{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T18:51:33Z","timestamp":1791571893820,"version":"4.3.4"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T00:00:00Z","timestamp":1783123200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"vor","delay-in-days":97,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Large language models (LLMs) have expanded the capabilities of AI and created new opportunities in medicine. To promote the development of safe and innovative digital health tools, there must be public biosignals datasets that encourage research and provide benchmarks. Multiple current examples involve data use agreements that restrict LLM studies. This work presents recommendations to ensure that public releases of biosignals data can ethically facilitate LLM research efforts.<\/jats:p>","DOI":"10.1038\/s41746-026-02872-3","type":"journal-article","created":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:24:25Z","timestamp":1783182265000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LLM research on public biosignals data is needed to protect patients"],"prefix":"10.1038","volume":"9","author":[{"given":"James","family":"Anibal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasmine","family":"Gunkel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hannah","family":"Huth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bradford J.","family":"Wood","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,4]]},"reference":[{"key":"2872_CR1","doi-asserted-by":"publisher","first-page":"9498","DOI":"10.3390\/s23239498","volume":"23","author":"S Shajari","year":"2023","unstructured":"Shajari, S. et al. The emergence of AI-based wearable sensors for digital health technology: a review. Sensors 23, 9498 (2023).","journal-title":"Sensors"},{"key":"2872_CR2","doi-asserted-by":"publisher","DOI":"10.36227\/techrxiv.175606236.62808131\/v1","author":"X Gu","year":"2025","unstructured":"Gu, X. et al. Foundation models for biosignals: a survey. TechRxiv https:\/\/doi.org\/10.36227\/techrxiv.175606236.62808131\/v1 (2025).","journal-title":"TechRxiv"},{"key":"2872_CR3","doi-asserted-by":"crossref","unstructured":"Onnela, J. P. & Rauch, S. L. Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacology 41, 1691\u20131696 (2016).","DOI":"10.1038\/npp.2016.7"},{"key":"2872_CR4","doi-asserted-by":"crossref","unstructured":"Trifan, A. et al. Passive sensing of health outcomes through smartphones: Systematic review of current solutions and possible limitations. JMIR mHealth uHealth 7, e12649 (2019).","DOI":"10.2196\/12649"},{"key":"2872_CR5","doi-asserted-by":"crossref","unstructured":"Topaz, M., Peltonen, L. M., & Zhang, Z. Beyond human ears: navigating the uncharted risks of AI scribes in clinical practice. npj Digi. Med. 8, 569 (2025).","DOI":"10.1038\/s41746-025-01895-6"},{"key":"2872_CR6","doi-asserted-by":"crossref","unstructured":"Qureshi, F. M. et al. Assessing physiologic changes during sexual activity using wearable devices: a pilot study. Int. J. Imp. Res. 35, 761\u2013763 (2023).","DOI":"10.1038\/s41443-023-00702-8"},{"key":"2872_CR7","doi-asserted-by":"publisher","first-page":"e165","DOI":"10.2196\/mhealth.9691","volume":"6","author":"DA Rohani","year":"2018","unstructured":"Rohani, D. A., Faurholt-Jepsen, M., Kessing, L. V. & Bardram, J. E. Correlations between objective behavioral features collected from mobile and wearable devices and depressive mood symptoms in patients with affective disorders: systematic review. JMIR Mhealth Uhealth 6, e165 (2018).","journal-title":"JMIR