Healthcare Data Collection And Labeling Market (2022 - 2030) Size, Share & Trends Analysis Report By Data Type (Image/Video, Audio, Text), By Region (North America, Europe, APAC, LATAM, MEA), And Segment Forecasts

Report Overview

The global healthcare data collection and labeling market size was valued at USD 526.6 million in 2021 and is expected to expand at a compound annual growth rate (CAGR) of 26.9% from 2022 to 2030. The healthcare industry witnessed the penetration of artificial intelligence and machine learning during the COVID-19 pandemic. The data collection is anticipated to witness growth due to the adoption of technology and medical imaging techniques for the early and accurate diagnosis of diseases. Various market players are undertaking strategic initiatives to build a robust artificial intelligence network by outsourcing data collection and labeling services. For instance, Centaur labs provide medical labeling solutions such as medical audio labeling, medical image labeling, medical text labeling, radiology labeling, ECG labeling, and labeling for cardiology.

According to the WHO, there were about 247 million confirmed cases of COVID-19 worldwide as of November 2021 with over 5 million deaths. Although RT-PCR testing is still widely used to diagnose COVID-19, there was a shortage of testing kits, and hence reliability of test results was a challenge in many countries. Medical imaging was used in many developed countries to detect the symptoms of COVID-19. Imaging techniques proved to be a powerful tool to minimize the risk of the spread of the virus. In recent years, medical imaging techniques have seen rapid progress due to artificial intelligence, machine learning, and deep learning. Data collection and labeling are used for training these AI algorithms.

Data collection is the process of systematically evaluating, measuring, and acquiring information to respond to hypotheses, study questions, and evaluate outcomes. Artificial intelligence-based solutions can be trained to recognize marked and labeled data. Medical images, X-rays, CT scan images, and magnetic resonance imaging are common sources of information. Video, text, audio, and image formats are all used to collect data. These are mostly used in the healthcare industry and are expected to play a significant part in medical imaging, which uses computer vision technology for early diagnosis, minimizing risk, and discovering trends.

AI systems have advanced in image-recognition tasks, which are relevant to disease diagnosis, detection of various disease patterns, and interpreting and analyzing the vast amount of unstructured data. As medical imaging uses computer vision technology to sense patterns and detect disease or injury, data collecting, and labeling play a key role in the healthcare industry. Data labeling contributes to the training of artificial intelligence systems in extracting information collected from medical pictures, such as MRI, X-ray, and CT scan images.

Artificial intelligence is widely employed in the healthcare sector for a variety of applications, such as early disease detection, identifying emerging risks, initiating drug discovery, enhancement of social distancing measures, and offering alternative methods to assist healthcare professionals. It also assists medical professionals in the automatic creation of reports of patients. As extremely precise data labeling is required for training artificial intelligence algorithms, the market for healthcare data collection and labeling will witness positive growth over the forecast period.

Data Type Insights

In 2021, the image/video segment held the largest revenue share of over 40.0% owing to the increased implementation of artificial intelligence algorithms in the healthcare industry. Medical image labeling uses semantic segmentation and polygon image annotation for organ segmentation and disease diagnosis. It is a helpful tool used to detect various rare diseases. Due to its accuracy and early diagnosis, medical imaging was widely used in data labeling in the healthcare industry during the COVID-19 pandemic.

The text data type segment is expected to expand at the fastest CAGR of 29.1% from 2022 to 2030. The collection of clinical data, particularly unstructured text documents, has become one of the most significant resources for clinical labeling. Text labeling is crucial to train NLP algorithms such as speech recognition, sentiment analysis, and chatbots. This is eventually contributing to the segment growth.

Regional Insights

North America dominated the market in 2021 with a revenue share of over 45.0% owing to the increased adoption of AI-based solutions in healthcare during the initial phase of the COVID-19 pandemic. The healthcare services in the region are moving towards medical imaging for accurate and early diagnosis as this also generates automated reports for individual patients. Data labeling is used to train AI systems for different medical images.

Asia Pacific is expected to expand at the fastest CAGR over the forecasted period. This growth is attributed to the increased use of medical imaging in the healthcare industry in developing countries, such as China and India. Various initiatives are taken by the governments to increase the adoption of AI in healthcare in the coming years. Growth in the implementation of face recognition surveillance systems in China is expected to contribute to the market growth. Additional factors such as rapid technological advancements, growth in smartphone and tablet users, and the rising popularity of social networking sites are major contributors to healthcare data.

Key Companies & Market Share Insights

The key market players are focusing on expanding their customer base to acquire a competitive advantage in the market. The companies operating in the market are undertaking several strategic activities such as collaborations, acquisitions, mergers, and partnerships with other industry leaders. For instance, in September 2021, Centaur labs raised funding of USD 15 million. The investors were Matrix Partners, Susa Ventures, Y Combinator, and Global Founders Capital.

In August 2021, Snorkel AI raised USD 85 million at a valuation of USD 1 billion to create an AI training database automatically and develop trained AI data companies that spend months doing it manually, which decreases the AI development process. Snorkel AI is developing an automatic mechanism that will reduce the time consumed and will be more accurate and reliable. In November 2020, Alegion, an Austin-based company that provides data labeling solutions, announced the launch of Alegion Control, a self-service software solution that would optimize data annotation by offering direct access to its data labeling platform. It provides high-resolution video annotation and model-ready data to train the Machine Learning models. It provides both platform and workforce to train the structured and unstructured data into video, images, audio, and text. Some prominent players in the global healthcare data collection and labeling market include:

• Alegion

• Labelbox, Inc.

• iMerit

• Cogito Tech LLC

• Appen Limited

• Shaip

• Snorkel AI

• Infloks

• Datalabeller

• Centaur labs

Healthcare Data Collection And Labeling Market Report Scope

Segments Covered in the Report 

This report forecasts revenue growth at the global, regional, and country levels and provides an analysis of the latest industry trends and opportunities in each of the sub-segments from 2017 to 2030. For this study, Grand View Research has segmented the global healthcare data collection and labeling market report based on data type and region:

• Data Type Outlook (Revenue, USD Million, 2017 - 2030)

  • Image/Video

  • Audio

  • Text

  • Others

• Regional Outlook (Revenue, USD Million, 2017 - 2030)

  • North America

    • U.S.

    • Canada

  • Europe

    • U.K.

    • Germany

    • France

    • Italy

    • Spain

  • Asia Pacific

    • Japan

    • China

    • India

  • Latin America

    • Brazil

    • Mexico

  • Middle East & Africa

    • South Africa