Medical AI Breakthroughs:

The use of medical images to improve medical diagnosis at the point of care is exploding. Every year in the U.S., it is estimated that nearly 40 million MRI scans, over 80 million CT scans, and 152.8 million X-rays are performed on patients. Additionally, millions of medical images are generated every year through other means, from robotic devices used in operating rooms to medical research and development.

And, just as the use of medical images for medical diagnosis, robotics, and research is exploding, so is the global market for computer vision in healthcare, which is expected to exceed more than $343.3 billion by 2024 and $416 billion by 2025.

Patient imaging produces a lot of image data in the USA every year. In order to train and refresh computer vision models, a large portion of these medical images, including CT scans, MRI scans, and X-rays, must be annotated precisely and securely.

Diving deeper into the computer vision market for the healthcare industry, there are three areas of medicine that stand to benefit the most from developments in AI, machine learning, and the training data that feeds these advanced algorithms:

• Processing and reading images in real-time to improve diagnoses, treatments, and prediction of diseases.
• Advancing the use of robotics and other medical devices.
• Improving the process of medical research and development.
  
  

While computer vision algorithms have made tremendous advances over the past few years, they are not perfect. In order to produce the best algorithms, medical data labeling and annotation must be high-quality, accurate, and cost-effective. This is especially important in high-stakes industries like healthcare where people’s lives and long-term health are at stake.

In machine learning, data labeling or data annotation is the process of structuring data to show the outcome you want your machine learning model to predict. You are marking - labeling, tagging, transcribing, or processing - a dataset with the features you want your machine learning system to learn to recognize in order to improve patient care, accelerate diagnosis and triage, or confidently introduce medical AI solutions to the market.

Labeled data is annotated to show the target or outcome you want your machine learning model to predict. Data labeling is an umbrella term that can entail any number of different types of tasks depending on the type of medical data and specific healthcare use case. Examples include data tagging, annotation, moderation, transcription, and processing. The process of data labeling involves marking a dataset with key features that will help train your algorithm. Labeled data explicitly calls out features that you have selected to identify in the data, and that pattern trains the algorithm to discern the same pattern in unlabeled data.

Data labeling supports a wide variety of use cases for healthcare such as identifying cancerous cells in chest X-rays to improve early diagnosis, transcribing doctors’ notes to streamline patient communications, or annotating surgery footage frame-by-frame to help train surgical robots.

Why is Training Data Key to Machine Learning?

No element is more essential in machine learning than large quantities of quality training data. Training data refers to the initial data used to develop a machine learning model, from which the model creates and refines its rules. The quality of this data has profound implications for the model’s subsequent development, setting a powerful precedent for all future applications that use the same training data.

This training data is used to train medical AI models for a variety of real-world applications: from making medical diagnoses and enhancing surgical robotics to accelerating complex research and development. Similar to the way an accountant has used the calculator to augment laborious and cumbersome calculations, medical AI models augment the work of medical professionals.

When trained properly, computer vision models often reveal insights that medical professionals, researchers, and statisticians may not readily spot—and often far more rapidly than a trained expert, such as a radiologist.

The data labeling tools and the teams using them for training and deploying machine learning models in healthcare can determine the success or failure of your AI project. Optimizing data labeling for medical AI requires the right technologies, tools, and teams to augment and support your existing workforce.

Choosing the right tool may not be a fast or easy decision. The data annotation tool ecosystem changes quickly in healthcare as more providers offer options for an increasingly diverse array of use cases. Tooling choices range from instant out-of-the-box functionality to completely customized. It is important to understand how teams can most effectively use the right tool to deliver increased productivity and higher quality.

In healthcare, where precision is required, every data annotation tool is meant to be used by a human workforce — even those tools that may lead with an AI-based automation feature. You still need humans to handle exceptions and quality assurance. Nearly every AI model requires refinement and optimization over time to ensure quality is well kept.

Selecting the right workforce to implement data annotation tools is as important as the tool itself.

Common Annotation Tasks in Healthcare and Medical AI

The potential uses of computer vision in healthcare are growing. However, today the applications mostly fall into one of three major categories:

1) Diagnostic and Clinical Imaging

Significant progress in AI and computer vision in the medical industry results in faster and more accurate diagnoses. An expanding choice of powerful tools supported by machine learning can automate the segmentation, tagging, and classification of millions of images from X-rays, CT scans, and MRIs generated every year. In addition, these systems can identify trends across multiple images or groups of images that are not always easily identifiable by medical professionals.

Annotating data from X-rays, ultrasounds, CT scans, and MRIs helps medical professionals gain insight into patients’ physical condition, predict when a disease will develop and when appropriate treatment will be needed.

