The global market for artificial intelligence in the healthcare sector is estimated to rise from $ 1.426 billion in . 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. 2017 to $28.04 in 2025 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. Challenges Facing Healthcare Data Labeling . Improper image annotation can bring inaccurate predictions, failing the computer vision project. It could also mean losing money, time, and a lot of effort. The importance of having a is crucial to the outcome of the ML models high-quality medical dataset and annotated images 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. Also published . here

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