{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:53:58Z","timestamp":1792000438890,"version":"4.4.0"},"reference-count":68,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T00:00:00Z","timestamp":1791936000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["GRK 3012 \u2013 520750254"],"award-info":[{"award-number":["GRK 3012 \u2013 520750254"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>The use of Artificial Intelligence (AI) for image-based diagnostics is rising rapidly and enables new opportunities for improving clinical diagnostics through high precision and a quick identification of anomalies. However, a technology characterized by a high variability of image data and the opaqueness of AI models within patient care raises ethical challenges and requires protection by an appropriate ethical and regulatory framework. To identify ethical challenges of the ethical and regulatory framework of the behavioral performance of medical AI systems for image-based diagnostics on the international and European Union level, we searched and screened documents in April and May 2025. The documents were thematically evaluated before conducting an ethical analysis. We identified regulations and ethical recommendations that could be divided into five thematic categories: The (i) data basis is regulated by data standardization, data assessment, and data quality. (ii) Reliability considers assessments, the intended performance, robustness, accuracy, and compliance, whereas the (iii) comprehensibility centers on explainability, understandability, and traceability. The (iv) disclosure of the system considers transparency, operational disclosure, and collaborations, and the (v) usability is regulated on resources and competency. This framework could support the autonomy, beneficence, non-maleficence, and (social) justice, however allows a wide range of compliant technical measures that vary in effectiveness. The insufficient technical feasibility to provide security measures against malicious data alteration as well as the dynamic development of attack and defense mechanisms severely challenge the maintenance of a compliant safe and secure diagnostic AI system. Thereby, the autonomy, beneficence, non-maleficence, and (social) justice could be jeopardized. In conclusion, AI image-based diagnostic systems are regulated by a similar ethical and regulatory framework as general medical AI systems. To protect data and the intended performance, sufficient protection through a regulatory framework is necessary that responds to the challenges of image-based AI diagnostic systems.<\/jats:p>","DOI":"10.1371\/journal.pdig.0001781","type":"journal-article","created":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:27:32Z","timestamp":1791998852000},"page":"e0001781","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["The ethical and regulatory framework of medical artificial intelligence (AI) system\u2019s behavioral performance for image-based diagnostics: An ethical evaluation of the International and European Union Scope"],"prefix":"10.1371","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6349-1052","authenticated-orcid":true,"given":"Nina","family":"Parchmann","sequence":"first","affiliation":[{"name":"Ulm University","place":["Ulm, Germany"],"department":["Institute of the History, Philosophy and Ethics of Medicine"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcin","family":"Orzechowski","sequence":"additional","affiliation":[{"name":"Ulm University","place":["Ulm, Germany"],"department":["Institute of the History, Philosophy and Ethics of Medicine"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Braun","sequence":"additional","affiliation":[{"name":"Ulm University","place":["Ulm, Germany"],"department":["Institute of Neural Information Processing"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian","family":"Steger","sequence":"additional","affiliation":[{"name":"Ulm University","place":["Ulm, Germany"],"department":["Institute of the History, Philosophy and Ethics of Medicine"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,10,14]]},"reference":[{"issue":"4","key":"pdig.0001781.ref001","doi-asserted-by":"crossref","first-page":"101333","DOI":"10.1016\/j.jmir.2023.10.001","article-title":"The shortage of radiographers: A global crisis in healthcare","volume":"55","author":"K Konstantinidis","year":"2024","journal-title":"J Med Imaging Radiat Sci"},{"key":"pdig.0001781.ref002","unstructured":"Asin S. The radiologist shortage, explained. 2024. Available from: https:\/\/www.beckershospitalreview.com\/radiology\/the-radiologist-shortage-explained\/"},{"key":"pdig.0001781.ref003","unstructured":"Statista. Diagnostic Imaging Devices. 