Aggarwal, N., Ahmed, M., Basu, S., Curtin, J. J., Evans, B. J., Matheny, M. E., Nundy, S., Sendak, M. P., Shachar, C., & Shah, R. U. (2020). Advancing artificial intelligence in health settings outside the hospital and clinic. NAM perspectives, 2020.
Ahmad, S., & Wasim, S. (2023). Prevent medical errors through artificial intelligence: A review. Saudi J Med Pharm Sci, 9(7), 419-423.
Alfieri, F., Ancona, A., Tripepi, G., Crosetto, D., Randazzo, V., Paviglianiti, A., Pasero, E., Vecchi, L., Cauda, V., & Fagugli, R. M. (2021). A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients. Journal of nephrology, 34(6), 1875-1886.
Article PubMed PubMed Central Google Scholar
Alharbi, A. I., Gay, V., AlGhamdi, M. J., Alturki, R., & Alyamani, H. J. (2021). Towards an application helping to minimize medication error rate. Mobile Information Systems, 2021(1), 9221005.
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., & Badreldin, H. A. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC medical education, 23(1), 689.
Article PubMed PubMed Central Google Scholar
Baron, J. M., Huang, R., McEvoy, D., & Dighe, A. S. (2021). Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alerts. JAMIA open, 4(1), ooab006.
Article PubMed PubMed Central Google Scholar
Chen, Z., Liang, N., Zhang, H., Li, H., Yang, Y., Zong, X., Chen, Y., Wang, Y., & Shi, N. (2023). Harnessing the power of clinical decision support systems: challenges and opportunities. Open Heart, 10(2), e002432.
Article PubMed PubMed Central Google Scholar
Comito, C., Falcone, D., & Forestiero, A. (2022). AI-driven clinical decision support: enhancing disease diagnosis exploiting patients similarity. IEEE Access, 10, 6878-6888.
Corny, J., Rajkumar, A., Martin, O., Dode, X., Lajonchère, J.-P., Billuart, O., Bézie, Y., & Buronfosse, A. (2020). A machine learning–based clinical decision support system to identify prescriptions with a high risk of medication error. Journal of the American Medical Informatics Association, 27(11), 1688-1694.
Article PubMed PubMed Central Google Scholar
Datta, S., Loftus, T. J., Ruppert, M. M., Giordano, C., Upchurch Jr, G. R., Rashidi, P., ... & Bihorac, A. (2020). Added value of intraoperative data for predicting postoperative complications: the MySurgeryRisk PostOp extension. Journal of Surgical Research, 254, 350–363.
De Sousa Barroca, J. D. (2021). Verification and validation of knowledge-based clinical decision support systems-a practical approach: A descriptive case study at Cambio CDS. In.
Ghanem, M., Ghaith, A. K., & Bydon, M. (2024). Artificial intelligence and personalized medicine: transforming patient care. In The New Era of Precision Medicine (pp. 131–142). Elsevier.
Giordano, C., Brennan, M., Mohamed, B., Rashidi, P., Modave, F., & Tighe, P. (2021). Accessing artificial intelligence for clinical decision-making. Frontiers in digital health, 3, 645232.
Article PubMed PubMed Central Google Scholar
Gkontra, P., Quaglio, G., Garmendia, A. T., & Lekadir, K. (2023). Challenges of machine learning and AI (What Is Next?), Responsible and ethical AI. In Clinical Applications of Artificial Intelligence in Real-World Data (pp. 263–285). Springer.
Gong, K., Lee, H. K., Yu, K., Xie, X., & Li, J. (2021). A prediction and interpretation framework of acute kidney injury in critical care. Journal of Biomedical Informatics, 113, 103653.
Hasan, M. M., Young, G. J., Shi, J., Mohite, P., Young, L. D., Weiner, S. G., & Noor-E-Alam, M. (2021). A machine learning based two-stage clinical decision support system for predicting patients’ discontinuation from opioid use disorder treatment: retrospective observational study. BMC Medical Informatics and Decision Making, 21, 1-21.
Huang, S., Yang, J., Fong, S., & Zhao, Q. (2020). Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer letters, 471, 61-71.
Article CAS PubMed Google Scholar
Jones, C., Thornton, J., & Wyatt, J. C. (2023). Artificial intelligence and clinical decision support: clinicians’ perspectives on trust, trustworthiness, and liability. Medical law review, 31(4), 501-520.
Article PubMed PubMed Central Google Scholar
Kaplan, A., Cao, H., FitzGerald, J. M., Iannotti, N., Yang, E., Kocks, J. W., Kostikas, K., Price, D., Reddel, H. K., & Tsiligianni, I. (2021). Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis. The Journal of Allergy and Clinical Immunology: In Practice, 9(6), 2255-2261.
