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Related Experiment Video

Updated: Apr 27, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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FAIR4prep: FAIR clinical informatics data preprocessing in artificial intelligence applications.

Miriam Cobo1, Adriana Katherine Calapaqui Terán2,3, Fernando Aguilar4

  • 1Advanced Computing and e-Science Group, Institute of Physics of Cantabria (IFCA), CSIC - UC, Santander, Spain. cobocano@ifca.unican.es.

Scientific Data
|April 25, 2026
PubMed
Summary
This summary is machine-generated.

Reproducibility in clinical informatics machine learning needs better data preparation reporting. We propose FAIR4prep best practices and a schema for transparent, standardized preprocessing documentation to enhance data sharing and reuse.

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Area of Science:

  • Clinical Informatics
  • Machine Learning
  • Data Science

Background:

  • Reproducibility in clinical informatics machine learning is hindered by inconsistent data preparation reporting.
  • Lack of standardized preprocessing documentation limits pipeline adaptability and reuse when sharing code and datasets.

Purpose of the Study:

  • To establish best practices for reporting data preprocessing in clinical informatics.
  • To improve the reproducibility, adaptability, and reusability of machine learning pipelines in healthcare.

Main Methods:

  • Development of a framework with minimum, actionable principles for reporting preprocessing steps.
  • Alignment of proposed practices with the FAIR principles (Findable, Accessible, Interoperable, Reusable).
  • Introduction of FAIR4prep, a JSON-LD schema for machine-readable implementation of these best practices.

Main Results:

  • A proposed set of best practices for transparent and consistent reporting of data preparation.
  • A JSON-LD schema (FAIR4prep) to operationalize machine-readable, FAIR-aligned preprocessing documentation.
  • A foundation for improved understanding, reuse, and comparison of preprocessing pipelines across studies.

Conclusions:

  • Standardized reporting of data preprocessing is crucial for machine learning reproducibility in clinical informatics.
  • The FAIR4prep framework and schema promote transparency, collaboration, and reliable translation of AI tools into clinical practice.