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:
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.
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.
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:
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.
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.
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.
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.
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.
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.
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.
Questions to Ask When Outsourcing Healthcare Data Labeling Work
Who is going to label the data?
The first question you should ask is about the data labeling team. Any training data labeling team performs well, doing regular tasks. But with training on domain-specific terms and concepts by medical experts, they would be able to develop datasets that match the competency required by the project.
Moreover, with a larger workforce, when the data labeling task is outsourced, it becomes easier to divide the work evenly among significant sections of experienced and trained annotators. Tracking, collaboration, and uniformity in quality can also be maintained.
- Ask for a sample review of the completed tasks. Look for accuracy in the datasets.
- Understand their training and recruitment criteria. Learn more about their training methods, quality benchmarks, moderation, and validation checklists.
Is it scalable?
The data labeling service provider should have a well-trained, healthcare domain team that can start quickly and scale quickly. You should work with exclusively healthcare experts that can ramp up work while maintaining quality.
Internal VS External Teams – Which is Better?
Choosing between internal and external teams is always an act of delicate balance. But start weighing these two based on the time taken for delivery, cost of scaling data labeling services, and specific healthcare experience.
An internal team might not have the required healthcare expertise and require extensive training to stand on par with the experts. But an external workforce could have medical dataset labeling expertise, making them ideal candidates to start and scale quickly.
When the experience in medical and health sciences is combined with advanced tools, you can see a considerable reduction in the cost and time of data processing.
Do they meet the Regulatory Requirements?
The correct data processing team should be trained to perform their tasks securely. The team should be prepared by medical experts or data scientists to ensure electronic health records of patients remain anonymous.
The third-party services providers will handle patient privacy regulations, including HIPAA and GDPR compliance certifications. Choose image annotation services with an ISO-9002 certificate that proves that they take stringent measures to maintain clients’ data privacy and organization.
How does the provider maintain Communication with the managed workforce?
Choose a data labeling partner who strives to maintain clear and regular communication to avoid discrepancies in instructions, requirements, and project demands. A lack of communication, real-time exchange of project-critical information, and an inadequate feedback loop system can adversely affect the quality of work and delivery deadlines. It is essential to choose a third party that uses the latest collaboration tools and has proven systems to detect productivity issues before it starts to affect the project.
Case Study: Medical Image Annotation for AI-Powered Radiology
A leading healthcare technology company partnered with Shaip to develop an AI-powered radiology solution. Shaip provided high-quality medical image annotation services, labeling thousands of CT scans and MRIs with precise anatomical structures and abnormalities. By working with Shaip’s team of experienced healthcare data annotators, the company was able to train its AI algorithms to detect diseases with high accuracy, ultimately improving patient outcomes and reducing healthcare costs.
Conclusion
Shaip is a industry leader in providing top-notch specialized medical data labeling services to critical projects. We have an exclusive team of healthcare experts trained by the best medical experts on best-in-class labeling solutions. Our experience, skill, stringent training modules, and proven quality assurance parameters have made us the most preferred data-labeling service partners for large businesses.
Ready to ensure the success of your healthcare AI projects with high-quality data labeling? Contact Shaip today to learn how our experienced healthcare data annotation team can help you achieve your goals while maintaining the highest standards of quality and compliance.Open Source Healthcare Datasets for Machine Learning Projects