Kim, K., Yang, H., Yi, J., Son, H.-E., Ryu, J.-Y., Kim, Y. C., Jeong, J. C., Chin, H. J., Na, K. Y., & Chae, D.-W. (2021). Real-time clinical decision support based on recurrent neural networks for in-hospital acute kidney injury: external validation and model interpretation. Journal of Medical Internet Research, 23(4), e24120.
Article PubMed PubMed Central Google Scholar
Loftus, T. J., Shickel, B., Ozrazgat-Baslanti, T., Ren, Y., Glicksberg, B. S., Cao, J., Singh, K., Chan, L., Nadkarni, G. N., & Bihorac, A. (2022). Artificial intelligence-enabled decision support in nephrology. Nature Reviews Nephrology, 18(7), 452-465.
Article PubMed PubMed Central Google Scholar
Luo, X.-Q., Yan, P., Zhang, N.-Y., Luo, B., Wang, M., Deng, Y.-H., Wu, T., Wu, X., Liu, Q., & Wang, H.-S. (2021). Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis. Scientific reports, 11(1), 20269.
Article CAS PubMed PubMed Central Google Scholar
Magrabi, F., Ammenwerth, E., McNair, J. B., De Keizer, N. F., Hyppönen, H., Nykänen, P., Rigby, M., Scott, P. J., Vehko, T., & Wong, Z. S.-Y. (2019). Artificial intelligence in clinical decision support: challenges for evaluating AI and practical implications. Yearbook of medical informatics, 28(01), 128-134.
Article PubMed PubMed Central Google Scholar
Mahyoub, M. A., Yadav, R. R., Dougherty, K., & Shukla, A. (2023). Development and validation of a machine learning model integrated with the clinical workflow for early detection of sepsis. Frontiers in Medicine, 10, 1–10.
Moazemi, S., Vahdati, S., Li, J., Kalkhoff, S., Castano, L. J., Dewitz, B., Bibo, R., Sabouniaghdam, P., Tootooni, M. S., & Bundschuh, R. A. (2023). Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review. Frontiers in Medicine, 10, 1109411.
Article PubMed PubMed Central Google Scholar
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.
Article CAS PubMed Google Scholar
Panigutti, C., Beretta, A., Giannotti, F., & Pedreschi, D. (2022). Understanding the impact of explanations on advice-taking: a user study for AI-based clinical Decision Support Systems. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, 1–9.
Peng, X., Li, L., Wang, X., & Zhang, H. (2022). A machine learning-based prediction model for acute kidney injury in patients with congestive heart failure. Frontiers in cardiovascular medicine, 9, 842873.
Article PubMed PubMed Central Google Scholar
Raita, Y., Camargo Jr, C. A., Liang, L., & Hasegawa, K. (2021). Big data, data science, and causal inference: A primer for clinicians. Frontiers in Medicine, 8, 678047.
Article PubMed PubMed Central Google Scholar
Ramgopal, S., Sanchez-Pinto, L. N., Horvat, C. M., Carroll, M. S., Luo, Y., & Florin, T. A. (2023). Artificial intelligence-based clinical decision support in pediatrics. Pediatric research, 93(2), 334-341.
Reis, W. C., Bonetti, A. F., Bottacin, W. E., Reis Jr, A. S., Souza, T. T., Pontarolo, R., Correr, C. J., & Fernandez-Llimos, F. (2017). Impact on process results of clinical decision support systems (CDSSs) applied to medication use: overview of systematic reviews. Pharmacy Practice (Granada), 15(4).
Rizzi, D. A. (1993). Medical prognosis—some fundamentals. Theoretical Medicine, 14, 365-375.
Article CAS PubMed Google Scholar
Sardar, P., Abbott, J. D., Kundu, A., Aronow, H. D., Granada, J. F., & Giri, J. (2019). Impact of artificial intelligence on interventional cardiology: from decision-making aid to advanced interventional procedure assistance. Cardiovascular interventions, 12(14), 1293-1303.
Shamszare, H., & Choudhury, A. (2023). Clinicians’ perceptions of artificial intelligence: focus on workload, risk, trust, clinical decision making, and clinical integration. Healthcare, 11(16), 1–15.
Shen, J., Zhang, C. J., Jiang, B., Chen, J., Song, J., Liu, Z., He, Z., Wong, S. Y., Fang, P.-H., & Ming, W.-K. (2019). Artificial intelligence versus clinicians in disease diagnosis: systematic review. JMIR medical informatics, 7(3), e10010.
Article PubMed PubMed Central Google Scholar
Sloane, E. B., & Silva, R. J. (2020). Artificial intelligence in medical devices and clinical decision support systems. In Clinical engineering handbook (pp. 556–568). Elsevier.
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ digital medicine, 3(1), 17.
Article PubMed PubMed Central Google Scholar
Tucci, V., Saary, J., & Doyle, T. E. (2022). Factors influencing trust in medical artificial intelligence for healthcare professionals: A narrative review. Journal of Medical Artificial Intelligence, 5.
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