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

Viewpoints and Opinions on Human Factors (8) Viewpoints and Perspectives (619) Artificial Intelligence (2129) Decision Support for Health Professionals (1539) Clinical Information and Decision Making (1915) Quality/Credibility of eHealth Information and Trust Issues (654) Theoretical Frameworks and Concepts (338) Theoretical Innovations in AI (6)

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 (30) 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. RETRACTED: 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
  15. Dailah H, Koriri M, Sabei A, Kriry T, Zakri M. Artificial Intelligence in Nursing: Technological Benefits to Nurse’s Mental Health and Patient Care Quality. Healthcare 2024;12(24):2555 View
  16. Owoyemi A, Osuchukwu J, Salwei M, Boyd A. Checklist Approach to Developing and Implementing AI in Clinical Settings: Instrument Development Study. JMIRx Med 2025;6:e65565 View
  17. Alami J, El Iskandarani M, Riggs S. The Effect of Workload and Task Priority on Multitasking Performance and Reliance on Level 1 Explainable AI (XAI) Use. Human Factors: The Journal of the Human Factors and Ergonomics Society 2025;67(9):897 View
  18. Hwang M, Zheng Y, Cho Y, Jiang Y. AI Applications for Chronic Condition Self-Management: Scoping Review. Journal of Medical Internet Research 2025;27:e59632 View
  19. Bottacin W, de Souza T, Melchiors A, Reis W. Explanation and elaboration of MedinAI: guidelines for reporting artificial intelligence studies in medicines, pharmacotherapy, and pharmaceutical services. International Journal of Clinical Pharmacy 2025;47(4):957 View
  20. Winkler P, Zsidai B, Hamrin Senorski E, Pruneski J, Hirschmann M, Ley C, Tischer T, Herbst E, Pareek A, Musahl V, Oeding J, Oettl F, Longo U, Samuelsson K, Feldt R. A practical guide to the implementation of AI in orthopaedic research—Part 7: Risks, limitations, safety and verification of medical AI systems. Journal of Experimental Orthopaedics 2025;12(2) View
  21. Munsamy A, Oderinlo O. Artificial intelligence: An innovation shaping modern eye care. African Vision and Eye Health 2024;83(1) View
  22. Velastegui-Hernandez D, Contreras-Vásquez L, Sandoval G, Tufiño-Aguilar A, Cevallos-Teneda A, Reyes-Rosero E, Salinas-Velastegui V, Velastegui-Hernández R, Vasquez de la Bandera F, Salazar-Garcés L. Psychological impact of ai in the automation of clinical decision-making. Salud, Ciencia y Tecnología 2025;5:1586 View
  23. Shamszare H, Chaudhry Z, Berenji M, Choudhury A. Conceptualizing Clinicians’ Trust in Artificial Intelligence as a Function of Their Expertise, Workload, Patient Outcome, Diagnosis Difficulty, and AI Accuracy: A Systems Thinking Approach. IEEE Access 2025;13:119601 View
  24. Velasco L, Wang W. Theoretical appraisal of explanatory paradigms for artificial intelligence usage by medical doctors. DIGITAL HEALTH 2025;11 View
  25. Boussi Rahmouni H, Hassine N, Chouchen M, Ceylan H, Muntean R, Bragazzi N, Dergaa I. Healthcare 5.0-Driven Clinical Intelligence: The Learn-Predict-Monitor-Detect-Correct Framework for Systematic Artificial Intelligence Integration in Critical Care. Healthcare 2025;13(20):2553 View
  26. Schaffernak I, Cecil J, Kleine A, Lermer E. Sociotechnical influences on the adoption and use of AI-enabled clinical decision support systems in ophthalmology: a theory-based interview study. BMC Health Services Research 2025;25(1) View
  27. Browning L, Ghosh A, Dolton M, Hutton K, Birks J, Scheffer R, Stanislaus E, Crofts J, Colling R, Bryant R, Verrill C. Exploring patient and clinician opinions, perspectives and acceptance of the use of artificial intelligence in the histological diagnosis of prostate cancer. BJUI Compass 2025;6(11) 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

Conference Proceedings

  1. Turchi T, Mikhaylova D, Troccoli M, Malizia A, Cimino M, Galatolo F, La Mantia G, Campisi G, Di Fede O. Proceedings of the 16th Biannual Conference of the Italian SIGCHI Chapter. Ecological Validity Missing in AI-Assisted Clinical Decision Support Research: Why Real-World Context Matters View