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Related Experiment Video

Updated: Aug 26, 2025

Finite Element Modelling of a Cellular Electric Microenvironment

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

Building the Model.

He S Yang¹, Daniel D Rhoads^2,3, Jorge Sepulveda⁴

¹From the Department of Pathology and Laboratory Medicine (Yang, Chadburn), Weill Cornell Medicine, New York, New York.

Archives of Pathology & Laboratory Medicine

|October 12, 2022

Summary

This summary is machine-generated.

Machine learning (ML) offers powerful tools for analyzing clinical laboratory data. Careful attention to data quality, model development, and evaluation is crucial to avoid bias and ensure reliable results in laboratory medicine.

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Last Updated: Aug 26, 2025

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Area of Science:

Clinical laboratory science
Biomedical data analysis
Artificial intelligence in healthcare

Background:

Machine learning (ML) enables analysis of large, high-dimensional clinical laboratory data, identifying complex patterns.
ML has the potential to enhance clinical data interpretation and laboratory medicine practices.
Risks include biased models leading to misleading conclusions or overestimated performance.

Conclusions:

Educating laboratorians and clinicians on ML is vital for effective implementation.
Domain-specific knowledge is indispensable throughout the ML model development workflow.
Proper data handling and rigorous evaluation are key to trustworthy ML applications in clinical settings.