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Best data annotation application in the healthcare industry

July 6, 2022

What is data annotation application in the healthcare industry?

With the current advancement in technology, the healthcare industry is under huge pressure. They are expected to improve the quality of care and patient experiences, while continually lowering the cost associated with healthcare. This is thus driving investments in smart solutions for better decision-making. Data annotation application in the healthcare industry is the application and labeling of medical imaging data in the healthcare industry which improves diagnostics, treatment and increases medical accuracy.

What is medical data annotation?

Medical Image Annotation involves the process of labeling medical imaging data which includes X-Ray, CT, MRI scans, or Ultrasound to train a machine learning model. AL and Computer Vision enables easier and faster patient care and has ultimately changed the healthcare industry. For instance, medical data annotation improves diagnostics and helps to quickly discover and transport new and improved drugs. Again, because of medical data annotation, it limits human intervention because new medical innovations are constantly being discovered which provide accurate diagnostics.

The Purpose of a Medical Annotation

Medical annotating involves the process of annotating medical files and clinical data. It also helps to perform quality controls on the processed outcome. Its main purpose includes the following:

1. It helps to bring attention (E.g. Use of boxes, circles, or arrows) areas or regions of prime interest.

2. It aids in the training of computer models so that they can recognize certain key features by adding metadata to an image.

3. Data annotation in healthcare is an important aspect of building up software based on Artificial Intelligence. Medical image annotators may perform diverse types of annotation such as segmentation, which involves classifying single pixels and classifying a whole image within a dataset. 

4. Again, in medical annotation, annotated images are required to train models to be highly accurate. In doing this, a huge amount of such data is needed for AI solutions to make accurate assessments and predictions.

Best Practices for Medical Annotation

1. Create and prepare an image dataset

To help train a dataset to be quicker, an image in a dataset that has varied sizes must be resized to be equal.

2. Identifying the class labels

In identifying the objects in an image, class labels need to be specified. This is what will be used in training a model. For instance, in diagnosing cancer, there must be two class labels. Class 1 will be the healthy label and the class 2 will be cancer. Hence, if certain characteristics or attributes of cancer are detected, then, the model will determine that it is not healthy and will then assign it to the cancer class.

3. Box size diversity

In image annotation projects, bounding boxes have been identified to be one of the most popular techniques. Bounding box adds more meaning and description to the location of an object in an image and the position of the object is represented with a rectangle. As such, it is vital to use different box sizes to train image data because it improves model performance. By training the model with different object sizes, it helps it to easily detect the same object even at different sizes.

Benefits of Medical Annotation

1. It helps Machine Learning models to learn from previous cases. 

2. Medical annotation can  provide predictions about new and unlabeled images. 

3. With such predictions, healthcare professionals can diagnose various types of diseases, including cancers and infections. 

4. Medical annotation helps you to annotate medical files and clinical data and perform quality controls on processed outcomes.

5. Medical Annotation can train Artificial Intelligence algorithms for medical image analysis and diagnostics, and this helps doctors to be time efficient, make informed decisions and improve patient outcomes.

Medical Annotation Use Cases

1. Medical Imaging

Medical Annotation is used in the creation of visualizations of specific organs. With this, medical teams can diagnose unusual defects in the human body that cannot be identified with the naked eye. It provides the medical professionals with an accurate analysis of their findings.

2. Cancer Detection

Training AI models with medical image annotation can help deep-learning models in the prediction of cancer.  Since these models are pre-trained with various labeled cancer images data, they can accurately predict cancerous cells. The trained model recognizes abnormal regions for new image data. It goes on to predict whether the patient is healthy or has cancer and in so doing, it reduces and helps to eliminate the possibility of human error and helps early detection of any form of cancers.

3. Dental Imaging

It provides an image of the tooth structure. It also indicates if there are any cavities between teeth and helps detect all issues that may be related to the teeth. By using X-Ray images, the dataset which is used to train the models is created.

4. Detecting Bone fractures

As mentioned already, X-ray permits the visualization of bone structures to annotate fractured areas. The model is then trained and fed annotated data to accurately detect and predict bone fractures.

