PDF(Pubmed)
Abstract：
BACKGROUND: Applications of artificial intelligence (AI) are pervasive in modern biomedical science. In fact, research results suggesting algorithms and AI models for different target diseases and conditions are continuously increasing. While this situation undoubtedly improves the outcome of AI models, health care providers are increasingly unsure which AI model to use due to multiple alternatives for a specific target and the \"black box\" nature of AI. Moreover, the fact that studies rarely use guidelines in developing and reporting AI models poses additional challenges in trusting and adapting models for practical implementation.
OBJECTIVE: This review protocol describes the planned steps and methods for a review of the synthesized evidence regarding the quality of available guidelines and frameworks to facilitate AI applications in medicine.
METHODS: We will commence a systematic literature search using medical subject headings terms for medicine, guidelines, and machine learning (ML). All available guidelines, standard frameworks, best practices, checklists, and recommendations will be included, irrespective of the study design. The search will be conducted on web-based repositories such as PubMed, Web of Science, and the EQUATOR (Enhancing the Quality and Transparency of Health Research) network. After removing duplicate results, a preliminary scan for titles will be done by 2 reviewers. After the first scan, the reviewers will rescan the selected literature for abstract review, and any incongruities about whether to include the article for full-text review or not will be resolved by the third and fourth reviewer based on the predefined criteria. A Google Scholar (Google LLC) search will also be performed to identify gray literature. The quality of identified guidelines will be evaluated using the Appraisal of Guidelines, Research, and Evaluation (AGREE II) tool. A descriptive summary and narrative synthesis will be carried out, and the details of critical appraisal and subgroup synthesis findings will be presented.
RESULTS: The results will be reported using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) reporting guidelines. Data analysis is currently underway, and we anticipate finalizing the review by November 2023.
CONCLUSIONS: Guidelines and recommended frameworks for developing, reporting, and implementing AI studies have been developed by different experts to facilitate the reliable assessment of validity and consistent interpretation of ML models for medical applications. We postulate that a guideline supports the assessment of an ML model only if the quality and reliability of the guideline are high. Assessing the quality and aspects of available guidelines, recommendations, checklists, and frameworks-as will be done in the proposed review-will provide comprehensive insights into current gaps and help to formulate future research directions.
UNASSIGNED: DERR1-10.2196/47105.
OBJECTIVE: This review protocol describes the planned steps and methods for a review of the synthesized evidence regarding the quality of available guidelines and frameworks to facilitate AI applications in medicine.
METHODS: We will commence a systematic literature search using medical subject headings terms for medicine, guidelines, and machine learning (ML). All available guidelines, standard frameworks, best practices, checklists, and recommendations will be included, irrespective of the study design. The search will be conducted on web-based repositories such as PubMed, Web of Science, and the EQUATOR (Enhancing the Quality and Transparency of Health Research) network. After removing duplicate results, a preliminary scan for titles will be done by 2 reviewers. After the first scan, the reviewers will rescan the selected literature for abstract review, and any incongruities about whether to include the article for full-text review or not will be resolved by the third and fourth reviewer based on the predefined criteria. A Google Scholar (Google LLC) search will also be performed to identify gray literature. The quality of identified guidelines will be evaluated using the Appraisal of Guidelines, Research, and Evaluation (AGREE II) tool. A descriptive summary and narrative synthesis will be carried out, and the details of critical appraisal and subgroup synthesis findings will be presented.
RESULTS: The results will be reported using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) reporting guidelines. Data analysis is currently underway, and we anticipate finalizing the review by November 2023.
CONCLUSIONS: Guidelines and recommended frameworks for developing, reporting, and implementing AI studies have been developed by different experts to facilitate the reliable assessment of validity and consistent interpretation of ML models for medical applications. We postulate that a guideline supports the assessment of an ML model only if the quality and reliability of the guideline are high. Assessing the quality and aspects of available guidelines, recommendations, checklists, and frameworks-as will be done in the proposed review-will provide comprehensive insights into current gaps and help to formulate future research directions.
