Data annotation tools market are enabling sophisticated AI algorithms to precisely detect signs of diseases as well as medical issues without needing help from a specialized medical staff. Citing an instance, GE Healthcare in September 2019 developed an AI algorithm for identifying collapsed lungs through X-ray images; UC San Francisco provided the labeled medical data.

Rising demand from healthcare institutions for medical literature classification and medical image labeling is anticipated to drive data annotation tools market over the forthcoming time period.

The surging volumes of digital medical data and proliferation of wearable health devices have made it really tough for data scientists to label data manually. These annotation tools are helping healthcare organizations in addressing these problems by offering accurate and top-quality labeled medical information with least assistance from medical experts.

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The segment of text annotation tools is anticipated to witness significant growth rate of over 35% CAGR over 2020 and 2026. The immense growth rate can be attributed to textual data labeling becoming increasingly crucial for performing sentiment analysis as well as developing document classification algorithms.

Textual type data annotation tools market is also picking up growth momentum and becoming crucial for text mining from social media platforms as national security authorities as well as federal agencies observe the behavior of their citizens for possible terroristic threats.

Leveraging deep learning neural networks that are integrated with textual annotation solutions, security agencies could monitor possible objectional material as well as threat topics which is posted in social media profiles.

Citing an example, the Defense Institute of Advanced Technology under the Ministry of Defense, India, sponsored the R&D that went into the deployment of text labeling tools for social network analysis of suspicious terrorist groups back in December of 2019.

The manual date annotation segment dominated the overall market, accounting for over 75% of the data annotation tools market share back in 2019 owing to the higher quality of labeled data in comparison to automated data labeling. Manual data labeling activities are conducted by highly skilled professionals having strong expertise on the content and nature of data.

Manually labeled data annotation is gaining traction because of creating AI algorithms which are utilized in crucial applications due of its precision and quality.

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The data annotation tools market in Europe is likely to witness growth of over 30% CAGR between 2020 and 2026 owing to the widespread adoption of AI technologies by European business corporations. The region has a well-developed AI market, with various countries like the Netherlands, Germany, and Nordics being the regional leaders in the execution of machine learning applications.

Increasing demand for annotated training datasets to enable machine learning algorithms throughout various applications like big data analytics, robotics, and cognitive systems are also likely to drive the growth of the data annotation tools market in the region.

Samasource Inc., Appen Limited, Neurala, Inc., MonkeyLearn Inc., Amazon Web Services, Inc., CloudFactory Limited, LionBridge AI, IBM Corporation, Scale, Inc., CloudFactory Limited, Clickworker GmbH, Google LLC, Trilldata Technologies Pvt. Ltd., Hive, Labelbox, Inc., Mighty AI, Webtunix AI, CloudApp, Inc., iMerit, Cogito, Alegion, Inc., and Playment Inc. among others are some of the major industry players operating in the data annotation tools market.

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