Mhealth Uhealth"},{"key":"2872_CR8","doi-asserted-by":"crossref","unstructured":"Kounadi, O. & Resch, B. A geoprivacy by design guideline for research campaigns that use participatory sensing data. J. Empir. Res. Hum. Res. Ethics 13, 203\u2013222 (2018).","DOI":"10.1177\/1556264618759877"},{"key":"2872_CR9","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1038\/s44387-026-00094-2","volume":"2","author":"A Karpurapu","year":"2026","unstructured":"Karpurapu, A., Guo, Z., Hu, X. & Rudin, C. Data-hugging shields proprietary AI models from research that could disprove them. npj Artif. Intell. 2, 45 (2026).","journal-title":"npj Artif. Intell."},{"key":"2872_CR10","doi-asserted-by":"publisher","DOI":"10.13026\/kpb9-mt58","author":"A Johnson","year":"2024","unstructured":"Johnson, A. et al. MIMIC-IV (version 3.1). PhysioNet https:\/\/doi.org\/10.13026\/kpb9-mt58 (2024).","journal-title":"PhysioNet"},{"key":"2872_CR11","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01899-x","volume":"10","author":"AEW Johnson","year":"2023","unstructured":"Johnson, A. E. W. et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci. Data 10, 1 (2023).","journal-title":"Sci. Data"},{"key":"2872_CR12","doi-asserted-by":"publisher","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"A Goldberger","year":"2000","unstructured":"Goldberger, A. et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101, e215\u2013e220 (2000).","journal-title":"Circulation"},{"key":"2872_CR13","doi-asserted-by":"crossref","unstructured":"Liu, M. et al. FAIM: Fairness-aware interpretable modeling for trustworthy machine learning in healthcare. Patterns 5, 101059 (2024).","DOI":"10.1016\/j.patter.2024.101059"},{"key":"2872_CR14","unstructured":"National Institute of Biomedical Imaging and Bioengineering. Medical Imaging and Data Resource Center. https:\/\/www.nibib.nih.gov\/programs\/medical-imaging-data-resource-center."},{"key":"2872_CR15","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.acra.2019.10.006","volume":"27","author":"L Oakden-Rayner","year":"2020","unstructured":"Oakden-Rayner, L. Exploring large-scale public medical image datasets. Acad. Radiol. 27, 106\u2013112 (2020).","journal-title":"Acad. Radiol."},{"key":"2872_CR16","doi-asserted-by":"publisher","first-page":"1471","DOI":"10.3233\/ADR-240129","volume":"8","author":"A Alabduljabbar","year":"2024","unstructured":"Alabduljabbar, A. et al. Medical imaging datasets, preparation, and availability for artificial intelligence in medical imaging. J. Alzheimers Dis. Rep. 8, 1471\u20131483 (2024).","journal-title":"J. Alzheimers Dis. Rep."},{"key":"2872_CR17","doi-asserted-by":"crossref","unstructured":"Cohen, J. B., Brady, T. M., Juraschek, S. P., Picone, D. S., Yang, E. & Schutte, A. E. Apple Watch for Hypertension Screening. Hypertension 83, e26031 (2026).","DOI":"10.1161\/HYPERTENSIONAHA.125.26031"},{"key":"2872_CR18","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-96575-6","volume":"15","author":"M Shen","year":"2025","unstructured":"Shen, M., Mortezaagha, P. & Rahgozar, A. Explainable artificial intelligence to diagnose early Parkinson\u2019s disease via voice analysis. Sci. Rep. 15, 11687 (2025).","journal-title":"Sci. Rep."},{"key":"2872_CR19","doi-asserted-by":"publisher","unstructured":"Gow, B. et al. MIMIC-IV-ECG: diagnostic electrocardiogram matched subset (version 1.0). (PhysioNet, 2023). RRID:SCR_007345. https:\/\/doi.org\/10.13026\/4nqg-sb35","DOI":"10.13026\/4nqg-sb35"},{"key":"2872_CR20","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1109\/10.867928","volume":"47","author":"B Kemp","year":"2000","unstructured":"Kemp, B., Zwinderman, A. H., Tuk, B., Kamphuisen, H. A. C. & Obery\u00e9, J. J. L. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE-BME 47, 1185\u20131194 (2000).","journal-title":"IEEE-BME"},{"key":"2872_CR21","doi-asserted-by":"crossref","unstructured":"Schmidt, P., Reiss, A., Duerichen, R., Marberger, C. & Van Laerhoven, K. Introducing wesad, a multimodal dataset for wearable stress and affect detection. In Proceedings of the 20th ACM international conference on multimodal interaction, pp 400\u2013408 (2018).","DOI":"10.1145\/3242969.3242985"},{"key":"2872_CR22","doi-asserted-by":"publisher","unstructured":"Areias Saraiva, J., Abreu, M., Carmo, A. S., Pl\u00e1cido da Silva, H. & Fred, A. ScientISST MOVE: Annotated Wearable Multimodal Biosignals recorded during Everyday Life Activities in Naturalistic Environments (version 1.0.1). (PhysioNet, 2024). RRID:SCR_007345. https:\/\/doi.org\/10.13026\/hyxq-r919","DOI":"10.13026\/hyxq-r919"},{"key":"2872_CR23","doi-asserted-by":"publisher","unstructured":"Bensoussan, Y. et al. Bridge2AI-Voice Pediatric Dataset (version 1.1.0). PhysioNet. RRID:SCR_007345. https:\/\/doi.org\/10.13026\/h995-bt35 (2026).","DOI":"10.13026\/h995-bt35"},{"key":"2872_CR24","doi-asserted-by":"publisher","unstructured":"Bensoussan, Y. et al. Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information (version 3.1.0). PhysioNet. RRID:SCR_007345. https:\/\/doi.org\/10.13026\/8xbn-nq66 (2026).","DOI":"10.13026\/8xbn-nq66"},{"key":"2872_CR25","unstructured":"PhysioNet. PhysioNet credentialed health data use agreement 1.5.0. https:\/\/physionet.org\/content\/mimiciii\/view-dua\/1.4\/."},{"key":"2872_CR26","unstructured":"PhysioNet. Responsible use of MIMIC data with online services like GPT. https:\/\/physionet.org\/news\/post\/gpt-responsible-use (2023)."},{"key":"2872_CR27","unstructured":"PhysioNet. Use of MIMIC data with Large Language Models and Online Services. https:\/\/physionet.org\/news\/post\/llm-responsible-use (2025)."},{"key":"2872_CR28","unstructured":"American Medical Association. 2026 physician survey on augmented intelligence. https:\/\/www.ama-assn.org\/system\/files\/physician-ai-sentiment-report.pdf (2026)."},{"key":"2872_CR29","unstructured":"Fierce Healthcare. Some doctors are using public AI chatbots like ChatGPT to assist with clinical decisions. Is it safe? https:\/\/www.fiercehealthcare.com\/special-reports\/some-doctors-are-using-public-generative-ai-tools-chatgpt-clinical-decisions-it."},{"key":"2872_CR30","doi-asserted-by":"publisher","DOI":"10.2196\/58801","volume":"11","author":"E Ozkan","year":"2025","unstructured":"Ozkan, E. et al. Global health care professionals\u2019 perceptions of large language model use in practice: cross-sectional survey study. JMIR Med. Educ. 11, e58801 (2025).","journal-title":"JMIR Med. Educ."},{"key":"2872_CR31","doi-asserted-by":"publisher","DOI":"10.2196\/56797","volume":"8","author":"S Kisvarday","year":"2024","unstructured":"Kisvarday, S. et al. ChatGPT use among pediatric health care providers: cross-sectional survey study. JMIR Form. Res. 8, e56797 (2024).","journal-title":"JMIR Form. Res."},{"key":"2872_CR32","unstructured":"OpenAI. AI as a Healthcare Ally: How Americans Are Navigating the System with ChatGPT. https:\/\/cdn.openai.com\/pdf\/2cb29276-68cd-4ec6-a5f4-c01c5e7a36e9\/OpenAI-AI-as-a-Healthcare-Ally-Jan-2026.pdf (2026)."},{"key":"2872_CR33","unstructured":"OpenAI. \u201cIntroducing ChatGPT Health.