Data labeling and annotation of medical images are helping reduce time spent on unnecessary diagnostic procedures and providing healthcare professionals with the means to make more accurate diagnoses and administer more effective treatments.

Over the next decade, machine learning will transform how medical images are used by reducing errors, improving outcomes, and lowering costs.

When done in the aggregate, using well-structured CV training data can help medical professionals predict the emergence of early diseases and develop treatment plans that help avoid serious medical conditions developing in specific patients.

One specific challenge to quality data annotation or labeling is the nuance of properly annotating 2-D and 3-D medical images that contain multiple layers and transparencies. For example, a chest X-ray or MRI requires viewing and analyzing multiple layers of internal organs. Done properly, data annotation combined with computer vision and machine learning can visually analyze interactive 3-D models to make more accurate medical diagnoses and provide earlier detection of specific diseases.

2) Healthcare Robotics and Devices

Robotics is rapidly transforming healthcare. In the operating room, robots support minimally invasive procedures. In clinical settings, fueled by the onset of COVID-19, the use of robotics gained wider traction by helping keep patients and medical professionals safer by performing tasks to help reduce exposure. For example, Intuitive Surgery’s Da Vinci robotic system is capable of performing a multitude of challenging surgical procedures remotely.

CV-enabled robots capture thousands of images in the operating room or medical offices. By annotating high-definition 3-D images and videos, AI-enabled robots can help surgeons improve techniques by guiding them to the specific area of the body that needs attention while avoiding damaging other nerves, muscles, and organs.

Surgical robots require the highest quality computer vision technology. It is essential that the annotations used to train models are spot on to ensure patient safety and positive post-op health outcomes. In some cases, with the right model and training data feeding it, it is even possible to develop robotic surgical techniques that assist in the decision-making processes.

3) Medical AI Research and Development

Developing a new procedure or delivering a new device or pharmaceutical product to market requires massive amounts of data to receive approval from regulatory bodies. Since over 90% of all medical data generated today are images, the right data labeling solution can get these products and treatments to market faster. In addition, companies researching biotechnology for drug discovery, understanding the impact of lifestyle choices and mental health on chronic conditions, and developing new mRNA vaccines as quickly as was done for COVID-19 benefit from annotating millions of computer-generated images.

All medical AI projects have unique requirements to train data analysts to perform quality work. For example, understanding the extra steps to ensure higher accuracy, such as zooming in on irregular MRI scan images, can make data labeling teams significantly more accurate and precise in tagging medical images.

Artificial intelligence (AI) will be an extremely important tool to support physicians, primary care doctors, and specialists as talent shortfalls increase dramatically by 2032.

Accelerated Medical Computer Vision with Turnkey Data Annotation

Whether you choose our CloudFactory Data Annotation Solution or our Computer Vision Managed Workforce option, we reduce complexity and give medical organizations a faster path to high-quality data labeling.

We bundle a professionally managed workforce with the option to use a best-in-class annotation platform for one inclusive price so you can:

• Scale fast with our trained, experienced, and managed teams of data analysts that can be ready to start your project in as little as one week.
• Remove the expense and hassle of directly managing an annotation workforce and a complicated tech stack.
• Streamline purchasing by integrating a data annotation platform and workforce into a single, monthly subscription service that quickly scales with your needs.
• Improve labeling quality by using experts trained on annotating pixel-level segmentation of multiple images using a tooling agnostic approach.
• Respond to changes in market conditions, product development, and business requirements.
• Have robust data security and business continuity policies in place while also addressing healthcare-specific security and data privacy concerns.
• Improve communications and build relationships with dedicated teams, messaging channels, and collaboration tools.
  
  

Which Service is Right for You?

If you need a highly trained, vetted, data labeling workforce to annotate your healthcare data, our Computer Vision Managed Workforce has you covered. If you also need an image annotation tool, we can bundle the workforce with an industry-leading labeling platform into one convenient contract and inclusive monthly price. With CloudFactory, you have options.

Who Does CloudFactory Work with?

CloudFactory has worked with several leading organizations to advance their use of medical AI through computer vision.

• Healthcare AI/ML Short Reads (blogs)
• Q&A with V7: COVID-19 AI Data Annotation for Clinical and Diagnostic Imaging (Q&A blog with short videos)
• 5 Qualities in Good Data Labeling Vendors (infographic)
• In-House vs Managed Workforce Data Labeling Partner (infographic)
• 3 Signs Data Labeling Provider Delivers Quality Data (infographic)
• Scaling Quality Training Data (white paper)
• The Outsourcers’ Guide to Quality (ebook)
• The Essential Guide to Computer Vision (ebook)
• Data Annotation Tools for Machine Learning (ebook)
• On-Demand Videos on Data Science and AI/ML (webinars)