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Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review.随机对照试验评估人工智能在临床实践中的应用:范围综述。
Lancet Digit Health. 2024 May;6(5):e367-e373. doi: 10.1016/S2589-7500(24)00047-5.
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TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.TRIPOD+AI 声明:报告使用回归或机器学习方法的临床预测模型的更新指南。
BMJ. 2024 Apr 16;385:e078378. doi: 10.1136/bmj-2023-078378.
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Reporting Use of AI in Research and Scholarly Publication-JAMA Network Guidance.《研究与学术出版中人工智能的报告——美国医学会杂志网络指南》
JAMA. 2024 Apr 2;331(13):1096-1098. doi: 10.1001/jama.2024.3471.
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A comparison of performance between a deep learning model with residents for localization and classification of intracranial hemorrhage.深度学习模型与住院医师在颅内出血定位和分类中的表现比较。
Sci Rep. 2023 Jun 20;13(1):9975. doi: 10.1038/s41598-023-37114-z.
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AI Reporting Guidelines: How to Select the Best One for Your Research.人工智能报告指南:如何为你的研究选择最佳指南。
Radiol Artif Intell. 2023 Apr 5;5(3):e230055. doi: 10.1148/ryai.230055. eCollection 2023 May.
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Enhancing physicians' radiology diagnostics of COVID-19's effects on lung health by leveraging artificial intelligence.通过利用人工智能提高医生对新冠病毒对肺部健康影响的放射学诊断水平。
Front Bioeng Biotechnol. 2023 Apr 20;11:1010679. doi: 10.3389/fbioe.2023.1010679. eCollection 2023.
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An AI based classifier model for lateral pillar classification of Legg-Calve-Perthes.基于人工智能的 Legg-Calve-Perthes 病外侧柱分型分类器模型
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Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review.机器学习预测模型研究中肿瘤学发现的过度解读:系统评价。
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9
Deep Learning Models for Cystoscopic Recognition of Hunner Lesion in Interstitial Cystitis.用于间质性膀胱炎中Hunner病变膀胱镜识别的深度学习模型
Eur Urol Open Sci. 2023 Jan 26;49:44-50. doi: 10.1016/j.euros.2022.12.012. eCollection 2023 Mar.
10
Paediatric sleep apnea event prediction using nasal air pressure and machine learning.使用鼻腔气压和机器学习预测儿科睡眠呼吸暂停事件。
J Sleep Res. 2023 Aug;32(4):e13851. doi: 10.1111/jsr.13851. Epub 2023 Feb 20.

关于报告和评估人工智能表现优于人类医生的声明的伦理指南。

Ethical guidance for reporting and evaluating claims of AI outperforming human doctors.

作者信息

Drogt Jojanneke, Milota Megan, van den Brink Anne, Jongsma Karin

机构信息

University Medical Center, Utrecht, The Netherlands.

出版信息

NPJ Digit Med. 2024 Oct 2;7(1):271. doi: 10.1038/s41746-024-01255-w.

DOI:10.1038/s41746-024-01255-w
PMID:39358556
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11447248/
Abstract

Claims of AI outperforming medical practitioners are under scrutiny, as the evidence supporting many of these claims is not convincing or transparently reported. These claims often lack specificity, contextualization, and empirical grounding. In this comment, we offer constructive ethical guidance that can benefit authors, journal editors, and peer reviewers when reporting and evaluating findings in studies comparing AI to physician performance. The guidance provided here forms an essential addition to current reporting guidelines for healthcare studies using machine learning.

摘要

人工智能表现优于医学从业者的说法正在接受审查,因为支持其中许多说法的证据并不令人信服,也没有得到透明的报告。这些说法往往缺乏具体性、背景信息和实证依据。在这篇评论中,我们提供了建设性的伦理指导,在报告和评估比较人工智能与医生表现的研究结果时,这对作者、期刊编辑和同行评审人员都有益处。这里提供的指导是对当前使用机器学习的医疗保健研究报告指南的重要补充。