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Artificial intelligence in diagnostic software: validation, safety, and lifecycle challenges in Europe.

Bertok Tomas1, Pankuchova Ivana2, Jane Eduard3

  • 1Institute of Chemistry, Slovak Academy of Sciences, Dubravska cesta 9, Bratislava 845 38, Slovak Republic; Faculty of Pharmacy, Comenius University in Bratislava, Ulica odbojarov 10, Bratislava 832 32, Slovak Republic.

Clinica Chimica Acta; International Journal of Clinical Chemistry
|June 30, 2026
PubMed
Summary

This study introduces machine learning (ML) applications in healthcare, covering clinical practice and research. It emphasizes data quality, validation, and regulatory considerations for safe AI integration in medical devices.

Keywords:
AI actArtificial intelligenceClinical validation, diagnosticsDigital healthMachine learningSoftware as a medical device

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Biomedical Research

Background:

  • Artificial intelligence (AI), encompassing machine learning (ML) and deep learning, is increasingly integrated into public healthcare.
  • AI applications span routine clinical practice, biomedical research, and biomarker discovery.
  • Understanding AI's role is crucial for healthcare advancement.

Purpose of the Study:

  • To provide a comprehensive introduction to ML applications in clinical practice and biomedical research.
  • To discuss the characteristics, strengths, and limitations of widely used ML models.
  • To outline principles for interpreting model outputs and engaging with regulatory bodies.

Main Methods:

  • Introduction to common machine learning models and their properties.
  • Discussion of principles for model output interpretation and regulatory communication.
  • Illustrations using real-world and synthetic datasets in Python.
  • Emphasis on data quality, validation, data governance, and interpretability.

Main Results:

  • Key machine learning models are detailed with their pros and cons.
  • Methods for interpreting AI model outputs and communicating with regulators are presented.
  • The importance of data quality, robust validation, and governance for AI systems is highlighted.
  • Current trends, benefits, and risks of medical AI are reviewed.

Conclusions:

  • Safe and effective AI implementation in healthcare requires data governance, external validation, interpretability, and clinical oversight.
  • AI integration presents both opportunities and challenges for modern healthcare systems.
  • A deeper understanding of AI in healthcare is facilitated by this overview.