医療データ収集・ラベリング市場レポート：動向、予測、競合分析 (2031年まで)

世界の医療データ収集・ラベリング市場の将来は、病院・クリニック・その他の市場における機会で有望視されています。世界の医療データ収集・ラベリング市場は、2025年から2031年までのCAGRが24.1%で、2031年までに推定33億米ドルに達すると予測されます。この市場の主な促進要因は、医療産業の成長、医療におけるAIとMLの採用の増加、個別化された遠隔患者モニタリングへの嗜好の高まりです。

• Lucintelの予測によると、医療産業における人工知能アルゴリズムの導入が拡大していることから、データタイプ別では画像/動画が予測期間中に最も高い成長を遂げる見込みです。
• 最終用途別では、病院が最大のセグメントであり続けると思われます。
• 地域別では北米が、確立された医療システム、洗練された医療施設の存在、医療におけるAIと機械学習の採用により、予測期間中最大の地域であり続けます。

医療データ収集・ラベリング市場の戦略的成長機会

技術開発の進展、データの適切な利用、進化する健康問題により、医療データ収集・ラベリング市場における新たな戦略的成長機会があらゆる分野で生まれています。これらの機会は、関連する利害関係者が分割して活用することで、各業界における地位を向上させ、その性質を変えることができます。

• AI包括的データラベリングの開発：AI包括的データラベリングの開発は、データ注釈プロセスの自動化や品質向上の機会を提供します。AIアルゴリズムにより、ラベリングのプロセス全体を高速化し、大量のデータをより効果的に管理することができます。
• ビッグデータ分析ツールの応用：ビッグデータ解析ツールの構築は、利用可能な健康データから価値ある情報を抽出する機会も提供します。予測分析や動向探索の分析ツールが利用可能です。
• 遠隔モニタリング技術の強化：ワイヤレス医療機器や遠隔モニタリング用ハンドヘルド機器は、年間を通じて患者をモニタリングしながら、データ収集のためのリアルタイムなやり取りを可能にします。これらの技術は医療の提供を促進し、医療提供者の関心を高めます。
• 意義のある利用を通じた、プロバイダー組織による医療情報技術の統合：ICTシステムが閉鎖的であると、医療クライアントとの関わりは難しくなります。そのため、多くの機関が、多様な医療部門を管理するクロスサイトEHRシステムの導入を急いでいます。これは、患者の健康に関連するため、特に必要なことです。
• データプライバシーとセキュリティソリューションへの注目：データプライバシーとセキュリティソリューションに関心が移っており、堅牢なデータセキュリティシステムの開発と法的規定の遵守に繋がっています。機密情報を保護する取り組みは、患者の信頼を維持するために必要です。

医療データ収集・ラベリング市場の成長の原動力には、AIベースデータラベリング・処理、ビッグデータ分析、遠隔モニタリングシステム、統合EHRシステム、データシステム内のプライバシーソリューションなどがあります。このような機会を活用することで、データ管理、質の高いケア提供、医療サービス提供の効率化が促進されます。

医療データ収集・ラベリング市場の促進要因・課題

医療データ収集・ラベリング市場は、それを支える要因が成長・開拓の促進要因・課題となるため、非常に重要です。多くの場合、科学技術や臨床技術、社会、臨床経済、法的側面が市場を牽引するため、これらを考慮する必要があります。利害関係者は、市場に参入するためにこれらの要因を理解する必要があります。

