This paper is in the following e-collection/theme issue:

Ethics, Privacy, and Legal Issues (617) Artificial Intelligence (2129) Quality/Credibility of eHealth Information and Trust Issues (654) Theoretical Frameworks and Concepts (338) Clinical Informatics (1325) Public (e)Health, Digital Epidemiology and Public Health Informatics (1008) Policy and Policy Proposals (150)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56306, first published .
Finding Consensus on Trust in AI in Health Care: Recommendations From a Panel of International Experts

Finding Consensus on Trust in AI in Health Care: Recommendations From a Panel of International Experts

Finding Consensus on Trust in AI in Health Care: Recommendations From a Panel of International Experts

Authors of this article:

Georg Starke1, 2 Author Orcid Image ;   Felix Gille3, 4 Author Orcid Image ;   Alberto Termine1, 2, 5 Author Orcid Image ;   Yves Saint James Aquino6 Author Orcid Image ;   Ricardo Chavarriaga7 Author Orcid Image ;   Andrea Ferrario8 Author Orcid Image ;   Janna Hastings4, 9 Author Orcid Image ;   Karin Jongsma10 Author Orcid Image ;   Philipp Kellmeyer11, 12 Author Orcid Image ;   Bogdan Kulynych13 Author Orcid Image ;   Emily Postan14 Author Orcid Image ;   Elise Racine15, 16 Author Orcid Image ;   Derya Sahin17 Author Orcid Image ;   Paulina Tomaszewska18 Author Orcid Image ;   Karina Vold19, 20 Author Orcid Image ;   Jamie Webb21 Author Orcid Image ;   Alessandro Facchini5 Author Orcid Image ;   Marcello Ienca1, 2 Author Orcid Image
Article Authors Cited by (19) Tweetations Metrics

Journals

  1. Starke G. More to Know Could Not be More to Trust: Open Communication as a Moral Imperative for AI Systems in Healthcare. The American Journal of Bioethics 2025;25(3):119 View
  2. Steerling E, Svedberg P, Nilsen P, Siira E, Nygren J. Influences on trust in the use of AI-based triage—an interview study with primary healthcare professionals and patients in Sweden. Frontiers in Digital Health 2025;7 View
  3. Serin O, Yıldırım B, Duman F, Ercorumlu K, Yavas R, Tasar M, Celik M. Physician perspectives on Artificial Intelligence in healthcare: a cross-sectional study of child-focused care in a Turkish tertiary hospital. International Journal of Medical Informatics 2025;203:106003 View
  4. Haferlach T, Eckardt J, Walter W, Maschek S, Kather J, Pohlkamp C, Middeke J. AML diagnostics in the 21st century: Use of AI. Seminars in Hematology 2025;62(3):226 View
  5. Starke G, Ienca M. AI, neurotechnology and society — a question of trust. Nature Reviews Neurology 2025;21(8):407 View
  6. Masayoshi K, Hashimoto M, Toda N, Mori H, Kobayashi G, Haque H, So M, Jinzaki M. Training Language Models for Estimating Priority Levels in Ultrasound Examination Waitlists: Algorithm Development and Validation. JMIR AI 2025;4:e68020 View
  7. Almalki M, Sacgaca L, Pangket P, Pasay-an E, Deep S, Maskay G, Benjamin L, Hashim S, Gonzales A, Rosal R, Mohsen M, Mostoles R, Lagura G. Integration of artificial intelligence in nursing education: a cross-national exploration. BMC Nursing 2025;24(1) View
  8. Timofte E, Dimian M, Mangul S, Potorac A, Gherman O, Balan D, Pușcașu M. Multi-Layered Security Assessment in mHealth Environments: Case Study on Server, Mobile and Wearable Components in the PHGL-COVID Platform. Applied Sciences 2025;15(15):8721 View
  9. Yong L, Tung J, Cheung N, Lee Z, Ng E, Ng A, Lim C, Boon Y, Lim D, Sng G, Tang J. Artificial Intelligence Applications in Emergency Toxicology: Advancements and Challenges. Journal of Medical Internet Research 2025;27:e73121 View
  10. Yao L, Hou W, Zheng Y, Su G. Global, regional, and national burden of cardiovascular diseases attributable to high body mass index from 1990 to 2021. Frontiers in Cardiovascular Medicine 2025;12 View
  11. Alchamdani , Anas A, Sunita . Artificial Intelligence, Big Data, and IoT in Public Health Surveillance: A Global Narrative Review. Medicor : Journal of Health Informatics and Health Policy 2025;3(3):161 View
  12. Fletcher I. Advocating for trust in and trustworthy AI to transform evidence synthesis. Environmental Evidence 2025;14(1) View
  13. Gille F, Daniore P, Maaß L, Zavattaro F. Trust Performance Indicators and Trust Stress Tests: A Conceptual Proposition for Trustworthy Health Data Spaces. International Journal of Public Health 2025;70 View
  14. Anghel C, Anghel A, Pecheanu E, Craciun M, Cocu A, Niculita C. PEARL: A Rubric-Driven Multi-Metric Framework for LLM Evaluation. Information 2025;16(11):926 View
  15. Reis F, Reis M, Drzeniek N, Balzer F. Trust is all you Need: Reinforcing the Patient-physician Bond in Times of AI. Journal of Medical Systems 2025;49(1) View
  16. Chen Z, Liu R, Huang S, Guo Y, Ren Y. A Survey of Large-Scale Deep Learning Models in Medicine and Healthcare. Computer Modeling in Engineering & Sciences 2025;144(1):37 View
  17. Tung J, Le Q, Yao J, Huang Y, Lim D, Sng G, Lau R, Tan Y, Chen K, Tay K, Tan J, Yuen J, Cheng C, Ho H. Performance of Retrieval-Augmented Generation Large Language Models in Guideline-Concordant Prostate-Specific Antigen Testing: Comparative Study With Junior Clinicians. Journal of Medical Internet Research 2025;27:e78393 View
  18. Xiao L, Han X. Enhancing patient value co-creation via AI-enabled cases disclosure: The role of uncertainty reduction, patient empowerment and risk aversion. Social Science & Medicine 2026;389:118822 View
  19. Sides T, Kbaier D, Farrell T, Third A. Bridging trust gaps: Stakeholder perspectives on AI adoption in the United Kingdom NHS primary care. DIGITAL HEALTH 2025;11 View