Data Ownership in the AI-Powered Integrative Health Care Landscape.

IF 3.1 3区医学 Q2 MEDICAL INFORMATICS

JMIR Medical Informatics Pub Date : 2024-11-19 DOI:10.2196/57754

Shuimei Liu, L Raymond Guo

{"title":"Data Ownership in the AI-Powered Integrative Health Care Landscape.","authors":"Shuimei Liu, L Raymond Guo","doi":"10.2196/57754","DOIUrl":null,"url":null,"abstract":"<p><p>In the rapidly advancing landscape of artificial intelligence (AI) within integrative health care (IHC), the issue of data ownership has become pivotal. This study explores the intricate dynamics of data ownership in the context of IHC and the AI era, presenting the novel Collaborative Healthcare Data Ownership (CHDO) framework. The analysis delves into the multifaceted nature of data ownership, involving patients, providers, researchers, and AI developers, and addresses challenges such as ambiguous consent, attribution of insights, and international inconsistencies. Examining various ownership models, including privatization and communization postulates, as well as distributed access control, data trusts, and blockchain technology, the study assesses their potential and limitations. The proposed CHDO framework emphasizes shared ownership, defined access and control, and transparent governance, providing a promising avenue for responsible and collaborative AI integration in IHC. This comprehensive analysis offers valuable insights into the complex landscape of data ownership in IHC and the AI era, potentially paving the way for ethical and sustainable advancements in data-driven health care.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"12 ","pages":"e57754"},"PeriodicalIF":3.1000,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Medical Informatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2196/57754","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}

引用次数: 0

Abstract

In the rapidly advancing landscape of artificial intelligence (AI) within integrative health care (IHC), the issue of data ownership has become pivotal. This study explores the intricate dynamics of data ownership in the context of IHC and the AI era, presenting the novel Collaborative Healthcare Data Ownership (CHDO) framework. The analysis delves into the multifaceted nature of data ownership, involving patients, providers, researchers, and AI developers, and addresses challenges such as ambiguous consent, attribution of insights, and international inconsistencies. Examining various ownership models, including privatization and communization postulates, as well as distributed access control, data trusts, and blockchain technology, the study assesses their potential and limitations. The proposed CHDO framework emphasizes shared ownership, defined access and control, and transparent governance, providing a promising avenue for responsible and collaborative AI integration in IHC. This comprehensive analysis offers valuable insights into the complex landscape of data ownership in IHC and the AI era, potentially paving the way for ethical and sustainable advancements in data-driven health care.

查看原文本刊更多论文

人工智能驱动的综合医疗保健领域的数据所有权。

在综合医疗保健（IHC）领域人工智能（AI）快速发展的背景下，数据所有权问题变得至关重要。本研究探讨了 IHC 和人工智能时代背景下数据所有权的复杂动态，提出了新颖的协作式医疗保健数据所有权（CHDO）框架。该分析深入探讨了数据所有权的多面性，涉及患者、医疗服务提供者、研究人员和人工智能开发人员，并解决了诸如模糊同意、见解归属和国际不一致性等挑战。研究探讨了各种所有权模式，包括私有化和共用化假设，以及分布式访问控制、数据信托和区块链技术，并评估了它们的潜力和局限性。拟议的 CHDO 框架强调共享所有权、明确的访问和控制以及透明的治理，为负责任、协作性地将人工智能整合到 IHC 中提供了一条大有可为的途径。这项全面的分析为了解 IHC 和人工智能时代数据所有权的复杂情况提供了宝贵的见解，有可能为数据驱动的医疗保健的道德和可持续发展铺平道路。

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分钟内获得全文求助全文

来源期刊

JMIR Medical Informatics Medicine-Health Informatics

CiteScore

7.90

自引率

3.10%

发文量

173

审稿时长

12 weeks

期刊介绍： JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals. Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.