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Published on in Vol 9 (2025)

This is a member publication of JISC

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65314, first published .
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Factors Influencing the Use of Online Symptom Checkers in the United Kingdom: Cross-Sectional Study

Factors Influencing the Use of Online Symptom Checkers in the United Kingdom: Cross-Sectional Study

Journals

  1. Joshi R, Schlesinger S, Bengler K. Who is my user? Explaining intention to use chat-based symptom checkers through user characteristics. Zeitschrift für Arbeitswissenschaft 2026;80(2):236 View
  2. Löwe W, Löwe G. A light-weight symptom checker and its methodological validation. Veterinary Research Communications 2026;50(4) View
  3. Mitsuhashi T. Routine Generative AI Use and Acceptance of Medical AI: A Cross-Sectional Survey of Healthcare Workers and the Public in Japan. Cureus 2026 View
  4. Kopka M, Majeed A, Spinelli G, El-Osta A, Feufel M, Khalemsky A. The trust in AI-generated health advice (TAIGHA) scale and short version (TAIGHA-S): Development and validation study. PLOS Digital Health 2026;5(7):e0001488 View
  5. Ayre J, Hudson C, Hudson-Barton M, Gallagher K, Smith J, Bamgboje-Ayodele A, Scott A, Woods A, Ng C, Wickramasinghe Y, Ma I, Edlund G, Butters L, Nadesan W, Vu T, Kapoor G, McCaffery K. Symptom checker formats to improve symptom management knowledge and trust in AI-enhanced advice: Qualitative insights. Patient Education and Counseling 2026:109874 View
  6. El-Osta A, Altalib S, Al Ammouri M, Smith P. What are the demographic predictors and barriers to self-care engagement in the UK? a cross-sectional survey study. BMJ Open 2026;16(9):e110380 View
  7. Al-Zahra F, El Idrissi A. Artificial Intelligence for Care Navigation and Patient Access: A Systematic Review of Scheduling Optimization, Referral Management, Eligibility Screening, Digital Front-Door Tools, and Follow-Up Coordination. Journal of Health Informatics and Digital Systems 2026;6(1) View