\u201d OpenAI, 7 Jan.2026, https:\/\/openai.com\/index\/introducing-chatgpt-health\/"},{"key":"2872_CR34","unstructured":"UK Biobank. A message to our participants: UK Biobank data security update. https:\/\/www.ukbiobank.ac.uk\/news\/a-message-to-our-participants-uk-biobank-data-security-update\/ (2026)."},{"key":"2872_CR35","doi-asserted-by":"publisher","first-page":"e2420496","DOI":"10.1001\/jamanetworkopen.2024.20496","volume":"7","author":"T Oami","year":"2024","unstructured":"Oami, T., Okada, Y. & Nakada, T.-A. Performance of a large language model in screening citations. JAMA Netw. Open 7, e2420496 (2024).","journal-title":"JAMA Netw. Open"},{"key":"2872_CR36","doi-asserted-by":"crossref","unstructured":"Bijker, R., Merkouris, S. S., Dowling, N. A. & Rodda, S. N. ChatGPT for automated qualitative research: Content analysis. J. Med. Internet Res. 26, e59050 (2024).","DOI":"10.2196\/59050"},{"key":"2872_CR37","doi-asserted-by":"publisher","DOI":"10.1002\/smr.2723","volume":"37","author":"M Nejjar","year":"2025","unstructured":"Nejjar, M., Zacharias, L., Stiehle, F. & Weber, I. LLMs for science: Usage for code generation and data analysis. J. Softw. Evol. Process 37, e2723 (2025).","journal-title":"J. Softw. Evol. Process"},{"key":"2872_CR38","doi-asserted-by":"crossref","unstructured":"Chatterji, A. et al. How people use ChatGPT. NBER Working Paper 34255 (National Bureau of Economic Research, 2025).","DOI":"10.3386\/w34255"},{"key":"2872_CR39","doi-asserted-by":"publisher","first-page":"1553","DOI":"10.1038\/ejhg.2016.52","volume":"24","author":"MG Hansson","year":"2016","unstructured":"Hansson, M. G. et al. The risk of re-identification versus the need to identify individuals in rare disease research. Eur. J. Hum. Genet. 24, 1553\u20131558 (2016).","journal-title":"Eur. J. Hum. Genet."},{"key":"2872_CR40","doi-asserted-by":"publisher","first-page":"6","DOI":"10.5334\/egems.270","volume":"7","author":"GE Simon","year":"2019","unstructured":"Simon, G. E. et al. Assessing and minimizing re-identification risk in research data derived from health care records. Egems 7, 6 (2019).","journal-title":"Egems"},{"key":"2872_CR41","doi-asserted-by":"publisher","DOI":"10.1186\/s13023-020-01497-3","volume":"15","author":"J Gow","year":"2020","unstructured":"Gow, J., Moffatt, C. & Blackport, J. Participation in patient support forums may put rare disease patient data at risk of re-identification. Orphanet J. Rare Dis. 15, 226 (2020).","journal-title":"Orphanet J. Rare Dis."},{"key":"2872_CR42","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1001\/jama.1997.03550120089044","volume":"278","author":"CA Ford","year":"1997","unstructured":"Ford, C. A., Millstein, S. G., Halpern-Felsher, B. L. & Irwin, C. E. Influence of physician confidentiality assurances on adolescents\u2019 willingness to disclose information and seek future health care: a randomized controlled trial. JAMA 278, 1029\u20131034 (1997).","journal-title":"JAMA"},{"key":"2872_CR43","unstructured":"V\u00e9liz, C. Privacy is Power (Melville House, 2021)."},{"key":"2872_CR44","doi-asserted-by":"publisher","first-page":"e42131","DOI":"10.2196\/42131","volume":"25","author":"Y de Man","year":"2023","unstructured":"de Man, Y. et al. Opt-in and opt-out consent procedures for the reuse of routinely recorded health data in scientific research and their consequences for consent rate and consent bias: systematic review. J. Med. Internet Res. 25, e42131 (2023).","journal-title":"J. Med. Internet Res."