5. Manage and Handle Medical Records

Medical annotation helps to manage and handle medical records and other important data.

6. Pathology

Medical annotation can also assist pathologists in making rapid and accurate diagnoses. Computation through Artificial Intelligence in Pathology has streamlined the process of detection and diagnosis of many critical conditions.

Best data annotation application in the healthcare industry

1. iMerit

It is an Indian and US based data annotation company that provides high quality data across computer vision and natural learning processing. iMerit collaborates to deploy AI and Machine Learning in Medical AI, and provides enriching, annotating and labeling data. Leading pharmaceutical companies, device manufacturers, health plans, and provider networks to deliver quality, secure, HIPAA-compliant data solutions both locally and offshore. iMerit combines the best of predictive and automated annotation technology with world-class data annotation and subject-matter experts to deliver the data you need to get to production, fast.

 2. Cogito

It creates high-quality annotated medical datasets to be used for building and enhancing various cutting-edge healthcare applications. We offer medical image annotation for deep learning segmentation of medical images through AI models. Cogito is helpful in healthcare training data as it has an established infrastructure that collects, classifies, and processes machine learning healthcare data with the highest quality and accuracy. Again, Cogito generates high-quality and structured medical datasets used in the creation of machine learning algorithms and computer vision systems.

3. Lynxcare

It is an AI-powered Big Data platform that provides healthcare facilities to improve clinical efficiency and increase the quality of healthcare provided to patients.

4. Capestart

It is a skilled data analyst, data scientists and developers that provide complex data annotation, machine learning and AI model development. It helps companies scale their AI innovation through data annotation, machine learning and software development for the healthcare, telecom, and various industries.

5. Alegion

It is a technology company based in Austin. It integrates human and machine intelligence to deliver high-accuracy data labeling for machine learning. It provides an industry-leading data labeling platform, that fully-manages data labeling services, and provides flexible solutions for every stage and type of data.

6. Ango Service

It prides itself as being the only labeling service focused on quality. They work with start-ups, insurance companies, banks, and even national security agencies. They manage data labeling service and data annotation platforms for AI teams.

How Data Annotation Application is helpful in the Healthcare Industry

1. Provides real-time data

Generally, AI (data annotation) is helpful in the healthcare industry as it provides real-time data. With AI, medical professionals can leverage accurate and instant data to advance critical clinical decision-making. AI helps to generate quicker and robust results that lead to improved healthcare services as it is cost and time effective.

2. Streamline Tasks.

Data annotation application in the healthcare industry has tremendously changed the healthcare sector. It has led to improved services in healthcare such as appointment-scheduling, translating clinical details and tracking patient histories. Due to the incorporation of AI in the medical field, tasks that were initially considered as cumbersome can now be streamlined.  AI is used to minimize costs resulting from insurance claim denials.

3. Saves time and resources

As already stated, AI in the healthcare sector saves time, energy, and resources. As there is the increase of automated processes, healthcare professionals can now have adequate time to attend to patients and diagnose illness. Unnecessary hours that were initially wasted on reviewing patient history and information can now be done with the use of data annotations. NLP algorithms can assist physicians in reviewing hospital cases and avoiding denials. This allows medical professionals to have more time to assist their patients.

4. Assists Research

AI enables researchers to accumulate and handle large sums of data from various sources and this provides them with adequate information to effectively analyze viral infections and deadly diseases. AI has also been used to assess and detect symptoms earlier in an illness’s progression.

Limitations of AI in the Healthcare Industry

There are some concerns about the impact of AI in the healthcare industry. Some of these concerns include an increase in unemployment. Even though it is argued that AI reduces unnecessary costs in the healthcare sector, it may increase the rate of unemployment in the society since most of the work that can be done by human beings is taken over by technology, thereby rendering some jobs redundant. Again, there is also the risk of bias, lack of clarity for some AI algorithms, privacy issues for data used for AI model training, and security issues and AI implementation responsibilities in clinical settings.

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