医療データ収集・ラベリング市場を牽引する要因には、以下のようなものがあります：

• 技術の進歩：AI、ビッグデータ、遠隔モニタリング技術の目覚ましい成長により、医療データの収集・ラベリングが加速しています。これらの技術は、収集したデータの処理効率と正確性を保証します。
• 正確なデータへの需要の高まり：臨床業務、研究、エビデンスに基づく患者ケアにおける意思決定を支援するため、より正確で精密、かつ信頼性の高い医療データが求められています。これは医療提供の重要な側面です。
• デジタル医療技術の拡大：EHRやモバイルヘルスアプリケーションのようなデジタル医療技術の利用拡大に伴い、効率的なデータ収集・ラベリングも必要とされています。これらの技術が適切に機能するためには、十分なデータが必要です。
• 規制コンプライアンス要件：特にGDPRとHIPAAに関する法的な問題により、安全で許容可能なデータ取り扱い方法の必要性が高まっています。規制は、データ収集・ラベリングのプロセスを変更する重要な原動力となっています。
• 患者中心のケアへの注目の高まり：包括的でエラーのない、信頼性の高い有用な患者データへの需要は、患者中心のケアへの注目の高まりにより高まっています。患者中心のモデルは、治療と管理を促進するための十分なデータによって支えられるべきです。

医療データ収集・ラベリング市場における課題は以下の通りです：

• データのプライバシーとセキュリティへの懸念：データのプライバシーとセキュリティに関する懸念が、医療データ収集・ラベリング市場の成長を妨げています。患者の信頼を維持するためには、機密性の高い医療情報を保護しなければなりません。
• データ統合の複雑さ：さまざまなシステムやインターフェースからのデータ統合は複雑で、課題となっています。これには、複数のソースからのデータを確実に調和させ、一貫して使用できるようにすることが含まれます。
• 新技術の高コスト：市場で入手可能な高度なデータ収集およびラベリング技術はコストが高く、多くのユーザー、特に予算が限られている小規模組織の意欲を削いでいます。

医療データ収集・ラベリング市場は、正確なデータに対する需要の高まり、技術の進歩、デジタルヘルスの拡大、法令順守、患者志向のケアへの注力といった要因の影響を受けています。一方、課題としては、プライバシーとセキュリティの問題、データ統合の複雑さ、技術コストの高さなどが挙げられます。利害関係者はこれらの市場促進要因・課題に対処することで、市場における戦略的な位置付けを確立し、データ管理戦略を改善し、医療サービスの進歩を実現する必要があります。

目次

第1章 エグゼクティブサマリー

第2章 世界の医療データ収集・ラベリング市場：市場力学

• イントロダクション、背景、分類
• サプライチェーン
• 業界の促進要因と課題

第3章 市場動向と予測分析 (2019年～2031年)

• マクロ経済動向 (2019～2024年) と予測 (2025～2031年)
• 世界の医療データ収集・ラベリング市場の動向 (2019～2024年) と予測 (2025～2031年)
• 世界の医療データ収集・ラベリング市場：データの種類別
  • 画像/動画
  • 音声
  • 文章
• 世界の医療データ収集・ラベリング市場：最終用途別
  • 病院
  • クリニック
  • その他

第4章 地域別の市場動向と予測分析 (2019年～2031年)

• 世界の医療データ収集・ラベリング市場：地域別
• 北米の医療データ収集・ラベリング市場
• 欧州の医療データ収集・ラベリング市場
• アジア太平洋の医療データ収集・ラベリング市場
• その他地域の医療データ収集・ラベリング市場

第5章 競合分析

• 製品ポートフォリオ分析
• 運用統合
• ポーターのファイブフォース分析

第6章 成長機会と戦略分析

• 成長機会分析
  • 世界の医療データ収集・ラベリング市場の成長機会：データの種類別
  • 世界の医療データ収集・ラベリング市場の成長機会：最終用途別
  • 世界の医療データ収集・ラベリング市場の成長機会：地域別
• 世界の医療データ収集・ラベリング市場の新たな動向
• 戦略的分析
  • 新製品の開発
  • 世界の医療データ収集・ラベリング市場の生産能力拡大
  • 世界の医療データ収集・ラベリング市場における企業合併・買収 (M&A)、合弁事業
  • 認証とライセンシング

第7章 主要企業のプロファイル

• Alegion
• Labelbox
• iMerit
• Cogito Tech
• Appen
• Shaip
• Snorkel AI
• Infloks
• Datalabeller
• Centaur Labs

The future of the global healthcare data collection and labeling market looks promising with opportunities in hospitals, clinics, and others markets. The global healthcare data collection and labeling market is expected to reach an estimated $3.3 billion by 2031 with a CAGR of 24.1% from 2025 to 2031. The major drivers for this market are growth in the healthcare industry, increasing adoption of AI and ML in healthcare, and rising preference towards personalized and remote patient monitoring.