},{"key":"2872_CR45","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1080\/15265161.2013.767955","volume":"13","author":"MA Rothstein","year":"2013","unstructured":"Rothstein, M. A. & Shoben, A. B. Does consent bias research? Am. J. Bioeth. 13, 27\u201337 (2013).","journal-title":"Am. J. Bioeth."},{"key":"2872_CR46","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1001\/jama.2024.21700","volume":"333","author":"S Bedi","year":"2025","unstructured":"Bedi, S. et al. Testing and evaluation of health care applications of large language models: a systematic review. JAMA 333, 319\u2013328 (2025).","journal-title":"JAMA"},{"key":"2872_CR47","first-page":"e53441","volume":"16","author":"A Almagazzachi","year":"2024","unstructured":"Almagazzachi, A. et al. Generative artificial intelligence in patient education: ChatGPT takes on hypertension questions. Cureus 16, e53441 (2024).","journal-title":"Cureus"},{"key":"2872_CR48","unstructured":"OpenAI. \u201cIntroducing ChatGPT Search\u201d. https:\/\/openai.com\/index\/introducing-chatgpt-search\/"},{"key":"2872_CR49","doi-asserted-by":"publisher","first-page":"1883","DOI":"10.1038\/s41591-022-01966-1","volume":"28","author":"Y Yang","year":"2022","unstructured":"Yang, Y. et al. A digital mask to safeguard patient privacy. Nat. Med. 28, 1883\u20131892 (2022).","journal-title":"Nat. Med."},{"key":"2872_CR50","doi-asserted-by":"publisher","DOI":"10.2196\/66873","volume":"27","author":"H Tan","year":"2025","unstructured":"Tan, H. et al. Artificial intelligence\u2013enabled facial privacy protection for ocular diagnosis: development and validation study. J. Med. Internet Res. 27, e66873 (2025).","journal-title":"J. Med. Internet Res."},{"key":"2872_CR51","doi-asserted-by":"publisher","DOI":"10.2196\/22739","volume":"22","author":"YU Jeong","year":"2020","unstructured":"Jeong, Y. U., Yoo, S., Kim, Y. H. & Shim, W. H. De-identification of facial features in magnetic resonance images: software development using deep learning technology. J. Med. Internet Res. 22, e22739 (2020).","journal-title":"J. Med. Internet Res."},{"key":"2872_CR52","doi-asserted-by":"crossref","unstructured":"Kuang, Z., Yang, X., Shen, Y., Hu, C. & Yu, J. Facial identity anonymization via intrinsic and extrinsic attention distraction. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition pp. 12406\u201312415 (IEEE, 2024).","DOI":"10.1109\/CVPR52733.2024.01179"},{"key":"2872_CR53","doi-asserted-by":"crossref","unstructured":"Cao, Q., Shen, L., Xie, W., Parkhi, O. M. & Zisserman, A. Vggface2: a dataset for recognising faces across pose and age. In Proc. 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) pp. 67\u201374 (IEEE, 2018).","DOI":"10.1109\/FG.2018.00020"},{"key":"2872_CR54","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X. & Tang, X. Deep learning face attributes in the wild. In Proc. IEEE International Conference on Computer Vision pp. 3730\u20133738 (IEEE, 2015).","DOI":"10.1109\/ICCV.2015.425"},{"key":"2872_CR55","doi-asserted-by":"crossref","unstructured":"Cao, J. et al. Face De-Identification: State-of-the-Art Methods and Comparative Studies, In IEEE Transactions on Broadcasting, Vol. 72, 260-280 (2025).","DOI":"10.1109\/TBC.2025.3639783"},{"key":"2872_CR56","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1109\/TBIOM.2024.3485990","volume":"7","author":"W Khan","year":"2024","unstructured":"Khan, W. et al. Person de-identification: a comprehensive review of methods, datasets, applications, and ethical aspects along with new dimensions. IEEE Trans. Biom. Behav. Identity Sci. 7, 293\u2013312 (2024).","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"key":"2872_CR57","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1044\/jslhr.4303.796","volume":"43","author":"FL Wuyts","year":"2000","unstructured":"Wuyts, F. L. et al. The dysphonia severity index: an objective measure of vocal quality based on a multiparameter approach. J. Speech Lang. Hearing Res. 43, 796\u2013809 (2000).","journal-title":"J. Speech Lang. Hearing Res."