Table of content

  • Challenges in applying AI in Healthcare

  • Lack of High-Quality Labeled datasets

  • Best practices to manage medical labeling projects

  • Summary

Medical Labeling: Best practices to generate high-quality labeled datasets

The healthcare industry has witnessed the remarkable growth of artificial intelligence (AI), which has found diverse applications. As technology advances, AI’s potential in healthcare continues to expand. Nevertheless, certain limitations currently hinder the seamless integration of AI into existing healthcare systems.

AI is used in healthcare datasets to analyze data, provide clinical decision support, detect diseases, personalize treatment, monitor health, and aid in drug discovery. It enhances patient care, improves outcomes, and drives advancements in the healthcare industry. Many AI services such as Amazon Comprehend Medical, Google Cloud Healthcare API, John Snow Labs provide pre-built models. Due to variety of medical data and requirements for accuracy human in the loop techniques are important to safeguard accuracy. However, the success of AI and ML models largely depends on the quality of the data they are trained on, necessitating reliable and accurate data labelling services.

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Challenges in applying AI in Healthcare

Extensive testing of AI is necessary to prevent diagnostic errors, which account for a significant portion of medical errors and result in numerous deaths each year. While AI shows promise for accurate diagnostics, concerns remain regarding potential mistakes. Ensuring representative training data and effective model generalization are crucial for successful AI integration in healthcare.

In the healthcare sector, ensuring the privacy and security of patient data is paramount, as it not only fosters trust between healthcare providers and patients, but also complies with stringent regulatory standards such as HIPAA, promoting ethical, responsible data handling practices.

Lack of High-Quality Labeled datasets

Achieving a high quality labeled medical dataset poses several challenges, including:

  • Medical Labeling Skills:-Properly labeling medical data requires specialized domain knowledge and expertise. Medical professionals or trained annotators with a deep understanding of medical terminology and concepts are necessary to ensure accurate and meaningful annotations.
  • Managing Labeling Quality:-Maintaining high-quality labeling is crucial for reliable and trustworthy datasets. Ensuring consistency, accuracy, and minimizing annotation errors is challenging, as medical data can be complex and subject to interpretation. Robust quality control measures, including double-checking annotations and inter-annotator agreement, are necessary to mitigate labeling inconsistencies.
  • Managing the Cost of Labeling:-Labeling medical datasets can be a resource-intensive process, both in terms of time and cost. Acquiring sufficient labeled data may require significant financial investment, especially when specialized expertise is involved. Efficient labeling workflows, leveraging automation when feasible, can help manage throughput and reduce costs without compromising data quality.
  • Data Privacy and Security:-Safeguarding patient privacy and ensuring secure handling of sensitive medical data is crucial when collecting and labeling datasets.
  • Data Diversity and Representativeness:-Ensuring that the dataset captures the diversity of medical conditions, demographics, and healthcare settings is essential for building robust and unbiased AI models.

Best practices to manage medical labeling projects

Addressing these challenges requires a combination of domain expertise, quality control measures, and optimizing labeling processes to strike a balance between accuracy, cost-effectiveness, and dataset scale.

At Objectways we follow the Best Practices in medical labeling which include

  • Adherence to Guidelines:-Familiarize labeling teams with clear and comprehensive guidelines specific to the medical domain. Thoroughly understanding the guidelines ensures consistent and accurate labeling.
  • Conducting KPT (Knowledge, Process, Test):-Provide comprehensive training to labeling teams on medical concepts, terminology, and labeling procedures. Regular knowledge assessments and testing help evaluate proficiency of labeling teams and ensure continuous improvement.
  • Robust Team Structure:-We have established a structured team comprising labeling personnel, spot Quality Assurance (QA) reviewers, and dedicated QA professionals. This structure promotes accountability, efficient workflow, and consistent quality.
  • Quality Metrics:-We have implemented appropriate quality metrics such as precision, recall, and F1 score to assess labeling accuracy. We regularly monitor and track these metrics to identify areas for improvement and maintain high-quality standards.
  • Continuous Feedback Loop:-We have established a feedback mechanism where labeling teams receive regular feedback on their performance. This helps address any inconsistencies, clarify guidelines, and improve overall labeling accuracy.
  • Quality Control and Spot QA:- By implementing robust quality control measures, including periodic spot QA reviews by experienced reviewers, helps identify and rectify any labeling errors, ensures adherence to guidelines, and maintains high labeling quality.
  • Data Security and Privacy:- To validate our commitment to security and privacy controls, we have obtained the following formal certifications SOC2 Type2, ISO 27001, HIPAA, and GDPR. These certifications affirm our dedication to safeguarding customer data. Our privacy and security programs continue to expand, adhering to Privacy by Design principles and incorporating industry standards and customer requirements from various sectors.


At Objectways we have a team of certified annotators, including medical professionals such as nurses, doctors, and medical coders. Our experience includes working with top Cloud Medical AI providers, Healthcare providers and Insurance companies, utilizing advanced NLP techniques to create top-notch training sets and conduct human reviews of pre-labels across a wide variety of document formats and ontologies, such as call transcripts, patient notes, and ICD documents. Our DICOM data labeling services for computer vision cover precise annotation of medical images, including CT scans, MRIs, and X-rays and expert domain knowledge in radiology to ensure the accuracy and quality of labeled data.

In summary, the effectiveness of AI and ML models hinges significantly on the calibre of the data used for training, underscoring the need for dependable and precise data labeling services. Please contact to enhance your AI Model Performance