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A Perspective on Artificial Intelligence for Molecular Pathologists.

Timothy J O'Leary1, Brendan J O'Leary2, Dianne P O'Leary3

  • 1Office of Research and Development, Veterans Health Administration, Washington, District of Columbia.

The Journal of Molecular Diagnostics : JMD
|February 15, 2025
PubMed
Summary
This summary is machine-generated.

Next-generation sequencing generates vast data, driving automation in molecular pathology. Machine learning and artificial intelligence are crucial for disease diagnosis and treatment guidance, requiring careful validation and bias assessment.

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Area of Science:

  • Computational biology
  • Genomics
  • Pathology

Background:

  • Next-generation sequencing (NGS) generates unprecedented genomic data.
  • Complex human and microbial genetics necessitate advanced analytical tools.
  • Automation is essential for timely disease diagnosis and patient treatment.

Purpose of the Study:

  • To review the fundamental concepts of machine learning (ML) and artificial intelligence (AI) in molecular pathology.
  • To discuss the collaborative roles of pathologists and data scientists in developing and implementing AI tools.
  • To highlight critical considerations for adopting AI in molecular pathology, including validation, regulation, and bias.

Main Methods:

  • Review of machine learning and artificial intelligence concepts.
  • Discussion of automation in molecular pathology.
  • Analysis of regulatory and professional society guidance for AI validation.
  • Examination of potential sources of bias in AI systems.

Main Results:

  • ML and AI are integral to automating genomic data analysis in pathology.
  • Collaboration between pathologists and data scientists is key for tool development.
  • Validation, regulatory compliance, and bias mitigation are critical for AI adoption.
  • Future advancements in computer science will further impact AI in pathology.

Conclusions:

  • AI and ML are transforming molecular pathology by enabling efficient analysis of complex genomic data.
  • Successful integration requires interdisciplinary collaboration, rigorous validation, and awareness of potential biases.
  • Ongoing research in computer science promises further innovation in AI-driven pathology.