The global market for artificial intelligence in the healthcare sector is estimated to rise from $ 1.426 billion in 2017 to $ 28.04 in 2025. The increase in the demand for artificial intelligence-based technologies is becoming apparent as the healthcare industry is always looking for ways to improve care, reduce costs, and ensure accurate decision-making.
Depending on the complexity of the project, the in-house team can’t always manage healthcare data labeling needs. As a consequence, the business is forced to seek quality datasets from reliable third-party providers.
But there are a few complications and challenges when you seek outside help for Healthcare data labeling. Let’s look at the challenges, and the points to note before outsourcing healthcare dataset labeling services.
The Importance of Data Labeling in Healthcare
Accurate data labeling is crucial for the development of AI-powered solutions in healthcare. Some of the key reasons why data labeling is essential in healthcare include:
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Improved Diagnostic Accuracy: Accurately labeled medical images and data help train AI algorithms to detect diseases and abnormalities with higher precision, leading to earlier detection and better patient outcomes.
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Enhanced Patient Care: Well-annotated healthcare data enables the development of personalized treatment plans, predictive analytics, and clinical decision support systems, ultimately improving patient care.
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Compliance with Regulations: Healthcare data labeling must adhere to strict privacy and security regulations such as HIPAA and GDPR. Ensuring compliance is essential to protect sensitive patient information and avoid legal consequences.
Best Practices for Healthcare Data Annotation
To ensure the success of your healthcare AI projects, consider the following best practices when outsourcing data labeling:
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Domain Expertise: Work with a data labeling partner that has domain expertise in healthcare. They should have a deep understanding of medical terminology, anatomical structures, and disease pathologies to ensure accurate annotations.
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Quality Assurance: Implement a rigorous quality assurance process that includes multiple levels of review, regular audits, and continuous feedback loops to maintain high-quality data labeling.
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Data Security and Privacy: Choose a data labeling partner that follows strict data security and privacy protocols, such as working with de-identified data, using secure data transfer methods, and regularly auditing their security measures.
Challenges Facing Healthcare Data Labeling
The importance of having a high-quality medical dataset and annotated images is crucial to the outcome of the ML models. Improper image annotation can bring inaccurate predictions, failing the computer vision project. It could also mean losing money, time, and a lot of effort.
It could also mean drastically incorrect diagnosis, delayed and improper medical care, and more. That is why several medical AI companies seek data labeling and annotation partners with years of experience.
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Challenge of Workflow management
One of the significant challenges of medical data labeling is having enough trained workers to handle extensive structured and unstructured data. Companies struggle to balance increasing their workforce, training, and maintaining quality.
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Challenge of Maintaining Dataset quality
It is a challenge to maintain consistent dataset quality – subjective and objective.
There is no single foundation of truth in subjective quality as it is subjective to the person annotating the medical data. The domain expertise, culture, language, and other factors can influence the quality of work.
In objective quality, there is a single unit of the correct answer. However, due to the lack of medical expertise or medical knowledge, the workers might not undertake image annotation accurately.
Both the challenges can be resolved with extensive healthcare domain training and experience.
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Challenge of Controlling costs
Without a good set of standard metrics, it is not possible to track the project results based on the time spent on data labeling work.
If the data labeling work is outsourced, the choice is usually between paying hourly or per task performed.
Paying per hour works out well in the long run, but some companies still prefer paying per task. However, if workers are paid per task, the quality of work might take a hit.
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Challenge of Privacy Constraints
Data privacy and confidentiality compliance is a considerable challenge when gathering large quantities of data. It is particularly true for collecting massive healthcare datasets since they might contain personally identifiable details, faces, from electronic medical records.
The need to store and manage data in a highly secure place with access controls is always strongly felt.
If the work is outsourced, the third-party company is responsible for acquiring compliance certifications and adding an extra layer of protection.