2025. Available from: https:\/\/www.statista.com\/outlook\/hmo\/medical-technology\/medical-devices\/diagnostic-imaging-devices\/worldwide?srsltid=AfmBOooFsgCz3KvPhHMipIb2xpSCnVK-Dx8eLGNRMPrcEYcx5ioPsO9q"},{"issue":"11","key":"pdig.0001781.ref004","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.3390\/life14111451","article-title":"Artificial intelligence in breast cancer diagnosis and treatment: Advances in imaging, pathology, and personalized care","volume":"14","author":"P Uchikov","year":"2024","journal-title":"Life"},{"key":"pdig.0001781.ref005","doi-asserted-by":"crossref","first-page":"1273253","DOI":"10.3389\/fpubh.2023.1273253","article-title":"Medical image analysis using deep learning algorithms","volume":"11","author":"M Li","year":"2023","journal-title":"Front Public Health"},{"issue":"7","key":"pdig.0001781.ref006","doi-asserted-by":"crossref","DOI":"10.1007\/s10586-025-05281-5","article-title":"Explainable artificial intelligence for medical imaging systems using deep learning: A comprehensive review","volume":"28","author":"EH Houssein","year":"2025","journal-title":"Cluster Comput"},{"key":"pdig.0001781.ref007","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1186\/s43055-024-01356-2","article-title":"Explainability, transparency and black box challenges of AI in radiology: Impact on patient care in cardiovascular radiology","volume":"55","author":"A Marey","year":"2024","journal-title":"Egypt J Radiol Nucl Med"},{"issue":"3","key":"pdig.0001781.ref008","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1007\/s10676-021-09593-z","article-title":"Artificial intelligence regulation: A framework for governance","volume":"23","author":"PGR de Almeida","year":"2021","journal-title":"Ethics Inf Technol"},{"key":"pdig.0001781.ref009","doi-asserted-by":"crossref","first-page":"134581","DOI":"10.1016\/j.scitotenv.2019.134581","article-title":"Ecosystem services research in mountainous regions: A systematic literature review on current knowledge and research gaps","volume":"702","author":"W Mengist","year":"2020","journal-title":"Sci Total Environ"},{"key":"pdig.0001781.ref010","doi-asserted-by":"crossref","first-page":"100777","DOI":"10.1016\/j.mex.2019.100777","article-title":"Method for conducting systematic literature review and meta-analysis for environmental science research","volume":"7","author":"W Mengist","year":"2019","journal-title":"MethodsX"},{"key":"pdig.0001781.ref011","doi-asserted-by":"crossref","first-page":"1386","DOI":"10.12688\/f1000research.27337.1","article-title":"Using citation tracking for systematic literature searching - study protocol for a scoping review of methodological studies and a Delphi study","volume":"9","author":"J Hirt","year":"2020","journal-title":"F1000Res"},{"issue":"6","key":"pdig.0001781.ref012","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3665926","article-title":"A.I. Robustness: A human-centered perspective on technological challenges and opportunities","volume":"57","author":"A Tocchetti","year":"2025","journal-title":"ACM Comput Surv"},{"key":"pdig.0001781.ref013","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"372","author":"MJ Page","year":"2021","journal-title":"BMJ"},{"key":"pdig.0001781.ref014","volume-title":"Qualitative Inhaltsanalyse- Grundlagen und Techniken","author":"P Mayring","year":"2015","edition":"12"},{"key":"pdig.0001781.ref015","article-title":"Using thematic analysis in psychology","author":"V Braun","year":"2006","journal-title":"Qual Res Psychol"},{"issue":"3","key":"pdig.0001781.ref016","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1111\/nhs.12048","article-title":"Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study","volume":"15","author":"M Vaismoradi","year":"2013","journal-title":"Nurs Health Sci"},{"key":"pdig.0001781.ref017","volume-title":"Principles of biomedical ethics","author":"T Beauchamp","year":"2019","edition":"8"},{"key":"pdig.0001781.ref018","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1057\/s41599-024-02894-w","article-title":"Shaping the future of AI in healthcare through ethics and governance","volume":"11","author":"R Bouderhem","year":"2024","journal-title":"Humanit Soc Sci Commun"},{"issue":"4","key":"pdig.0001781.ref019","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e26297","article-title":"Ethical and regulatory challenges of AI technologies in healthcare: A narrative review","volume":"10","author":"C Mennella","year":"2024","journal-title":"Heliyon"},{"key":"pdig.0001781.ref020","doi-asserted-by":"crossref","first-page":"105152","DOI":"10.1016\/j.healthpol.2024.105152","article-title":"The EU Artificial Intelligence Act (2024): Implications for healthcare","volume":"149","author":"H Van Kolfschooten","year":"2024","journal-title":"Health