Driving the future of healthcare with medical data annotation
Studies reveal that the global data annotation market was valued at $629.5 million in 2021, with a compound annual growth rate (CAGR) of 26.6% from 2022 to 2030. The healthcare sector is a major component that drives these numbers. With a focus on AI-/ML-based technologies for medical diagnosis and treatment, data annotation plays a key role in streamlining data sets.
Technologies such as artificial intelligence (AI), machine learning (ML), the internet of things (IoT), robotic process automation (RPA), and predictive analysis generate massive amounts of data. Thus, the healthcare sector is partnering with medical data annotation experts to enhance their ML and deep learning capabilities.* Here we discuss the necessity for data annotation as well as its benefits and use cases in the healthcare sector.
The role of data annotation in the healthcare sector
Annotation enhances the value of big data by deep learning and data labelling for medical applications. Data annotation tools possess a combination of data attributes, which eliminate the requirement for rewriting rules in multiple places. To identify injuries and patterns among patients, AI-based machines use computer vision in medical imaging data technologies. This helps medical practitioners generate diagnosis reports automatically. AI technology scans a database of CT and MRI scans and X-ray images for accurate diagnosis.
Data annotation applications in healthcare
Annotation creates high-quality data to train the AI/ML models within the healthcare sector. Over the years, this training has enabled AI/ML modules to analyse, prescribe, and conclude data to gain accurate insights as well as the enhance the following AI applications with data annotation.
Remote monitoring sensors accelerate the identification of illness, monitor health status, schedule doctor appointments, and raise reminders for medication.
Data annotation: Facial recognition, key point annotation, gesture recognition, and data extraction from wearable devices.
Conversational bots assist in symptom checking, escalating emergency cases, scheduling doctor appointments, and patient engagement.
Data annotation: Entity recognition and sentiment/intent analysis.
This involves identifying and rectifying human errors in CT, MRI, and ultrasound scans and X-rays. AI can increase the speed and accuracy and lower costs. For example, AI can detect COVID-19 pneumonia and perform embryo classification.
Data annotation: Medical images from CT and MRI scans and X-rays.
AI with thermal sensors can detect breast cancer by visually displaying the amount of infrared energy emitting from the tumour. The system can quickly parse multiple patients and identify symptomatic patients for additional investigation.
Data annotation: Thermal image annotation for breast cancer and identifying patients with high body temperatures.
Pattern recognition for drug development
ML algorithms can accelerate the search for chemical and biological interactions, which is useful in drug development. Analysing a huge volume of data from research papers, patents, clinical trials, and patient records can accelerate the development of new drugs to the market faster. It can also create billions of inferred relationships among genes, diseases, symptoms, proteins, tissues, species, and candidate drugs.
Data annotation: Natural language processing (NLP) that recognises entities, attributes, and relationships between attributes.
Computer vision–driven autonomous robotic surgery will make treatments better and safer. Specialised annotation teams label critical structures in millions of frames in surgical videos.
Data annotation: Lesion detection and phase identification.
Medical annotation use cases
Medical annotation labels images from X-rays and CT, MRI, ultrasound, and PET scans to train the ML model. Typical use cases include the following areas.
Medical annotation creates an image data set of different organs and helps medical practitioners identify any abnormalities with a quick, deep, and accurate analysis.
AI models with medical image annotation can predict cancer using labelled cancer image data. This reduces the possibility of human error and helps in the early detection of various types of cancers.
It visualises the teeth structure and detects tooth decay, cavities, gum disease, and other abnormalities. X-ray based image data sets train ML models in deep learning.
The ML model can learn to detect bone fractures accurately using a data set of X-ray images.
By annotating medical records, the ML model can identify information and automatically extract it. This helps the staff find and process documents faster.
How can Infosys BPM help?
Infosys BPM assists data science teams build high-quality training data for their AI systems. By combining human capability with state-of-the-art intelligent automation, we can increase the speed to market and deliver accurate training data. Our data annotation capabilities include:
- Text annotation
- Image automation
- Audio annotation
- Video annotation
- Sensor data annotation
View the annotation services for AI/ML from Infosys BPM.
*For organisations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed on organisational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like a living organism will be imperative for business excellence going forward. A comprehensive, yet modular suite of services is doing exactly that. Equipping organisations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organisations that are innovating collaboratively for the future.