• Lucintel forecasts that, within the data type category, image/video is expected to witness the highest growth over the forecast period due to the growing implementation of artificial intelligence algorithms in the healthcare industry.
• Within the end use category, hospitals will remain the largest segment.
• In terms of regions, North America will remain the largest region during the forecast period due to the presence of well-established healthcare systems, sophisticated medical facilities, and the adoption of AI and machine learning in healthcare.

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Emerging Trends in the Healthcare Data Collection and Labeling Market

This is the sentence defining the research area and objectives/focus of the paper. The market for healthcare data collection and labeling is becoming dynamic due to developments in technology, policy transformations, and the increased demand for accurate health data. Other emerging areas are changing the phenomenon of healthcare data management and the methods of data collection, processing, and usage. Understanding these trends is important, especially for market players, to keep up with the market and exploit opportunities.

• Inclusion of Artificial Intelligence in Processes: More and more healthcare data collection and labeling activities are incorporating AI technologies. AI algorithms are used to expedite data annotation, improve precision, and enhance analytics.
• Growing Interest in Big Data Analysis: There has been increased adoption of big data analytics in healthcare to gain insights from large quantities of data. Novel analytics help with trend spotting, outcome prediction, and facilitating stratum-specific therapies.
• Focus on Data Privacy and Security: Given the rise in data breaches and privacy intrusions, the emphasis on data privacy and security cannot be overstated. Enforcement is required for practices such as GDPR and HIPAA, which necessitate secure handling of data.
• Adoption of Standardized Data Formats: Increasingly, there is a shift toward the use of standardized data formats and procedures for sharing electronic health records. This makes it easier to share and integrate data flowing between different health organizations and systems.
• Development of Remote Data Collection Technologies: Such technologies are becoming prevalent in the form of wearable devices or mobile health applications, especially in multidisciplinary health research. They enable seamless and frequent communication between researchers and subjects.

Some trends resulting in changes to the healthcare data collection and labeling industry include, but are not limited to: the integration of AI in services, analytics of big data, privacy and security of data, uniformity in data structure, and remote data collection technologies. These trends improve performance by increasing the accuracy of data and enhancing people's experience in the healthcare industry.

Recent Developments in the Healthcare Data Collection and Labeling Market

Over the years, the healthcare data collection and labeling market has seen several changes in technology, legislation, and the interest in data accuracy and efficiency. These changes are setting the direction for healthcare data management, affecting how data is collected, processed, and utilized.

• AI-Driven Data Labeling Solutions: Data management is undergoing a revolution with the deployment of AI-powered data labeling solutions. By integrating AI strategies into algorithms, the labeling of data in analytic processes is automated, enhancing efficiency in speed and accuracy.
• Enhanced Data Privacy Regulations: New and updated data privacy regulations, such as GDPR and HIPAA, are reshaping data collection and data branding activities. This emphasizes the need for safety and legality in the investments made by these regulatory bodies.
• Growth of Big Data Analytics Platforms: The growth of platforms such as big data analytics is enabling healthcare organizations to manage vast databases comfortably. These insights have proven crucial for decision-making and research purposes.
• Adoption of Remote Monitoring Technologies: The adoption of remote monitoring technologies, which include wearables and mobile health applications, is increasing the coverage of data collection. These technologies assist in monitoring health in real-time.

Some recent breakout trends and events taking place in the healthcare data collection and labeling market include: AI services, increased privacy regulations, big data analytics services/infrastructure, EHR interoperability standards, and remote monitoring technologies. These trends foster creativity and improve data management. They will redefine the healthcare data collection and labeling process in the future, enhancing healthcare services.

