将人工智能应用于儿童癌症的实用指南:数据收集和人工智能模型实施

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引用次数: 0

摘要

儿童癌症是儿童死亡的主要原因之一,数字医疗数据的可用性不断提高,再加上人工智能(AI)的快速发展,为利用各种数据资源彻底改变儿童癌症的诊断和治疗并最终改善患者预后带来了变革性机遇。然而,要在儿童癌症中有效应用人工智能,就必须严格遵守有关患者数据准备和人工智能模型开发的法规和最佳实践指南。目前,还缺乏专门针对儿科群体的监管和方法指导。本综述旨在填补这一空白。它首先概述了现有的监管框架,然后研究了目前正在使用或可能用于开发儿童癌症人工智能应用的数据类型。这包括传统来源的数据,如患者数据和电子健康记录 (EHR),以及新兴来源的数据,如社交媒体数据和健康的社会决定因素。本综述还概述了收集、处理和共享这些数据的规则和标准。数据收集和再利用需要知情同意和再同意,数据质量、隐私和安全以及数据标准化、协调性和互操作性对数据处理非常重要。此外,本综述还阐明了在儿童癌症和医疗保健领域开发人工智能模型的基本要求和方法。它还强调了人工智能在临床环境中值得信赖、保护隐私、负责和验证的重要性。通过系统地讨论这些关键要素,本综述旨在为人工智能在儿童癌症领域的可靠应用和实施提供全面的知识和实用工具,以提高人工智能的接受度,促进其在儿童癌症领域的广泛应用。这反过来又会改善癌症儿童的诊断、治疗和预后。
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A practical guide to apply AI in childhood cancer: Data collection and AI model implementation
Childhood cancer is a leading cause of death in children, and the increasing availability of digital healthcare data, coupled with rapid progress in artificial intelligence (AI), brings a transformative opportunity to revolutionise its diagnosis, treatment and ultimately improve patient outcomes by leveraging diverse data resources. However, the effective application of AI in childhood cancer requires strict adherence to regulatory and best practice guidelines for patient data preparation and AI model development. Currently, there is a lack of such regulatory and methodological guidance specifically tailored for the paediatric community. This review seeks to address this gap. Beginning with an overview of existing regulatory frameworks, it examines the types of data currently in use or with potential use in developing AI applications for childhood cancer. This encompasses data from traditional sources, such as patient data and electronic health records (EHRs), as well as emerging sources like social media data and social determinants of health. This review also outlines the rules and criteria for collecting, processing, and sharing these data. Informed consent and re-consent are required for data collection and re-use, and data quality, privacy, and security as well as data standardisation, harmonisation and interoperability are important for data processing. Additionally, this review clarifies the essential requirements and methodologies for developing AI models in childhood cancer and healthcare. It also emphasises the importance of AI being trustworthy, protecting privacy, and being accountable and validated in clinical settings. By systematically addressing these key components, this review aims to provide comprehensive knowledge and practical tools for the reliable application and implementation of AI in paediatric cancer to enhance AI acceptance and promote its widespread integration within the childhood cancer community. This, in turn, will lead to improved diagnosis, treatment and outcomes for children with cancer.
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