},{"key":"2872_CR58","doi-asserted-by":"publisher","first-page":"AIdbp2400537.","DOI":"10.1056\/AIdbp2400537","volume":"2","author":"IC Wiest","year":"2025","unstructured":"Wiest, I. C. et al. Deidentifying medical documents with local, privacy-preserving large language models: the LLM-anonymizer. NEJM AI 2, AIdbp2400537. (2025).","journal-title":"NEJM AI"},{"key":"2872_CR59","unstructured":"North American Association of Central Cancer Registries. NAACCR Virtual Pooled Registry Data Use Agreement. Version 4. 2 Mar. 2023. Available at: https:\/\/apps.naaccr.org\/vpr-static\/VPR_Templated_DUA_4_19_23.0556c3304efa.pdf."},{"key":"2872_CR60","unstructured":"Mayo Clinic. (n.d.). Mayo Data Use Agreement [Data use agreement for n2c2 2019 Tracks 1\u20132 \/ THYME protocol]. https:\/\/n2c2.dbmi.hms.harvard.edu\/sites\/g\/files\/omnuum9281\/files\/n2c2\/files\/n2c2_2019_-_dua_tracks_1-2.pdf."},{"key":"2872_CR61","unstructured":"Bridge2AI Voice Registered Access Agreement. PhysioNet. License for Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information, version 3.1.0. https:\/\/physionet.org\/content\/b2ai-voice\/view-dua\/3.1.0\/."},{"key":"2872_CR62","unstructured":"U.S. Department of Health and Human Services. Summary of the HIPAA Privacy Rule. https:\/\/www.hhs.gov\/hipaa\/for-professionals\/privacy\/laws-regulations\/index.html (2025)."},{"key":"2872_CR63","unstructured":"U.S. Department of Health & Human Services. Addressing Gaps in Cybersecurity: OCR Releases Crosswalk Between HIPAA Security Rule and NIST Cybersecurity Framework. https:\/\/www.hhs.gov\/hipaa\/for-professionals\/security\/nist-security-hipaa-crosswalk\/index.html (2016)."},{"key":"2872_CR64","unstructured":"U.S. Department of Health & Human Services. What does the Security Rule require a covered entity to do to comply with the Security Incidents Procedures standard? https:\/\/www.hhs.gov\/hipaa\/for-professionals\/faq\/2002\/what-does-the-security-rule-require-a-covered-entity-to-do-to-comply\/index.html (2013)."},{"key":"2872_CR65","doi-asserted-by":"publisher","unstructured":"National Institute of Standards & Technology. The NIST Cybersecurity Framework (CSF) 2.0 (CSWP 29). https:\/\/doi.org\/10.6028\/NIST.CSWP.29 (2024).","DOI":"10.6028\/NIST.CSWP.29"},{"key":"2872_CR66","unstructured":"Regulation (EU) 2016\/679 (GDPR), Article 32. https:\/\/gdpr-info.eu\/art-32-gdpr\/."},{"key":"2872_CR67","unstructured":"U.K. Information Commissioner's Office. A guide to data security. https:\/\/ico.org.uk\/for-organisations\/uk-gdpr-guidance-and-resources\/security\/a-guide-to-data-security\/ (2023)."},{"key":"2872_CR68","unstructured":"U.K. Information Commissioner's Office. Security outcomes. https:\/\/ico.org.uk\/for-organisations\/uk-gdpr-guidance-and-resources\/security\/a-guide-to-data-security\/security-outcomes\/."