دورية أكاديمية
Global healthcare fairness: We should be sharing more, not less, data.
| العنوان: | Global healthcare fairness: We should be sharing more, not less, data. |
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| المؤلفون: | Seastedt KP; Beth Israel Deaconess Medical Center, Department of Surgery, Harvard Medical School, Boston, Massachusetts, United States of America., Schwab P; GlaxoSmithKline, Artificial Intelligence & Machine Learning, Zug, Switzerland., O'Brien Z; Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia., Wakida E; Mbarara University of Science and Technology, Mbarara, Uganda., Herrera K; Quality and Patient Safety, Hospital Militar, Managua, Nicaragua., Marcelo PGF; Department of Family & Community Medicine, University of the Philippines, Manila, Philippines., Agha-Mir-Salim L; Institute of Medical Informatics, Charité-Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Berlin, Germany.; Laboratory for Computational Physiology, Harvard-MIT Division of Health Sciences & Technology, Cambridge, Massachusetts, United States of America., Frigola XB; Laboratory for Computational Physiology, Harvard-MIT Division of Health Sciences & Technology, Cambridge, Massachusetts, United States of America.; Anesthesiology and Critical Care Department, Hospital Clinic de Barcelona, Barcelona, Spain., Ndulue EB; Department of Journalism, Northeastern University, Boston, Massachusetts, United States of America., Marcelo A; Department of Surgery, University of the Philippines, Manila, Philippines., Celi LA; Laboratory for Computational Physiology, Harvard-MIT Division of Health Sciences & Technology, Cambridge, Massachusetts, United States of America.; Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States of America.; Department of Biostatistics Harvard T.H, Chan School of Public Health, Boston, Massachusetts, United States of America. |
| المصدر: | PLOS digital health [PLOS Digit Health] 2022 Oct 06; Vol. 1 (10), pp. e0000102. Date of Electronic Publication: 2022 Oct 06 (Print Publication: 2022). |
| نوع المنشور: | Journal Article; Review |
| اللغة: | English |
| بيانات الدورية: | Publisher: PLOS Country of Publication: United States NLM ID: 9918335064206676 Publication Model: eCollection Cited Medium: Internet ISSN: 2767-3170 (Electronic) Linking ISSN: 27673170 NLM ISO Abbreviation: PLOS Digit Health Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: San Francisco, California : PLOS, [2022]- |
| مستخلص: | The availability of large, deidentified health datasets has enabled significant innovation in using machine learning (ML) to better understand patients and their diseases. However, questions remain regarding the true privacy of this data, patient control over their data, and how we regulate data sharing in a way that that does not encumber progress or further potentiate biases for underrepresented populations. After reviewing the literature on potential reidentifications of patients in publicly available datasets, we argue that the cost-measured in terms of access to future medical innovations and clinical software-of slowing ML progress is too great to limit sharing data through large publicly available databases for concerns of imperfect data anonymization. This cost is especially great for developing countries where the barriers preventing inclusion in such databases will continue to rise, further excluding these populations and increasing existing biases that favor high-income countries. Preventing artificial intelligence's progress towards precision medicine and sliding back to clinical practice dogma may pose a larger threat than concerns of potential patient reidentification within publicly available datasets. While the risk to patient privacy should be minimized, we believe this risk will never be zero, and society has to determine an acceptable risk threshold below which data sharing can occur-for the benefit of a global medical knowledge system. Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Leo Anthony Celi is the Editor-in Chief of PLOS Digital Health. (Copyright: © 2022 Seastedt et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
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| معلومات مُعتمدة: | R01 EB017205 United States EB NIBIB NIH HHS |
| تواريخ الأحداث: | Date Created: 20230222 Latest Revision: 20240426 |
| رمز التحديث: | 20240426 |
| مُعرف محوري في PubMed: | PMC9931202 |
| DOI: | 10.1371/journal.pdig.0000102 |
| PMID: | 36812599 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 2767-3170 |
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| DOI: | 10.1371/journal.pdig.0000102 |