Previously submitted to: JMIR Medical Informatics (no longer under consideration since Aug 07, 2026)
Date Submitted: Jul 31, 2026
Open Peer Review Period: Aug 7, 2026 - Aug 7, 2026
(closed for review but you can still tweet)
NOTE: This is an unreviewed Preprint
Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).
Peer review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.
Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).
Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.
Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.
Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Using Artificial Intelligence to enhance data integrity in health care sector: scoping review
ABSTRACT
Background:
Data integrity is essential for ensuring accurate, complete, consistent, and secure healthcare information that supports quality patient care and informed clinical decision-making. However, healthcare data are vulnerable to errors, inconsistencies, and unauthorized alterations. Artificial intelligence (AI) has emerged as a promising technology for improving healthcare data integrity through automated validation, anomaly detection, and security monitoring.
Objective:
This scoping review aimed to map the existing literature on the use of AI to enhance data integrity in healthcare, identify the AI techniques used, determine the data integrity dimensions addressed, and highlight gaps requiring further research.
Methods:
A scoping review was conducted following the PCC (Population, Concept, Context) framework. Literature published in English between 2021 and 2026 was searched in PubMed, Scopus, IEEE Xplore, Web of Science, and Google Scholar. Peer-reviewed articles, conference papers, and relevant grey literature focusing on AI applications for healthcare data integrity were included. Data were extracted using a standardized charting form and synthesized thematically.
Results:
A total of 1,247 records were identified, of which 20 studies met the inclusion criteria. The most frequently reported AI techniques were anomaly detection, supervised machine learning, and natural language processing. Most studies focused on improving data accuracy and security, reporting high error detection rates (89–94%) and enhanced protection against data tampering. However, comparatively few studies addressed data completeness, consistency, and timeliness. Most evidence originated from high-income countries, with limited implementation studies and relatively little research conducted in low- and middle-income countries.
Conclusions:
This scoping review demonstrates that artificial intelligence has considerable potential to improve healthcare data integrity through enhanced error detection, automated validation, improved consistency, and strengthened data security. Technologies such as anomaly detection, machine learning, natural language processing, and blockchain-based AI have shown encouraging results across various healthcare settings. Nevertheless, the current evidence remains heavily focused on technical development rather than large-scale implementation. The review also identified several important evidence gaps, including limited research on data completeness, consistency, timeliness, interoperability, and AI implementation within low- and middle-income countries. Addressing these gaps will require more prospective implementation studies, stronger collaboration between healthcare and technology professionals, and greater investment in context-specific research. As healthcare systems continue to adopt digital technologies, responsible and evidence-based implementation of AI has the potential to significantly improve the quality, reliability, and integrity of healthcare information, ultimately contributing to safer and more effective patient care.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.