Medical Labeling: Best practices to generate high-quality
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
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.
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
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
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
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
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
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