The data annotation market globally was valued at $630 Mn in 2021 and will grow at a CAGR of 26% till 2030. The healthcare sector is a key contributor to its growth, with the increasing adoption of AI/ML-based technologies for medical diagnosis. With artificial intelligence, machine learning, the Internet of Things (IoT), Robotic Process Automation (RPA), and other technologies generating large amounts of datasets, healthcare companies are working with medical data annotation companies to enhance the performance of their models.
Let us understand Medical Data Annotation in detail and how it impacts the future of Healthcare.
Achieving High Accuracy in Medical Data Annotation
In most developed economies, the healthcare sector attracts the strictest regulations regarding patient medical records privacy, data protection, and overall product safety standards. Low-quality data sets will adversely affect the accuracy and reliability of AI and increase risks of non-compliance.
As noted, AI developers need vast amounts of quality data from multiple sources to build an effective AI model. This data could be images, videos, audio files, or text. The challenge is that one cannot simply feed raw data to AI algorithms. They must be correctly annotated using best-in-class annotation tools and labeled with meaningful and informative tags to provide some reference or context to the machine. This manual or automatic data labeling process for making data easily readable and understandable by ML algorithms is called data annotation or data labeling.
In a perfect world, AI firms would employ professionally certified radiologists and experienced radiographers to handle all medical image data annotation projects. But this is neither feasible nor affordable in a modern healthcare market where experts are in short supply at the most critical positions – on the frontlines of hospitals. Moreover, delivering high-quality machine-learning data requires the right mix of technologies, techniques, and talent. In a typical AI project, the data annotation and labeling can take up 80% of the development time if left in-house.
Companies like iMerit have data labelers carefully chosen based on selection criteria involving educational qualifications, aptitude for video/image annotations, pattern recognition, and agile learning capabilities to work on specific annotation requirements of medical data labeling projects. At iMerit, we only select candidates that exceed a high skill threshold for medical data labeling. Upon selection, they undergo domain-specific and project-specific training by our in-house medical experts. We have extensive experience providing specialized annotation services for 20 million data points across the healthcare sector.
Continuous monitoring, skillset, experience, expertise, client feedback, and high-performing tools are all required for achieving highly-accurate data annotation of medical data.
How Data Annotation Enables Medical AI
Any clinical intelligence engine needs training on two main types of data – medical histories and case studies built by clinicians and the other being real-world clinical data. All this data is then transformed into structured data through annotation for the machine to interpret the correct meaning.
Data annotation involves extracting and encoding clinical information, including concepts, entities, events, and relationships in text, image, video and audio. Such structured data sets are used to build data solutions that help triage patients and guide clinical decision-making.
Here are medical AI applications that require high-quality training data and, therefore, also need the expertise of medical data annotators.
AI has proven to be a valuable innovation for radiologists and pathologists, helping them with high-resolution imaging captured in X-rays, CAT scans, MRIs, and other relevant tests, challenging even the most experienced radiology professional. Some of the top applications of AI in clinical imaging include detecting cardiovascular abnormalities, diagnosis of neurological conditions, early detection of common cancers, and detecting fine fractures and musculoskeletal and thoracic complications.
In this case, medical data experts annotate or label the medical images with specific features, such as regions of interest, anatomical structures, or abnormalities, enabling the algorithms to provide diagnostic suggestions in real-time.
Robotic-Assisted Surgery and Endoscopy
Robotic surgery, also called robot-assisted surgery, allows doctors to have enhanced precision, flexibility, and control during the operation, enabling them to better see the site, compared to traditional techniques. Similarly, robotic-assisted endoscopy involves using a robotic system to assist with inserting and maneuvering an endoscope through the patient’s body for diagnostic purposes.
In both cases, AI algorithms are trained on large datasets of medical images to detect and classify abnormalities more accurately, potentially reducing the need for additional testing or procedures. The data annotation projects may include instrument tracking, lesion detection, and phase identification.
Digital pathology is another critical application that uses high-quality training data reviewed by medical data specialists. Using machine learning and image analysis, AI in pathology primarily interprets digital slide images. A task, such as producing a diagnostic, calculating a score, or completing a subtask, like classifying cells into various cell types, can be inferred from machine learning data.
Robotic Process Automation
RPA can provide task automation across the organization, from front-office tasks to operational processes. AI models require high-quality training data to power the most accurate and sophisticated RPA technology, from claims processing to inventory management to billing. These advancements reduce costs and free up resources across the healthcare industry.
Biomechanics/Sports or Behavioral Medicine
The biomechanical analysis reviews an athlete’s form to make adjustments for improving their performance. This technology uses AI to check footage of an athlete’s movements to find faults in their form, such as spinning hips or bending knees, and to see how their movement patterns differ from others in their sport. One of Major League Baseball’s go-to motion capture vendors, Kinatrax, relies on iMerit to annotate their motion capture data, so their algorithms generate intelligent pitching insights.
Conversational AI & Virtual Nursing Assistants
Data annotation teams trained in standardized medical ontologies provide companies with structured datasets necessary to power next-generation conversational AI. These algorithms today enable virtual nursing assistants to help patients identify illnesses, monitor their status, schedule appointments, and more.
We discussed the most popular medical AI applications and the associated use case for data annotation. The quality of training data will depend on the technology, technique, and expertise of the people involved. It will determine how effectively AI models can support clinical decisions and improve patient care.