UNASSIGNED: DERR1-10.2196/47105.
摘要：
背景：人工智能（AI）的应用在现代生物医学中普遍存在。事实上,研究结果表明，针对不同目标疾病和病症的算法和人工智能模型正在不断增加。虽然这种情况无疑改善了AI模型的结果，由于针对特定目标的多种替代方案以及AI的“黑匣子”性质，医疗保健提供者越来越不确定使用哪种AI模型。此外，事实上，研究很少在开发和报告AI模型时使用指南，这给信任和调整模型以进行实际实施带来了额外的挑战。
目的：本审查方案描述了计划的步骤和方法，用于审查有关现有指南和框架质量的综合证据，以促进AI在医学中的应用。
方法：我们将使用医学主题词进行系统的文献检索，指导方针,机器学习（ML）所有可用的指导方针，标准框架，最佳实践,清单,和建议将包括在内，无论研究设计如何。搜索将在基于网络的存储库中进行，例如PubMed，WebofScience,和EQUATOR（提高健康研究的质量和透明度）网络。删除重复结果后，标题的初步扫描将由2名审稿人完成。第一次扫描后,审稿人将重新扫描选定的文献以进行抽象审查，任何关于是否将文章纳入全文审阅的不一致问题将由第三和第四审阅者根据预定义的标准解决。还将进行Google学者（GoogleLLC）搜索以识别灰色文献。确定的准则的质量将使用评估准则进行评估，Research,和评估(AGREEII)工具。将进行描述性总结和叙述性综合，并将介绍关键评估和分组综合结果的详细信息。
结果：结果将使用PRISMA（系统评价和荟萃分析的首选报告项目）报告指南报告。目前正在进行数据分析,我们预计在2023年11月完成审查。
结论：开发指南和建议框架，reporting,不同的专家开发了人工智能研究，以促进对医学应用ML模型的有效性和一致解释的可靠评估。我们假设，只有在指南的质量和可靠性较高时，指南才支持对ML模型的评估。评估可用指南的质量和方面，recommendations,清单,和框架——正如拟议的审查中所做的那样——将提供对当前差距的全面见解，并有助于制定未来的研究方向。
■DERR1-10.2196/47105。
目的：本审查方案描述了计划的步骤和方法，用于审查有关现有指南和框架质量的综合证据，以促进AI在医学中的应用。
方法：我们将使用医学主题词进行系统的文献检索，指导方针,机器学习（ML）所有可用的指导方针，标准框架，最佳实践,清单,和建议将包括在内，无论研究设计如何。搜索将在基于网络的存储库中进行，例如PubMed，WebofScience,和EQUATOR（提高健康研究的质量和透明度）网络。删除重复结果后，标题的初步扫描将由2名审稿人完成。第一次扫描后,审稿人将重新扫描选定的文献以进行抽象审查，任何关于是否将文章纳入全文审阅的不一致问题将由第三和第四审阅者根据预定义的标准解决。还将进行Google学者（GoogleLLC）搜索以识别灰色文献。确定的准则的质量将使用评估准则进行评估，Research,和评估(AGREEII)工具。将进行描述性总结和叙述性综合，并将介绍关键评估和分组综合结果的详细信息。
结果：结果将使用PRISMA（系统评价和荟萃分析的首选报告项目）报告指南报告。目前正在进行数据分析,我们预计在2023年11月完成审查。
结论：开发指南和建议框架，reporting,不同的专家开发了人工智能研究，以促进对医学应用ML模型的有效性和一致解释的可靠评估。我们假设，只有在指南的质量和可靠性较高时，指南才支持对ML模型的评估。评估可用指南的质量和方面，recommendations,清单,和框架——正如拟议的审查中所做的那样——将提供对当前差距的全面见解，并有助于制定未来的研究方向。
■DERR1-10.2196/47105。
