Published on in Vol 23, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25856, first published .
Patients’ Perceptions Toward Human–Artificial Intelligence Interaction in Health Care: Experimental Study

Patients’ Perceptions Toward Human–Artificial Intelligence Interaction in Health Care: Experimental Study

Patients’ Perceptions Toward Human–Artificial Intelligence Interaction in Health Care: Experimental Study

Journals

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  3. Jeyakumar T, Younus S, Zhang M, Clare M, Charow R, Karsan I, Dhalla A, Al-Mouaswas D, Scandiffio J, Aling J, Salhia M, Lalani N, Overholt S, Wiljer D. Preparing for an Artificial Intelligence–Enabled Future: Patient Perspectives on Engagement and Health Care Professional Training for Adopting Artificial Intelligence Technologies in Health Care Settings. JMIR AI 2023;2:e40973 View
  4. Sebastian G, George A, Jackson Jr G. Persuading Patients Using Rhetoric to Improve Artificial Intelligence Adoption: Experimental Study. Journal of Medical Internet Research 2023;25:e41430 View
  5. Wilson N. CORR Insights®: Can a Deep Learning Algorithm Improve Detection of Occult Scaphoid Fractures in Plain Radiographs? A Clinical Validation Study. Clinical Orthopaedics & Related Research 2023;481(9):1836 View
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  12. Marks R. Artificial intelligence and rehabilitation: what’s new and promising. International Physical Medicine & Rehabilitation Journal 2023;8(2):135 View
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  15. Wang B, Asan O, Mansouri M. Perspectives of Patients With Chronic Diseases on Future Acceptance of AI–Based Home Care Systems: Cross-Sectional Web-Based Survey Study. JMIR Human Factors 2023;10:e49788 View
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  19. Busch F, Adams L, Bressem K. Biomedical Ethical Aspects Towards the Implementation of Artificial Intelligence in Medical Education. Medical Science Educator 2023;33(4):1007 View
  20. Tang L, Li J, Fantus S. Medical artificial intelligence ethics: A systematic review of empirical studies. DIGITAL HEALTH 2023;9 View
  21. Holm S, Ploug T, Fiaschetti M. Population preferences for AI system features across eight different decision-making contexts. PLOS ONE 2023;18(12):e0295277 View
  22. Alanzi T, Almahdi R, Alghanim D, Almusmili L, Saleh A, Alanazi S, Alshobaki K, Attar R, Al Qunais A, Alzahrani H, Alshehri R, Sulail A, Alblwi A, Alanzi N, Alanzi N. Factors Affecting the Adoption of Artificial Intelligence-Enabled Virtual Assistants for Leukemia Self-Management. Cureus 2023 View
  23. Cai Z, He H, Huo W, Xu X. More Unique, More Accepting? Integrating Sense of Uniqueness, Perceived Knowledge, and Perceived Empathy with Acceptance of Medical Artificial Intelligence. International Journal of Human–Computer Interaction 2023:1 View
  24. Bhandarkar S, Tsutsumi A, Schneider E, Ong C, Paredes L, Brackett A, Ahuja V. Emergent Applications of Machine Learning for Diagnosing and Managing Appendicitis: A State-of-the-Art Review. Surgical Infections 2024;25(1):7 View
  25. Sassi Z, Hahn M, Eickmann S, Herrmann-Johns A, Tretter M. Beyond algorithmic trust: interpersonal aspects on consent delegation to LLMs. Journal of Medical Ethics 2024;50(2):139 View
  26. Gavette H, McDonald C, Kostick-Quenet K, Mullen A, Najafi B, Finco M. Advances in prosthetic technology: a perspective on ethical considerations for development and clinical translation. Frontiers in Rehabilitation Sciences 2024;4 View
  27. Evans R, Bryant L, Russell G, Absolom K. Trust and acceptability of data-driven clinical recommendations in everyday practice: A scoping review. International Journal of Medical Informatics 2024;183:105342 View
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  30. Fazakarley C, Breen M, Thompson B, Leeson P, Williamson V. Beliefs, experiences and concerns of using artificial intelligence in healthcare: A qualitative synthesis. DIGITAL HEALTH 2024;10 View
  31. Wong C, O’Byrne C, Taribagil P, Liu T, Antaki F, Keane P. Comparing code-free and bespoke deep learning approaches in ophthalmology. Graefe's Archive for Clinical and Experimental Ophthalmology 2024;262(9):2785 View
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  35. Guo S, Li R, Li G, Chen W, Huang J, He L, Ma Y, Wang L, Zheng H, Tian C, Zhao Y, Pan X, Wan H, Liu D, Li Z, Lei J. Comparing ChatGPT's and Surgeon's Responses to Thyroid-related Questions From Patients. The Journal of Clinical Endocrinology & Metabolism 2024 View
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  44. Alanezi F. Factors influencing patients’ engagement with ChatGPT for accessing health-related information. Critical Public Health 2024;34(1):1 View
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  54. Haykal D. Emerging and Pioneering AI Technologies in Aesthetic Dermatology: Sketching a Path Toward Personalized, Predictive, and Proactive Care. Cosmetics 2024;11(6):206 View
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Books/Policy Documents

  1. Saxena A, Chauhan S, Singh H, Chauhan U, Kumari P. Infrastructure Possibilities and Human-Centered Approaches With Industry 5.0. View
  2. Durrah O, Aldhmour F, El-Maghraby L, Chakir A. Engineering Applications of Artificial Intelligence. View
  3. Zaabi A, Padela A. Digital Healthcare in Asia and Gulf Region for Healthy Aging and More Inclusive Societies. View
  4. Mohammed A, AL-Abrrow H, Thajil K, Alnoor A, Abbas S. Explainable Artificial Intelligence in the Digital Sustainability Administration. View
  5. Abidi N, Abidi Z, Hanrahan B, Parampreet Kaur M, Kerego Y, Ng Pellegrino A, Thulasi V. Artificial Intelligence in Medicine and Surgery - An Exploration of Current Trends, Potential Opportunities, and Evolving Threats - Volume 2. View
  6. Khan W, Shayan Khan M, Khan N, Ahmad A, Siddiqui Z, Brojen Singh R, Zubbair Malik M. Artificial Intelligence in Biomedical and Modern Healthcare Informatics. View