Institute of Health and Nursing Sciences, Martin Luther University Halle-Wittenberg, Magdeburger Str. 8, Halle, 06112, Germany.
Harding Center for Risk Literacy, Faculty of Health Sciences Brandenburg, University of Potsdam, Virchowstr. 2, Potsdam, 14482, Germany.
Syst Rev. 2024 Sep 6;13(1):228. doi: 10.1186/s13643-024-02646-6.
Algorithmic decision-making (ADM) utilises algorithms to collect and process data and develop models to make or support decisions. Advances in artificial intelligence (AI) have led to the development of support systems that can be superior to medical professionals without AI support in certain tasks. However, whether patients can benefit from this remains unclear. The aim of this systematic review is to assess the current evidence on patient-relevant benefits and harms, such as improved survival rates and reduced treatment-related complications, when healthcare professionals use ADM systems (developed using or working with AI) compared to healthcare professionals without AI-related ADM (standard care)-regardless of the clinical issues.
Following the PRISMA statement, MEDLINE and PubMed (via PubMed), Embase (via Elsevier) and IEEE Xplore will be searched using English free text terms in title/abstract, Medical Subject Headings (MeSH) terms and Embase Subject Headings (Emtree fields). Additional studies will be identified by contacting authors of included studies and through reference lists of included studies. Grey literature searches will be conducted in Google Scholar. Risk of bias will be assessed by using Cochrane's RoB 2 for randomised trials and ROBINS-I for non-randomised trials. Transparent reporting of the included studies will be assessed using the CONSORT-AI extension statement. Two researchers will screen, assess and extract from the studies independently, with a third in case of conflicts that cannot be resolved by discussion.
It is expected that there will be a substantial shortage of suitable studies that compare healthcare professionals with and without ADM systems concerning patient-relevant endpoints. This can be attributed to the prioritisation of technical quality criteria and, in some cases, clinical parameters over patient-relevant endpoints in the development of study designs. Furthermore, it is anticipated that a significant portion of the identified studies will exhibit relatively poor methodological quality and provide only limited generalisable results.
This study is registered within PROSPERO (CRD42023412156).
算法决策（ADM）利用算法收集和处理数据，并开发模型来做出或支持决策。人工智能（AI）的进步导致了支持系统的发展，这些系统在某些任务上可以优于没有 AI 支持的医疗专业人员。然而，患者是否能从中受益尚不清楚。本系统评价的目的是评估当前关于患者相关益处和危害的证据，例如提高生存率和减少与治疗相关的并发症，当医疗保健专业人员使用 ADM 系统（使用或与 AI 合作开发）与没有 AI 相关 ADM（标准护理）的医疗保健专业人员相比——无论临床问题如何。
根据 PRISMA 声明，将使用英语自由文本在标题/摘要、医学主题词（MeSH）术语和 Embase 主题词（Emtree 字段）中搜索 MEDLINE 和 PubMed（通过 PubMed）、Embase（通过 Elsevier）和 IEEE Xplore。通过联系纳入研究的作者和纳入研究的参考文献列表，将确定其他研究。将在 Google Scholar 中进行灰色文献搜索。将使用 Cochrane 的 RoB 2 对随机试验和 ROBINS-I 对非随机试验评估偏倚风险。将使用 CONSORT-AI 扩展声明评估纳入研究的透明报告。两名研究人员将独立筛选、评估和提取研究，如有无法通过讨论解决的冲突，则由第三名研究人员介入。
预计将有大量比较具有和不具有 ADM 系统的医疗保健专业人员的研究，这些研究涉及患者相关终点。这归因于在研究设计的开发中，技术质量标准和在某些情况下临床参数优先于患者相关终点。此外，预计相当一部分已确定的研究将具有相对较差的方法学质量，并仅提供有限的可推广结果。
本研究在 PROSPERO（CRD42023412156）中注册。
