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Updated: Jan 12, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Nine quick tips for trustworthy machine learning in the biomedical sciences.

Luca Oneto¹, Davide Chicco^2,3

¹Dipartimento di Informatica Bioingegneria Robotica e Ingegneria dei Sistemi, Università di Genova, Genoa, Italy.

Plos Computational Biology

|October 30, 2025

Summary

This summary is machine-generated.

Researchers need trustworthy machine learning (ML) models for biomedical science. This paper offers nine actionable tips to build technically sound, ethically responsible, and contextually appropriate ML systems for reliable biomedical applications.

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Area of Science:

Biomedical research
Machine learning
Artificial intelligence in healthcare

Background:

Machine learning (ML) is increasingly integral to biomedical research.
Ensuring the trustworthiness of ML models in this sensitive field is critical.
Existing methods may not fully address the ethical and contextual needs of biomedical applications.

Purpose of the Study:

To provide nine actionable tips for building trustworthy machine learning systems in biomedical research.
To guide researchers in creating ML models that are technically sound, ethically responsible, and contextually appropriate.
To address the multifaceted nature of trustworthiness in ML for healthcare.

Main Methods:

The study outlines practical recommendations for ML system development.
It emphasizes integrating trustworthiness throughout the ML pipeline, from design to deployment.
Guidance is provided on defining trustworthiness and mitigating untrustworthiness.

Main Results:

Nine concise and actionable tips are presented to enhance ML trustworthiness.
The recommendations cover technical, ethical, and domain-specific considerations.
Strategies for addressing potential consequences and stakeholder needs are discussed.

Conclusions:

Embedding trustworthiness into every stage of the ML pipeline is essential.
These recommendations support both novice and experienced practitioners.
The goal is to foster the creation of reliable ML systems for biomedical science.