How Enhancing Medical Data Annotation Workflows Makes for Better AI Training

Medical diagnostics are rapidly evolving, driven by advancements in artificial intelligence (AI). AI-powered models are increasingly crucial for more accurately diagnosing conditions, identifying patterns and making predictions. But the effectiveness of these models hinges heavily on the quality of training data. And in a rapidly evolving medical and technology environment, time is of the essence. That’s why improving workflows related to medical data annotation and segmentation of imaging files is so important to advancing patient care.  

In this blog, we’ll dive into how you can accelerate medical data annotation workflows to enhance data quality, ultimately refining AI’s ability to function proficiently in clinical and research settings. 

Challenges in Medical Data Annotation 

Labeling datasets with relevant information helps make them usable for machine learning models. The more precise and comprehensive the annotations, the better an AI model can learn to mimic human diagnostic processes. But creating high-quality annotations is challenging, requiring domain expertise, accuracy, efficiency and scale. 

The process is intrinsically complex due to the technical nature of medical terminology and the variety of medical conditions that exist. Labor-intensive and time-consuming tasks can lead to errors and inconsistencies.  

Common issues include: 

Strategies for Enhancing Annotation Workflows 

Below, we detail some of the topline strategies to undergo when looking to improve medical data annotation. 

Standardization of Annotation Guidelines 

To ensure consistency across your annotation workflows, it’s crucial to establish clear and standardized guidelines. Because multiple annotators often work on large datasets, these guidelines should detail every step, minimize subjective interpretations and incorporate examples to illustrate key points. Tactics like implementing concise and clear instructions with exemplary images can help derive better results. 

Use Automation  

Due to the size of datasets needed for model training and the time that would be needed to process these datasets manually, you’ll need automation pipelines to complete this work efficiently. Implementing containerized algorithms and chaining these together into annotation pipelines can help you rapidly process datasets, suggest preliminary annotations, and identify errors needing human intervention.  

Crowdsource Remote Readers 

While expert annotation may be the gold standard, it may also not be practical to get the kind of massive datasets needed to effectively train AI models. Studies have shown that crowdsourcing annotators may help obtain a more usable training set for further machine learning tasks as long as the crowdsourced training data contains more correctly labeled data than incorrect due to the volume of annotated data you can obtain this way. 

Implement Quality Assurance Protocols 

Given that you may be outsourcing this work, setting up detailed annotation review is key to keeping quality in check. Expert annotators should work through various checks to make sure everything is accurate and consistent. Use tools like random sampling, peer reviews and automated checks to identify and mitigate discrepancies early in the annotation process. 

Harness the Potential of a Medical Imaging Platform 

Utilizing a medical imaging platform can help you streamline your entire medical data annotation workflow, leading to improved efficiency and accuracy for better AI training datasets. This platform should allow you to centralize imaging data and then share it with contributors securely while maintaining compliance with regulations such as HIPAA. And, ideally, you could implement containerized algorithms within this same environment and view medical video as the same time as imaging files. 

Enabling Medical Imaging Annotation on the Flywheel Platform 

Flywheel is designed to help medical researchers and life sciences organizations not only manage their imaging data but also prepare it for AI development through accelerated annotation workflows. Flywheel empowers researchers to securely ingest and manage massive amounts of data in the cloud or in hybrid environments, then securely open those data sets to contributors for data labeling, contouring and segmentation.  

The platform helps you automate routine tasks with built-in or custom containerized algorithms. Task management, guided workflows, and agreement analytics help you reduce the time and resources needed to run reader studies. 

In addition, Flywheel enables organizations to: 

Learn more about how Flywheel enables research and life sciences organizations to expedite their medical data annotation here. And be sure to schedule a demo to see how you can start turning your raw data into analysis-ready datasets.