Strategic Growth Opportunities for Healthcare Data Collection and Labeling Market

Due to increasing technological developments, proper use of data, and evolving health issues, new strategic growth opportunities in the healthcare data collection and labeling market are emerging across all sectors. These opportunities, when divided and capitalized on by relevant stakeholders, can enhance positions in respective industries and transform their nature.

• Development of AI-Inclusive Data Labeling: The development of AI-inclusive data labeling presents opportunities to automate or enhance the quality of data annotation processes. With AI algorithms, the entire process of labeling can be made faster and more effective in managing large amounts of data.
• Applications of Big Data Analytics Tools: The construction of big data analytics tools also provides opportunities to extract valuable information from available health data. Predictive analytics and trend-searching analytics tools are available.
• Enhancement of Remote Monitoring Technologies: Wireless medical devices and remote monitoring handheld devices allow real-time interactions for data collection while monitoring patients year-round. These technologies promote healthcare delivery and create interest among health providers.
• Integration of Health Information Technology by Provider Organizations through Meaningful Use: Engaging with healthcare clients becomes difficult when their ICT systems are closed. That is why many agencies are moving quickly to implement cross-site EHR systems that control diverse medical sectors. This is particularly necessary as it relates to patient health.
• Attention Shifting to Data Privacy and Security Solutions: Attention is shifting toward data privacy and security solutions, which are leading to the development of robust data security systems and adherence to legal provisions. Efforts to secure sensitive information are necessary to maintain patient trust.

An impetus for growth in the healthcare data collection and labeling market includes AI-based data labeling and processing, big data analytics, remote monitoring systems, integrated EHR systems, and privacy solutions within data systems. Taking advantage of such opportunities facilitates growth in data management, quality care delivery, and efficiency in healthcare service provision.

Healthcare Data Collection and Labeling Market Driver and Challenges

The healthcare data collection and labeling market is critical because the factors that support it are the drivers or challenges to its growth and development. In most cases, scientific and clinical technology, social, clinical economies, and legal aspects must be considered, as they drive the market. Stakeholders need to understand these factors to tap into the market proficiently.

The factors responsible for driving the healthcare data collection and labeling market include:

• Technological Advancements: There has been spectacular growth in AI, big data, and telemonitoring technologies, accelerating the collection and labeling of healthcare data. These technologies ensure efficiency in processing and accuracy of the data collected.
• Increasing Demand for Accurate Data: More accurate, precise, and trustworthy healthcare data is required to aid decision-making in clinical work, research, and evidence-based patient care. This is a critical aspect of healthcare provision.
• Expansion of Digital Health Technologies: With the increased usage of digital health technologies such as EHRs and mobile health applications, there is also a corresponding need for efficient data collection and labeling. These technologies require sufficient data to function properly.
• Regulatory Compliance Requirements: There is a growing need for secure and acceptable methods of data handling due to legalities, especially concerning GDPR and HIPAA. Regulations are key drivers in altering data collection and labeling processes.
• Rising Focus on Patient-Centric Care: The demand for comprehensive, error-free, reliable, and useful patient data is growing due to the increasing focus on patient-centric care. Patient-centric models should be supported by sufficient data to facilitate treatment and management.

Challenges in the healthcare data collection and labeling market include:

• Data Privacy and Security Concerns: Data privacy and security concerns hinder the growth of the healthcare data collection and labeling market. Sensitive health information must be protected to maintain patient trust.
• Data Integration Complexity: Data integration from various systems and interfaces is complex and presents a challenge. This involves ensuring that data from multiple sources is harmonized and can be used consistently.
• High Costs of New Technologies: Advanced data collection and labeling technologies available in the market are costly, which discourages many users, especially small organizations with limited budgets.

The market for healthcare data aggregation and tagging services is influenced by factors such as growing demand for precise data, technological advancements, expansion of digital health, legal compliance, and a focus on patient-oriented care. On the flip side, challenges include privacy and security issues, data integration complexity, and the high cost of technology. Stakeholders need to address these drivers and challenges to strategically position themselves in the market, improve data management strategies, and enable progress in healthcare services.

