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

Journals

  1. Saukkonen P, Elovainio M, Virtanen L, Kaihlanen A, Nadav J, Lääveri T, Vänskä J, Viitanen J, Reponen J, Heponiemi T. The Interplay of Work, Digital Health Usage, and the Perceived Effects of Digitalization on Physicians’ Work: Network Analysis Approach. Journal of Medical Internet Research 2022;24(8):e38714 View
  2. Roy S, Meena T, Lim S. Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine. Diagnostics 2022;12(10):2549 View
  3. Idnay B, Fang Y, Dreisbach C, Marder K, Weng C, Schnall R. Clinical research staff perceptions on a natural language processing-driven tool for eligibility prescreening: An iterative usability assessment. International Journal of Medical Informatics 2023;171:104985 View
  4. Stankovic S, Macq B, Bernardini S, Gouget B, Homsak E, Dabla P, Gruson D. Artificial intelligence and thyroid disease management. Biochemia medica 2022;32(2):182 View
  5. Ogundipe A, Sim T, Emmerton L. Health information communication technology evaluation frameworks for pharmacist prescribing: A systematic scoping review. Research in Social and Administrative Pharmacy 2023;19(2):218 View
  6. Kim K, Sohn M, Park C. Conformity assessment of a computer vision-based posture analysis system for the screening of postural deformation. BMC Musculoskeletal Disorders 2022;23(1) View
  7. Tegenaw G, Amenu D, Ketema G, Verbeke F, Cornelis J, Jansen B. Evaluating a clinical decision support point of care instrument in low resource setting. BMC Medical Informatics and Decision Making 2023;23(1) View
  8. He X, Zheng X, Ding H, Liu Y, Zhu H. AI-CDSS Design Guidelines and Practice Verification. International Journal of Human–Computer Interaction 2023:1 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

Books/Policy Documents

  1. Barua R. Approaches to Human-Centered AI in Healthcare. View