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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Published on: July 11, 2025

Data Integrity in Medical AI.

Lenka Lhotska1,2

  • 1Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Data integrity is crucial for trustworthy medical artificial intelligence (AI). Ensuring high-quality, unbiased data through robust collection methods is essential for reliable AI applications in healthcare and precision medicine.

Keywords:
Bias MitigationData Collection DesignData QualityEthical AIHealthcare Data IntegrityMedical Artificial IntelligenceNoise Reduction

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Area of Science:

  • Medical Artificial Intelligence
  • Data Science
  • Bioethics

Background:

  • Reliability and ethical use of AI in medicine depend on data integrity.
  • Medical datasets are often small, heterogeneous, and vulnerable to quality issues.
  • Poor data quality, noise, and bias can distort analyses and clinical decisions.

Purpose of the Study:

  • Examine data integrity (quality, noise, bias, collection design) as the foundation for trustworthy medical AI.
  • Highlight data integrity as a scientific and moral requirement for AI in healthcare.
  • Provide a framework for robust data collection in medical AI.

Main Methods:

  • Peer-review style report examining data integrity concepts.
  • Analysis of how data deficits impact medical AI.
  • Outline of a ten-step framework for robust data collection.

Main Results:

  • Poor data quality, noise, and bias significantly distort medical AI analyses and clinical decisions.
  • A ten-step framework for robust data collection is proposed.
  • Data integrity is identified as critical for transparent, equitable, and reliable AI.

Conclusions:

  • Well-curated, representative, and unbiased data are essential for safe patient care and advancing precision medicine.
  • Medical AI requires data integrity for clinical reliability and ethical application.
  • Robust data collection frameworks are necessary to mitigate risks associated with medical datasets.