List of Healthcare Data Collection and Labeling Companies

Companies in the market compete on the basis of product quality offered. Major players in this market focus on expanding their manufacturing facilities, R&D investments, infrastructural development, and leverage integration opportunities across the value chain. With these strategies healthcare data collection and labeling companies cater increasing demand, ensure competitive effectiveness, develop innovative products & technologies, reduce production costs, and expand their customer base. Some of the healthcare data collection and labeling companies profiled in this report include-

• Alegion
• Labelbox
• Imerit
• Cogito Tech
• Appen
• Shaip
• Snorkel AI
• Infloks
• Datalabeller
• Centaur Labs

Healthcare Data Collection and Labeling by Segment

The study includes a forecast for the global healthcare data collection and labeling market by data type, end use, and region.

Healthcare Data Collection and Labeling Market by Data Type [Analysis by Value from 2019 to 2031]:

• Image/Video
• Audio
• Text

Healthcare Data Collection and Labeling Market by End Use [Analysis by Value from 2019 to 2031]:

• Hospitals
• Clinics
• Others

Healthcare Data Collection and Labeling Market by Region [Analysis by Value from 2019 to 2031]:

• North America
• Europe
• Asia Pacific
• The Rest of the World

Country Wise Outlook for the Healthcare Data Collection and Labeling Market

There have been many improvements in the healthcare data collection and labeling market as healthcare organizations and technology providers continue to seek ways to enhance the accuracy of the data obtained, the patient experience, and fulfill legal obligations. Recent trends in data integration, AI, and improvements to the regulatory environment have changed how healthcare data is collected, managed, and annotated. These changes provide the impetus to seek newer forms of health information that are more effective, precise, and actionable for the healthcare sector, ultimately enhancing the quality of care for patients.

• United States: In the United States, progress has been made in using AI and machine learning in the healthcare data labeling process. Advanced algorithms are now applied to automatically annotate data, cutting down the time required for data annotation and improving quality. There is also constant advocacy for greater standardization of data formats and increased interoperability to improve data sharing between healthcare providers. Other recent regulations, such as the 21st Century Cures Act, have also influenced how data is collected, actively pushing for the availability of patients' health data and the adoption of electronic health records (EHRs).
• China: With increased expenditure on digital health technologies and AI, the Chinese healthcare data collection and annotation market is witnessing rapid growth. The national government is formulating policies aimed at improving infrastructure and access to healthcare data, as outlined in the Healthy China 2030 plan. There is also an emphasis on creating sophisticated data platforms for big data analytics and enhancing data labeling capabilities for precision and personalized medicine. Companies are also using AI to automate the management, classification, and analysis of medical records and imaging data.
• Germany: Emerging trends in Germany in healthcare data collection and labeling include changes in data privacy laws and integration with digital health technologies. New policies, such as the Digital Healthcare Act (DVG), are advancing digital health apps and EHRs, making the data collection and labeling process more user-friendly. Efforts are focused on avoiding breaches of GDPR while utilizing new technologies to enhance data processing and analytics capabilities. Technologies that improve data accuracy and facilitate data tagging processes are gaining attention from German firms.
• India: The market for healthcare data collection and labeling in India is on the rise, driven by the increasing use of electronic solutions like EHRs. Recent developments include low-cost data visualization and the incorporation of the National Digital Health Mission, which aims for a holistic approach to digital health integration. Improved methods of data collection, including the integration of AI to enhance data labeling, are becoming the focus of companies. The goal is to provide affordable, scalable services that meet the needs of India's healthcare system, including its underserved rural and remote areas.
• Japan: In Japan, new initiatives are further enhancing efforts in healthcare data collection and labeling. The integration of novel AI and machine learning techniques is improving data acquisition and processing. The government is advocating for a shift to digital health record systems, supported by agencies such as the Japan Agency for Medical Research and Development. Additionally, there is an increasing focus on data integration from disparate systems to enhance patient well-being and treatment efficacy. Japanese companies are also working on data labeling solutions that improve interoperability and facilitate the data acquisition process.

