@文章{info:doi/10.2196/36199，作者="Abbasgholizadeh Rahimi, Samira和Cwintal, Michelle和Huang, Yuhui和Ghadiri, Pooria和Grad, Roland和Poenaru, Dan和Gore, Genevieve和Zomahoun, Herv{\'e} Tchala Vignon和L{\'e}gar{\'e}， France and Pluye, Pierre"，标题="人工智能在共享决策中的应用:范围审查"，期刊="JMIR Med Inform"，年="2022"，月="Aug"，日="9"，卷="10"，数="8"，页数="e36199"，关键词="人工智能;机器学习;共同决策;以病人为中心的护理;背景:人工智能(AI)在医学的各个领域都显示出了可喜的成果。它具有促进共享决策(SDM)的潜力。然而，目前还没有关于人工智能如何用于SDM的全面映射。目的:我们旨在识别和评估已发表的测试或实施人工智能以促进SDM的研究。方法:我们根据Levac等人提出的方法学框架、对最初Arksey和O'Malley框架的修改以及Joanna Briggs研究所的范围评估框架进行了范围评估。我们根据PRISMA-ScR(系统评价的首选报告项目和范围评价的元分析扩展)报告指南报告了我们的结果。 At the identification stage, an information specialist performed a comprehensive search of 6 electronic databases from their inception to May 2021. The inclusion criteria were: all populations; all AI interventions that were used to facilitate SDM, and if the AI intervention was not used for the decision-making point in SDM, it was excluded; any outcome related to patients, health care providers, or health care systems; studies in any health care setting, only studies published in the English language, and all study types. Overall, 2 reviewers independently performed the study selection process and extracted data. Any disagreements were resolved by a third reviewer. A descriptive analysis was performed. Results: The search process yielded 1445 records. After removing duplicates, 894 documents were screened, and 6 peer-reviewed publications met our inclusion criteria. Overall, 2 of them were conducted in North America, 2 in Europe, 1 in Australia, and 1 in Asia. Most articles were published after 2017. Overall, 3 articles focused on primary care, and 3 articles focused on secondary care. All studies used machine learning methods. Moreover, 3 articles included health care providers in the validation stage of the AI intervention, and 1 article included both health care providers and patients in clinical validation, but none of the articles included health care providers or patients in the design and development of the AI intervention. All used AI to support SDM by providing clinical recommendations or predictions. Conclusions: Evidence of the use of AI in SDM is in its infancy. We found AI supporting SDM in similar ways across the included articles. We observed a lack of emphasis on patients' values and preferences, as well as poor reporting of AI interventions, resulting in a lack of clarity about different aspects. Little effort was made to address the topics of explainability of AI interventions and to include end-users in the design and development of the interventions. Further efforts are required to strengthen and standardize the use of AI in different steps of SDM and to evaluate its impact on various decisions, populations, and settings. ", issn="2291-9694", doi="10.2196/36199", url="https://medinform.www.mybigtv.com/2022/8/e36199", url="https://doi.org/10.2196/36199", url="http://www.ncbi.nlm.nih.gov/pubmed/35943" }