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

Viewpoints and Opinions on Human Factors (6) Viewpoints and Perspectives (514) Artificial Intelligence (1371) Decision Support for Health Professionals (1185) Clinical Information and Decision Making (1407) Quality/Credibility of eHealth Information and Trust Issues (531) Theoretical Frameworks and Concepts (252) Theoretical Innovations in AI (4)

Published on in Vol 9, No 2 (2022): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35421, first published .
Toward an Ecologically Valid Conceptual Framework for the Use of Artificial Intelligence in Clinical Settings: Need for Systems Thinking, Accountability, Decision-making, Trust, and Patient Safety Considerations in Safeguarding the Technology and Clinicians

Toward an Ecologically Valid Conceptual Framework for the Use of Artificial Intelligence in Clinical Settings: Need for Systems Thinking, Accountability, Decision-making, Trust, and Patient Safety Considerations in Safeguarding the Technology and Clinicians

Toward an Ecologically Valid Conceptual Framework for the Use of Artificial Intelligence in Clinical Settings: Need for Systems Thinking, Accountability, Decision-making, Trust, and Patient Safety Considerations in Safeguarding the Technology and Clinicians

Authors of this article:

Avishek Choudhury1 Author Orcid Image
Article Authors Cited by (16) Tweetations (3) Metrics

Journals

  1. Wu C, Xu H, Bai D, Chen X, Gao J, Jiang X. Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis. BMJ Open 2023;13(1):e066322 View
  2. Choudhury A, Asan O. Impact of accountability, training, and human factors on the use of artificial intelligence in healthcare: Exploring the perceptions of healthcare practitioners in the US. Human Factors in Healthcare 2022;2:100021 View
  3. Al-Arnous A, Abdelwahed N. The Impact of the Implementation of Safety Measures on Frontline Workers’ Safety Accountability: A Saudi Arabian Case Study of a Well Intervention Business Model. Safety 2022;8(4):82 View
  4. Choudhury A, Elkefi S. Acceptance, initial trust formation, and human biases in artificial intelligence: Focus on clinicians. Frontiers in Digital Health 2022;4 View
  5. Choudhury A. Factors influencing clinicians' willingness to use an AI-based clinical decision support system. Frontiers in Digital Health 2022;4 View
  6. Shamszare H, Choudhury A. Clinicians’ Perceptions of Artificial Intelligence: Focus on Workload, Risk, Trust, Clinical Decision Making, and Clinical Integration. Healthcare 2023;11(16):2308 View
  7. Khalifa M, Albadawy M, Iqbal U. Advancing clinical decision support: The role of artificial intelligence across six domains. Computer Methods and Programs in Biomedicine Update 2024;5:100142 View
  8. Kinney M, Anastasiadou M, Naranjo-Zolotov M, Santos V. Expectation management in AI: A framework for understanding stakeholder trust and acceptance of artificial intelligence systems. Heliyon 2024;10(7):e28562 View
  9. Franco D’Souza R, Mathew M, Mishra V, Surapaneni K. Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education. Medical Education Online 2024;29(1) View
  10. Choudhury A, Shamszare H. The Impact of Performance Expectancy, Workload, Risk, and Satisfaction on Trust in ChatGPT: Cross-sectional Survey Analysis (Preprint). JMIR Human Factors 2023 View
  11. Bhatnagr P, Rajesh A, Misra R. A study on online brand experience in Indian neobanking. International Journal of System Assurance Engineering and Management 2024 View
  12. Sharma H, Gupta N, Garg N, Dhankhar S, Chauhan S, Beniwal S, Saini D. Herbal Medicinal Nanoformulations for Psoriasis Treatment: Current State of Knowledge and Future Directions. The Natural Products Journal 2024;14(7) View
  13. Ibrahim A, Abdel-Aziz H, Mohamed H, Zaghamir D, Wahba N, Hassan G, Shaban M, EL-Nablaway M, Aldughmi O, Aboelola T. Balancing confidentiality and care coordination: challenges in patient privacy. BMC Nursing 2024;23(1) View
  14. Barbosa J, Toscano M, Araújo D, Moreira M, Nóbrega W, Martins Q. Hypodermoclysis As a Care Tool: Algorithm Construction and Validation. Aquichan 2024;24(3):1 View

Books/Policy Documents

  1. Choudhury A, Saremi M, Urena E. Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems. View
  2. Sreeraj V, Parlikar R, Bagali K, Singh Shekhawat H, Venkatasubramanian G. Exploration of Artificial Intelligence and Blockchain Technology in Smart and Secure Healthcare. View