Policy"},{"key":"pdig.0001781.ref021","first-page":"596","article-title":"How interoperability can enable artificial intelligence in clinical applications","volume":"316","author":"F Rehburg","year":"2024","journal-title":"Stud Health Technol Inform"},{"key":"pdig.0001781.ref022","doi-asserted-by":"crossref","first-page":"101388","DOI":"10.1016\/j.cogsys.2025.101388","article-title":"A machine-readable metadata model for intelligent data analysis","volume":"93","author":"X Angerri","year":"2025","journal-title":"Cogn Syst Res"},{"key":"pdig.0001781.ref023","unstructured":"United States Environmental Protection Agency. Guidance on Environmental Data Verification and Data Validation. EPA\/240\/R-02\/004. 2002. Available from: https:\/\/www.epa.gov\/sites\/default\/files\/2015-06\/documents\/g8-final.pdf"},{"issue":"4","key":"pdig.0001781.ref024","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/s00778-022-00775-9","article-title":"Data collection and quality challenges in deep learning: A data-centric AI perspective","volume":"32","author":"SE Whang","year":"2023","journal-title":"The VLDB Journal"},{"key":"pdig.0001781.ref025","article-title":"The effects of data quality on machine learning performance on tabular data","author":"S Mohammed","year":"2025","journal-title":"Hasso Plattnerar"},{"issue":"3","key":"pdig.0001781.ref026","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MRL.2024.3430192","article-title":"The role of data quality for reliable AI performance in medical applications","volume":"1","author":"N Enshaei","year":"2024","journal-title":"IEEE Rel Mag"},{"issue":"1","key":"pdig.0001781.ref027","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/s41746-022-00611-y","article-title":"Clinical artificial intelligence quality improvement: Towards continual monitoring and updating of AI algorithms in healthcare","volume":"5","author":"J Feng","year":"2022","journal-title":"NPJ Digit Med"},{"issue":"16","key":"pdig.0001781.ref028","doi-asserted-by":"crossref","first-page":"3184","DOI":"10.3390\/electronics13163184","article-title":"Innovative noise extraction and denoising in low-dose CT using a supervised deep learning framework","volume":"13","author":"W Zhang","year":"2024","journal-title":"Electronics"},{"key":"pdig.0001781.ref029","article-title":"The impact of scanner domain shift on deep learning performance in medical imaging: an experimental study","author":"B Guo","year":"2026","journal-title":"Int J CARS"},{"issue":"11","key":"pdig.0001781.ref030","article-title":"Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study","volume":"15","author":"JR Zech","year":"2018","journal-title":"PLoS Med"},{"issue":"6","key":"pdig.0001781.ref031","article-title":"Enhancing radiological diagnosis: A comprehensive review of image quality assessment and optimization strategies","volume":"16","author":"AP Varghese","year":"2024","journal-title":"Cureus"},{"key":"pdig.0001781.ref032","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3592597","article-title":"A survey of threats to research literature-dependent medical AI solutions","volume":"55","author":"S Saini","year":"2023","journal-title":"ACM Comput Surv"},{"issue":"1","key":"pdig.0001781.ref033","doi-asserted-by":"crossref","first-page":"7281","DOI":"10.1038\/s41467-021-27577-x","article-title":"A machine and human reader study on AI diagnosis model safety under attacks of adversarial images","volume":"12","author":"Q Zhou","year":"2021","journal-title":"Nat Commun"},{"key":"pdig.0001781.ref034","first-page":"47","article-title":"TrojFair: Trojan Fairness Attacks.","volume-title":"Proceedings of the 1st ACM Workshop on Large AI Systems and Models with Privacy and Safety Analysis (LAMPS\u201924)","author":"J Xue","year":"2023"},{"issue":"7","key":"pdig.0001781.ref035","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3538707","article-title":"Threats to training: A survey of poisoning attacks and defenses on machine learning systems","volume":"55","author":"Z Wang","year":"2022","journal-title":"ACM Comput Surv"},{"key":"pdig.0001781.ref036","article-title":"How AI challenges the medical device regulation: Patient safety, benefits, and intended uses","author":"D Onitiu","year":"2024","journal-title":"J Law Biosci"},{"key":"pdig.0001781.ref037","article-title":"SAM: Foreseeing Inference-Time False Data Injection Attacks on ML-enabled Medical Devices.","volume-title":"Proceedings of the 2024 Workshop on Cybersecurity in Healthcare (HealthSec\u201924)","author":"M Hallajiyan","year":"2024"},{"issue":"12","key":"pdig.0001781.ref038","article-title":"Recommendations for the development and use of imaging test sets to investigate the test performance of artificial intelligence in