Road map for clinicians to develop and evaluate AI predictive models to inform clinical decision-making.

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES BMJ Health & Care Informatics Pub Date : 2023-08-01 DOI:10.1136/bmjhci-2023-100784
Nehal Hassan, Robert Slight, Graham Morgan, David W Bates, Suzy Gallier, Elizabeth Sapey, Sarah Slight
{"title":"Road map for clinicians to develop and evaluate AI predictive models to inform clinical decision-making.","authors":"Nehal Hassan, Robert Slight, Graham Morgan, David W Bates, Suzy Gallier, Elizabeth Sapey, Sarah Slight","doi":"10.1136/bmjhci-2023-100784","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Predictive models have been used in clinical care for decades. They can determine the risk of a patient developing a particular condition or complication and inform the shared decision-making process. Developing artificial intelligence (AI) predictive models for use in clinical practice is challenging; even if they have good predictive performance, this does not guarantee that they will be used or enhance decision-making. We describe nine stages of developing and evaluating a predictive AI model, recognising the challenges that clinicians might face at each stage and providing practical tips to help manage them.</p><p><strong>Findings: </strong>The nine stages included clarifying the clinical question or outcome(s) of interest (output), identifying appropriate predictors (features selection), choosing relevant datasets, developing the AI predictive model, validating and testing the developed model, presenting and interpreting the model prediction(s), licensing and maintaining the AI predictive model and evaluating the impact of the AI predictive model. The introduction of an AI prediction model into clinical practice usually consists of multiple interacting components, including the accuracy of the model predictions, physician and patient understanding and use of these probabilities, expected effectiveness of subsequent actions or interventions and adherence to these. Much of the difference in whether benefits are realised relates to whether the predictions are given to clinicians in a timely way that enables them to take an appropriate action.</p><p><strong>Conclusion: </strong>The downstream effects on processes and outcomes of AI prediction models vary widely, and it is essential to evaluate the use in clinical practice using an appropriate study design.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"30 1","pages":""},"PeriodicalIF":4.1000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/4a/36/bmjhci-2023-100784.PMC10414079.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMJ Health & Care Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1136/bmjhci-2023-100784","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Predictive models have been used in clinical care for decades. They can determine the risk of a patient developing a particular condition or complication and inform the shared decision-making process. Developing artificial intelligence (AI) predictive models for use in clinical practice is challenging; even if they have good predictive performance, this does not guarantee that they will be used or enhance decision-making. We describe nine stages of developing and evaluating a predictive AI model, recognising the challenges that clinicians might face at each stage and providing practical tips to help manage them.

Findings: The nine stages included clarifying the clinical question or outcome(s) of interest (output), identifying appropriate predictors (features selection), choosing relevant datasets, developing the AI predictive model, validating and testing the developed model, presenting and interpreting the model prediction(s), licensing and maintaining the AI predictive model and evaluating the impact of the AI predictive model. The introduction of an AI prediction model into clinical practice usually consists of multiple interacting components, including the accuracy of the model predictions, physician and patient understanding and use of these probabilities, expected effectiveness of subsequent actions or interventions and adherence to these. Much of the difference in whether benefits are realised relates to whether the predictions are given to clinicians in a timely way that enables them to take an appropriate action.

Conclusion: The downstream effects on processes and outcomes of AI prediction models vary widely, and it is essential to evaluate the use in clinical practice using an appropriate study design.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
临床医生开发和评估人工智能预测模型的路线图,为临床决策提供依据。
背景:预测模型用于临床护理已有几十年的历史。它们可以确定患者罹患某种疾病或并发症的风险,并为共同决策过程提供信息。开发用于临床实践的人工智能(AI)预测模型具有挑战性;即使这些模型具有良好的预测性能,也不能保证它们会被使用或促进决策。我们描述了开发和评估人工智能预测模型的九个阶段,认识到临床医生在每个阶段可能面临的挑战,并提供了帮助应对这些挑战的实用技巧:这九个阶段包括明确临床问题或感兴趣的结果(输出)、确定适当的预测因子(特征选择)、选择相关数据集、开发人工智能预测模型、验证和测试所开发的模型、展示和解释模型预测结果、许可和维护人工智能预测模型以及评估人工智能预测模型的影响。将人工智能预测模型引入临床实践通常由多个相互作用的部分组成,包括模型预测的准确性、医生和患者对这些概率的理解和使用、后续行动或干预的预期效果以及对这些行动或干预的坚持。效益是否实现的很大程度上取决于预测是否及时提供给临床医生,使他们能够采取适当的行动:人工智能预测模型对流程和结果的下游影响差异很大,因此必须采用适当的研究设计来评估其在临床实践中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
期刊最新文献
Scaling equitable artificial intelligence in healthcare with machine learning operations. Understanding prescribing errors for system optimisation: the technology-related error mechanism classification. Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan. PubMed captures more fine-grained bibliographic data on scientific commentary than Web of Science: a comparative analysis. Method to apply temporal graph analysis on electronic patient record data to explore healthcare professional-patient interaction intensity: a cohort study.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1