whether to trust and adopt these technologies-particularly in high-stakes contexts like personal health. OBJECTIVE: This study examines how ease of use, perceived usefulness, and risk perception interact to shape user trust in and intentions to adopt DeepSeek, an emerging LLM-based platform, for healthcare purposes. METHODS: We adapted survey items from validated technology acceptance scales to assess user perception of DeepSeek, focusing on constructs such as trust, intent to use for health, ease of use, perceived usefulness, and risk perception. A 12-item Likert scale questionnaire was developed and pilot-tested (n=20) for clarity and consistency. It was then distributed online to users in India (IND), United Kingdom (UK), and United States of America (USA) who had used DeepSeek within the past two weeks. Data analysis involved descriptive frequency assessments and Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the measurement and structural models. Structural equation modeling assessed direct and indirect effects, including potential quadratic relationships. RESULTS: A total of 556 complete responses were collected, with respondents almost evenly split across IND (n=184), the UK (n=185), and the USA (n=187). Regarding AI in healthcare, when asked if they were comfortable with their healthcare provider using AI tools, 59.3% (n=330) were fine with AI use provided their doctor verified its output, and 31.5% (n=175) were enthusiastic about its use without conditions. DeepSeek was used primarily for academic and educational purposes, 50.7% (n=282) used DeepSeek as a search engine, and 47.7% (n=265) for health-related queries. When asked about their intent to adopt DeepSeek over other LLMs like ChatGPT, 52.1% (n=290) were likely to switch, and 28.9% (n=161) were very likely to do so. The study revealed that trust plays a pivotal mediating role: ease of use exerts a significant indirect impact on usage intentions through trust. At the same time, perceived usefulness contributes to trust development and direct adoption. By contrast, risk perception negatively affects usage intent, emphasizing the importance of robust data governance and transparency. Significant non-linear paths were observed for ease of use and risk, indicating threshold or plateau effects. CONCLUSIONS: Users are receptive to DeepSeek when it's easy to use, useful, and trustworthy. The model highlights trust as a mediator and shows non-linear dynamics shaping AI-driven healthcare tool adoption. Expanding the model with mediators like privacy and cultural differences could provide deeper insights. Longitudinal or experimental designs could establish causality and track user attitudes. Further investigation into threshold and plateau phenomena could refine our understanding of user perceptions as they become more familiar with AI-driven healthcare tools.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».