health screening","volume":"4","author":"A Chalkidou","year":"2022","journal-title":"Lancet Digit Health"},{"issue":"1","key":"pdig.0001781.ref039","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1148\/radiol.220182","article-title":"Methods for clinical evaluation of artificial intelligence algorithms for medical diagnosis","volume":"306","author":"SH Park","year":"2023","journal-title":"Radiology"},{"issue":"7","key":"pdig.0001781.ref040","doi-asserted-by":"crossref","first-page":"108","DOI":"10.9734\/jocamr\/2024\/v25i7552","article-title":"Post-market surveillance of medical devices using AI","volume":"25","author":"T Khinvasara","year":"2024","journal-title":"J Compl Altern Med Res"},{"issue":"5","key":"pdig.0001781.ref041","doi-asserted-by":"crossref","DOI":"10.1056\/AIp2401116","article-title":"Challenges in the postmarket surveillance of clinical prediction models","volume":"2","author":"S Ansari","year":"2025","journal-title":"NEJM AI"},{"key":"pdig.0001781.ref042","doi-asserted-by":"crossref","first-page":"100146","DOI":"10.1016\/j.cmpbup.2024.100146","article-title":"AI in diagnostic imaging: Revolutionising accuracy and efficiency","volume":"5","author":"M Khalifa","year":"2024","journal-title":"Comput Methods Programs Biomed Update"},{"issue":"11","key":"pdig.0001781.ref043","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0260560","article-title":"Deep learning algorithm in detecting intracranial hemorrhages on emergency computed tomographies","volume":"16","author":"A Kundisch","year":"2021","journal-title":"PLoS One"},{"issue":"10","key":"pdig.0001781.ref044","doi-asserted-by":"crossref","first-page":"2838","DOI":"10.1038\/s41591-024-03113-4","article-title":"The limits of fair medical imaging AI in real-world generalization","volume":"30","author":"Y Yang","year":"2024","journal-title":"Nat Med"},{"key":"pdig.0001781.ref045","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1038\/s42256-021-00338-7","article-title":"AI for radiographic COVID-19 detection selects shortcuts over signal","volume":"3","author":"AJ DeGrave","year":"2021","journal-title":"Nat Mach Intell"},{"issue":"1","key":"pdig.0001781.ref046","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/MPRV.2022.3218773","article-title":"DDoD: Dual denial of decision attacks on human-AI teams","volume":"22","author":"B Tag","year":"2023","journal-title":"IEEE Pervasive Comput"},{"issue":"3","key":"pdig.0001781.ref047","doi-asserted-by":"crossref","first-page":"157","DOI":"10.3390\/a18030157","article-title":"AI under attack: Metric-driven analysis of cybersecurity threats in deep learning models for healthcare applications","volume":"18","author":"S Brohi","year":"2025","journal-title":"Algorithms"},{"key":"pdig.0001781.ref048","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1007\/s00500-025-10516-z","article-title":"Threats to medical diagnosis systems: Analyzing targeted adversarial attacks in deep learning-based COVID-19 diagnosis","volume":"29","author":"SBU Haque","year":"2025","journal-title":"Soft Comput"},{"issue":"12","key":"pdig.0001781.ref049","article-title":"Robustness in deep learning models for medical diagnostics: Security and adversarial challenges towards robust AI applications","volume":"58","author":"H Javed","year":"2025","journal-title":"Artif Intell Rev"},{"issue":"06","key":"pdig.0001781.ref050","first-page":"40","article-title":"Implementation of dual-use technologies in defense and public security","volume":"7","author":"K Ilova\u010da","year":"2025","journal-title":"Am J Poli Sci Law Criminol"},{"issue":"7","key":"pdig.0001781.ref051","doi-asserted-by":"crossref","first-page":"109","DOI":"10.55927\/9gazwn03","article-title":"Transnational cyber crime: Challenges of international cooperation in combating cybercrime","volume":"3","author":"A Widiastuti","year":"2025","journal-title":"IJCS"},{"key":"pdig.0001781.ref052","doi-asserted-by":"crossref","first-page":"1274273","DOI":"10.3389\/fradi.2023.1274273","article-title":"RoMIA: A framework for creating robust medical imaging AI models for chest radiographs","volume":"3","author":"A Anand","year":"2024","journal-title":"Front Radiol"},{"issue":"1","key":"pdig.0001781.ref053","doi-asserted-by":"crossref","first-page":"11654","DOI":"10.1038\/s41598-022-15245-z","article-title":"Temporal quality degradation in AI models","volume":"12","author":"D Vela","year":"2022","journal-title":"Sci Rep"},{"issue":"10","key":"pdig.0001781.ref054","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0291908","article-title":"Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient","volume":"18","author":"GM Foody","year":"2023","journal-title":"PLoS One"},{"issue":"1","key":"pdig.0001781.ref055","first-page":"34","article-title":"Ethical