What question did this study set out to answer?

To provide laboratory professionals with accessible guidance on machine learning in chemical pathology.

February 22, 2026

Laboratory-developed machine learning in chemical pathology: an interactive guide from problem formulation to model evaluation

Key Points

To provide laboratory professionals with accessible guidance on machine learning in chemical pathology.
Discussed core concepts of machine learning and its applications in laboratory medicine
Identified challenges faced by laboratory professionals in implementing ML
Offered structured guidance from problem formulation to model evaluation
Promoted understanding of machine learning applications
Highlighted the need for practical guidance in laboratory settings
Emphasized the role of AI in transforming healthcare through improved laboratory practice

Abstract

Artificial intelligence is transforming healthcare, including the field of laboratory medicine. Nonetheless, laboratory-driven machine learning (ML) initiatives remain uncommon, often due to the perceived complexity and limited practical guidance available. To bridge this gap, laboratory professionals need accessible opportunities to learn the core concepts, applications, and challenges of machine learning.

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