},{"key":"2872_CR69","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2016.18","volume":"3","author":"MD Wilkinson","year":"2016","unstructured":"Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. data 3, 1\u20139 (2016).","journal-title":"Sci. data"},{"key":"2872_CR70","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01894-2","volume":"9","author":"J Sainz-Pardo Diaz","year":"2022","unstructured":"Sainz-Pardo Diaz, J. & Lopez Garcia, A. A Python library to check the level of anonymity of a dataset. Sci. Data 9, 785 (2022).","journal-title":"Sci. Data"},{"key":"2872_CR71","unstructured":"Microsoft. Presidio. https:\/\/github.com\/microsoft\/presidio."},{"key":"2872_CR72","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/OJSP.2023.3344375","volume":"5","author":"S Meyer","year":"2023","unstructured":"Meyer, S., Miao, X. & Vu, N. T. VoicePAT: an efficient open-source evaluation toolkit for voice privacy research. IEEE Open J. Signal Process. 5, 257\u2013265 (2023).","journal-title":"IEEE Open J. Signal Process."},{"key":"2872_CR73","first-page":"984","volume":"2014","author":"F Prasser","year":"2014","unstructured":"Prasser, F. et al. ARX\u2014a comprehensive tool for anonymizing biomedical data. AMIA Annu. Symp. Proc. 2014, 984\u2013993 (2014).","journal-title":"AMIA Annu. Symp. Proc."},{"key":"2872_CR74","doi-asserted-by":"crossref","unstructured":"Meyer, S., Lux, F. & Vu, N. T. Probing the feasibility of multilingual speaker anonymization. In Proc. Interspeech 2024, 4448\u20134452 (2024).","DOI":"10.21437\/Interspeech.2024-1615"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02872-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02872-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02872-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T18:08:19Z","timestamp":1791569299000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02872-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,4]]},"references-count":74,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2872"],"URL":"https:\/\/doi.org\/10.1038\/s41746-026-02872-3","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,4]]},"assertion":[{"value":"12 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"B.W. is Principal Investigator on the following CRADAs (Cooperative Research & Development Agreements) between NIH and industry: Philips (CRADA), Philips Research (CRADA), Celsion Corp (CRADA), BTG Biocompatibles\/Boston Scientific) (CRADA), Siemens (CRADA), NVIDIA (CRADA), XAct Robotics (CRADA). ProMaxo (CRADA), Theromics, Imactis, Canon Medical, Varian, MediView, URO-1, Immunophotonics (pending), Deepsight. The following industry partners also support the research of BW via equipment, personnel, devices and\/or drugs: 3T Technologies (devices), Exact Imaging (data), Angiodynamics (equipment), Astra Zeneca (pharmaceuticals, NCI CRADA), ArciTrax (devices and equipment), Imactis (Equipment), Johnson and Johnson (equipment), Medtronic (equipment), Theromics (Supplies), Profound (equipment and supplies), QT Imaging (equipment and supplies), Medtronic (equipment), Boston Scientific (equipment), Varian (materials), Combat Medical (equipment), Clinical LaserThermia Systems (equipment), MediView (equipment), CIVCO Medical Solutions (equipment), Galvanize, Alessa Therapeutics (NCI CRADA or CTA), Immunophotonics (supplies). Philips and NIH have a patent licensing agreement under which NIH receives royalties, a portion of which are then given to BW. NVIDIA and NIH have a licensing agreement. NIH and Canon have a licensing agreement. NIH and CIVCO are co-inventors on IP. NIH and ArciTrax are coinventors on IP. NIH and Philips are coinventors on IP. NIH and Boston Scientific are coinventors on IP. NIH and NVIDIA are coinventors on IP.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"779"}}