AI in medicine: Balancing innovation with regulation and compliance","volume":"2","author":"U Gowda","year":"2025","journal-title":"Int J Sci Technol"},{"key":"pdig.0001781.ref056","volume-title":"Think Again: Freedom of Thought in the Age of Artificial Intelligence Policy Brief","author":"OSCE\/ODIHR","year":"2025"},{"issue":"2","key":"pdig.0001781.ref057","doi-asserted-by":"crossref","first-page":"168","DOI":"10.3390\/diagnostics15020168","article-title":"Explainable AI in diagnostic radiology for neurological disorders: A systematic review, and what doctors think about it","volume":"15","author":"Y Hafeez","year":"2025","journal-title":"Diagnostics (Basel)"},{"issue":"9","key":"pdig.0001781.ref058","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.3390\/cancers18091361","article-title":"Explainability and trust in deep learning for cancer imaging: Systematic barriers, clinical misalignment, and a translational roadmap","volume":"18","author":"S Borra","year":"2026","journal-title":"Cancers (Basel)"},{"key":"pdig.0001781.ref059","article-title":"A Taxonomy of AI Opacity in the EU: Rethinking Transparency, Traceability, Interpretability, and Explainability","author":"C Buttaboni","year":"2025","journal-title":"SSRN"},{"key":"pdig.0001781.ref060","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1145\/3442188.3445937","article-title":"Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems.","volume-title":"Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT \u201821)","author":"JA Kroll","year":"2021"},{"issue":"1","key":"pdig.0001781.ref061","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1186\/s12910-021-00687-3","article-title":"Privacy and artificial intelligence: Challenges for protecting health information in a new era","volume":"22","author":"B Murdoch","year":"2021","journal-title":"BMC Med Ethics"},{"key":"pdig.0001781.ref062","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1145\/3611026","article-title":"Why should red and green never be seen? Exploring color blindness simulations as tools to create chromatically accessible games","volume":"2023","author":"M Pinheiro","year":"2023","journal-title":"Proc ACM Hum-Comput Interact"},{"issue":"10","key":"pdig.0001781.ref063","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1016\/j.jacr.2024.04.027","article-title":"Establishing a validation infrastructure for imaging-based artificial intelligence algorithms before clinical implementation","volume":"21","author":"OA Ramwala","year":"2024","journal-title":"J Am Coll Radiol"},{"issue":"2","key":"pdig.0001781.ref064","article-title":"Impact of artificial intelligence on healthcare informatics: Opportunities and challenges","volume":"3","author":"PD Saxena","year":"2023","journal-title":"JIER"},{"issue":"6","key":"pdig.0001781.ref065","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1370\/afm.2887","article-title":"Competencies for the use of artificial intelligence in primary care","volume":"20","author":"W Liaw","year":"2022","journal-title":"Ann Fam Med"},{"issue":"1","key":"pdig.0001781.ref066","doi-asserted-by":"crossref","first-page":"849","DOI":"10.5334\/pme.2233","article-title":"Not replaced, but reinvented: AI education pathways to prepare future physicians to lead healthcare transformation","volume":"14","author":"N Mehta","year":"2025","journal-title":"Perspect Med Educ"},{"issue":"7","key":"pdig.0001781.ref067","article-title":"Empowering patients: Promoting patient education and health literacy","volume":"14","author":"Pacifico","year":"2022","journal-title":"Cureus"},{"key":"pdig.0001781.ref068","doi-asserted-by":"crossref","first-page":"31","DOI":"10.58496\/BJIoT\/2023\/005","article-title":"Safeguarding connected health: Leveraging trustworthy AI techniques to harden intrusion detection systems against data poisoning threats in IoMT environments","volume":"2023","author":"M Aljanabi","year":"2023","journal-title":"Babylonian J Internet Things"}],"container-title":["PLOS Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0001781","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:27:41Z","timestamp":1791998861000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0001781"}},"subtitle":[],"editor":[{"given":"Qiangqiang","family":"Gu","sequence":"first","affiliation":[{"name":"University of Pittsburgh School of Medicine","place":["UNITED STATES OF AMERICA"]}],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,10,14]]},"references-count":68,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,10,14]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pdig.0001781","relation":{},"ISSN":["2767-3170"],"issn-type":[{"value":"2767-3170","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,14]]}}}