|
1. Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ. 2020; 368. doi.10.1136/bmj.m689 PMid:32213531 PMCid:PMC7190037 2. Stipelman CH, Kukhareva PV, Trepman E, Nguyen QT, Valdez L, Kenost C, et al. Electronic health record-integrated clinical decision support for clinicians serving populations facing health care disparities: literature review. Yearb Med Inform. 2022; 31(01):184-98. doi.10.1055/s-0042-1742518 PMid:36463877 PMCid:PMC9719761 3. Shawahna R. Merits, features, and desiderata to be considered when developing electronic health records with embedded clinical decision support systems in Palestinian hospitals: a consensus study. BMC Med Inform Decis Mak. 2019; 19(1):216. doi.10.1186/s12911-019-0928-3 PMid:31703675 PMCid:PMC6842153 4. ShojaeiBaghini M, Ghaemi MM, Ahmadipour A. Artificial intelligence in the identification and prediction of adverse transfusion reactions(ATRs) and implications for clinical management: a systematic review of models and applications. BMC Med Inform Decis Mak. 2025;25(1):396. doi.10.1186/s12911-025-03232-z PMid:41152861 PMCid:PMC12560389 5. Sounderajah V, Ashrafian H, Rose S, Shah NH, Ghassemi M, Golub R, et al. A Quality Assessment Tool for Artificial Intelligence-Centered Diagnostic Test Accuracy Studies: Quadas-Ai. Nature Med. 2021; 27(10):1663-5. doi.10.1038/s41591-021-01517-0 PMid:34635854 6. Smith H. Clinical AI: opacity, accountability, responsibility and liability. Ai & Society. 2021; 36(2): 535-45. doi.10.1007/s00146-020-01019-6 7. Gharib M, Bondavalli A, editors. On the evaluation measures for machine learning algorithms for safety-critical systems. 2019 15th European Dependable Computing Conference (EDCC); 2019: IEEE. doi.10.1109/EDCC.2019.00035 8. Marcilly R, Colliaux J, Payen A, Beuscart J-B. Considering work systems and processes in assessing the impact of a CDSS intervention: preliminary results. Context Sensitive Health Informatics and the Pandemic Boost: IOS Press; 2023. p. 52-6. doi.10.3233/SHTI230368 9. Oyeniran C, Adewusi AO, Adeleke AG, Akwawa LA, Azubuko CF. Ethical AI: Addressing bias in machine learning models and software applications. Computer Sci IT Res J. 2022; 3(3):115-26. doi.10.51594/csitrj.v3i3.1559 10. Mukhiya SK, Lamo Y. An HL7 FHIR and GraphQL approach for interoperability between heterogeneous Electronic Health Record systems. Health Inform J. 2021; 27(3): 14604582211043920. doi.10.1177/14604582211043920 PMid:34524029 11. Schöning J, Kruse N. Compliance of AI Systems. arXiv preprint arXiv:250305571. 2025. 12. Muhiyaddin R, Abd-Alrazaq AA, Househ M, Alam T, Shah Z. The impact of clinical decision support systems (CDSS) on physicians: a scoping review. Stud Health Technol Inform. 2020:470-3. doi.10.3233/SHTI200597 13. Newton N, Bamgboje-Ayodele A, Forsyth R, Tariq A, Baysari MT. How are clinicians' acceptance and use of clinical decision support systems evaluated over time? A systematic review. Stud Health Technol Inform. 2024; 259-63. doi.10.3233/SHTI230967 14. Mammen JJ, Asirvatham ES, Sarman CJ, Ranjan V, Charles B. A review of legal, regulatory, and policy aspects of blood transfusion services in India: Issues, challenges, and opportunities. Asian J Transfus Sci. 2021; 15(2):204-11. doi.10.4103/ajts.AJTS_65_20 PMid:34908756 PMCid:PMC8628249 15. Aliabad MB. Examination of Electronic Health Records in Iran: Legal Requirements and Implementation Challenges. 2022. 16. Nader M, Haleh A, Hamid H. Examining the Barriers to the Creation and Implementation of Electronic Health Records in Iran. 2013.
|