TY - JOUR AU - Abbasgholizadeh Rahimi, Samira AU - Cwintal, Michelle AU - Huang, Yuhui AU - Ghadiri, Pooria AU - Grad, Roland AU - Poenaru, Dan AU - Gore, Genevieve AU - Zomahoun, Hervé Tchala Vignon AU - Légaré，法国AU - Pluye, Pierre PY - 2022 DA - 2022/8/9 TI -人工智能在共享决策中的应用:范围综述JO - JMIR Med Inform SP - e36199 VL - 10 IS - 8 KW -人工智能KW -机器学习KW -共享决策KW -以患者为中心的护理KW -范围综述AB -背景:人工智能(AI)在医学的各个领域都显示出了有前景的结果。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。在识别阶段，一名信息专家对6个电子数据库从成立到2021年5月进行了全面搜索。纳入标准为:所有人群;所有用于促进SDM的AI干预，如果AI干预没有用于SDM的决策点，则将其排除在外; 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. SN - 2291-9694 UR - https://medinform.www.mybigtv.com/2022/8/e36199 UR - https://doi.org/10.2196/36199 UR - http://www.ncbi.nlm.nih.gov/pubmed/35943 DO - 10.2196/36199 ID - info:doi/10.2196/36199 ER -