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

Theoretical Frameworks and Concepts (258) Artificial Intelligence (1409) Decision Support for Health Professionals (1203) Clinical Information and Decision Making (1426)

Published on in Vol 23, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25929, first published .
Evaluation Framework for Successful Artificial Intelligence–Enabled Clinical Decision Support Systems: Mixed Methods Study

Evaluation Framework for Successful Artificial Intelligence–Enabled Clinical Decision Support Systems: Mixed Methods Study

Evaluation Framework for Successful Artificial Intelligence–Enabled Clinical Decision Support Systems: Mixed Methods Study

Authors of this article:

Mengting Ji1, 2 Author Orcid Image ;   Georgi Z Genchev3, 4, 5 Author Orcid Image ;   Hengye Huang1 Author Orcid Image ;   Ting Xu1 Author Orcid Image ;   Hui Lu3, 4, 6 Author Orcid Image ;   Guangjun Yu1, 7 Author Orcid Image
Article Authors Cited by (22) Tweetations (7) Metrics

Journals

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  8. He X, Zheng X, Ding H, Liu Y, Zhu H. AI-CDSS Design Guidelines and Practice Verification. International Journal of Human–Computer Interaction 2024;40(18):5469 View
  9. Cresswell K, Rigby M, Magrabi F, Scott P, Brender J, Craven C, Wong Z, Kukhareva P, Ammenwerth E, Georgiou A, Medlock S, De Keizer N, Nykänen P, Prgomet M, Williams R. The need to strengthen the evaluation of the impact of Artificial Intelligence-based decision support systems on healthcare provision. Health Policy 2023;136:104889 View
  10. Nair M, Lundgren L, Soliman A, Dryselius P, Fogelberg E, Petersson M, Hamed O, Triantafyllou M, Nygren J. Machine Learning Model for Readmission Prediction of Patients With Heart Failure Based on Electronic Health Records: Protocol for a Quasi-Experimental Study for Impact Assessment. JMIR Research Protocols 2024;13:e52744 View
  11. Deng W, Yang T, Ju X, Deng J. Linking enterprise information systems success to female employees’ work–family enrichment in China. Journal of Management & Organization 2024:1 View
  12. Yang Y, Ngai E, Wang L. Resistance to artificial intelligence in health care: Literature review, conceptual framework, and research agenda. Information & Management 2024;61(4):103961 View
  13. Tegenaw G, Sori D, Teklemariam G, Verbeke F, Cornelis J, Jansen B. Evaluation of a Computer-Aided Clinical Decision Support System for Point-of-Care Use in Low-Resource Primary Care Settings: Acceptability Evaluation Study. JMIR Human Factors 2024;11:e47631 View
  14. Ogundipe A, Sim T, Emmerton L. Prescription for Digital Evolution: Transformative Recommendations for Pharmacy Practice in the Digital Age. Journal of Pharmacy Practice 2024 View
  15. Gharibshahian M, Torkashvand M, Bavisi M, Aldaghi N, Alizadeh A. Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine. Skin Research and Technology 2024;30(9) View
  16. Ramgopal S, Macy M, Hayes A, Florin T, Carroll M, Kshetrapal A. Clinician Perspectives on Decision Support and AI-based Decision Support in a Pediatric ED. Hospital Pediatrics 2024;14(10):828 View
  17. Aminifard M, Makizadeh V, Ahmadi Kahnali R, Nekooeezadeh M. ChatGPT Adoption in Marketing: Exploring Drivers and Barriers through Behavioral Reasoning Theory. International Journal of Human–Computer Interaction 2024:1 View
  18. Kong A, Liu N, Tan H, Sia A, Sng B. Artificial intelligence in obstetric anaesthesiology – the future of patient care?. International Journal of Obstetric Anesthesia 2025;61:104288 View
  19. Hassan N, Slight R, Bimpong K, Bates D, Weiand D, Vellinga A, Morgan G, Slight S. Systematic review to understand users perspectives on AI-enabled decision aids to inform shared decision making. npj Digital Medicine 2024;7(1) View

Books/Policy Documents

  1. Barua R. Approaches to Human-Centered AI in Healthcare. View
  2. Shedge P, Mehta P, Vyavahare S, Rupnar V. Modernizing Maternal Care With Digital Technologies. View