Features of the Global Healthcare Data Collection and Labeling Market

Market Size Estimates: Healthcare data collection and labeling market size estimation in terms of value ($B).

Trend and Forecast Analysis: Market trends (2019 to 2024) and forecast (2025 to 2031) by various segments and regions.

Segmentation Analysis: Healthcare data collection and labeling market size by data type, end use, and region in terms of value ($B).

Regional Analysis: Healthcare data collection and labeling market breakdown by North America, Europe, Asia Pacific, and Rest of the World.

Growth Opportunities: Analysis of growth opportunities in different data types, end uses, and regions for the healthcare data collection and labeling market.

Strategic Analysis: This includes M&A, new product development, and competitive landscape of the healthcare data collection and labeling market.

Analysis of competitive intensity of the industry based on Porter's Five Forces model.

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This report answers following 11 key questions:

• Q.1. What are some of the most promising, high-growth opportunities for the healthcare data collection and labeling market by data type (image/video, audio, and text), end use (hospitals, clinics, and others), and region (North America, Europe, Asia Pacific, and the Rest of the World)?
• Q.2. Which segments will grow at a faster pace and why?
• Q.3. Which region will grow at a faster pace and why?
• Q.4. What are the key factors affecting market dynamics? What are the key challenges and business risks in this market?
• Q.5. What are the business risks and competitive threats in this market?
• Q.6. What are the emerging trends in this market and the reasons behind them?
• Q.7. What are some of the changing demands of customers in the market?
• Q.8. What are the new developments in the market? Which companies are leading these developments?
• Q.9. Who are the major players in this market? What strategic initiatives are key players pursuing for business growth?
• Q.10. What are some of the competing products in this market and how big of a threat do they pose for loss of market share by material or product substitution?
• Q.11. What M&A activity has occurred in the last 5 years and what has its impact been on the industry?

Table of Contents

1. Executive Summary

2. Global Healthcare Data Collection and Labeling Market : Market Dynamics

• 2.1: Introduction, Background, and Classifications
• 2.2: Supply Chain
• 2.3: Industry Drivers and Challenges

3. Market Trends and Forecast Analysis from 2019 to 2031

• 3.1. Macroeconomic Trends (2019-2024) and Forecast (2025-2031)
• 3.2. Global Healthcare Data Collection and Labeling Market Trends (2019-2024) and Forecast (2025-2031)
• 3.3: Global Healthcare Data Collection and Labeling Market by Data Type
  • 3.3.1: Image/Video
  • 3.3.2: Audio
  • 3.3.3: Text
• 3.4: Global Healthcare Data Collection and Labeling Market by End Use
  • 3.4.1: Hospitals
  • 3.4.2: Clinics
  • 3.4.3: Others

4. Market Trends and Forecast Analysis by Region from 2019 to 2031

• 4.1: Global Healthcare Data Collection and Labeling Market by Region
• 4.2: North American Healthcare Data Collection and Labeling Market
  • 4.2.1: North American Market by Data Type: Image/Video, Audio, and Text
  • 4.2.2: North American Market by End Use: Hospitals, Clinics, and Others
• 4.3: European Healthcare Data Collection and Labeling Market
  • 4.3.1: European Market by Data Type: Image/Video, Audio, and Text
  • 4.3.2: European Market by End Use: Hospitals, Clinics, and Others
• 4.4: APAC Healthcare Data Collection and Labeling Market
  • 4.4.1: APAC Market by Data Type: Image/Video, Audio, and Text
  • 4.4.2: APAC Market by End Use: Hospitals, Clinics, and Others
• 4.5: ROW Healthcare Data Collection and Labeling Market
  • 4.5.1: ROW Market by Data Type: Image/Video, Audio, and Text
  • 4.5.2: ROW Market by End Use: Hospitals, Clinics, and Others

5. Competitor Analysis

• 5.1: Product Portfolio Analysis
• 5.2: Operational Integration
• 5.3: Porter's Five Forces Analysis

6. Growth Opportunities and Strategic Analysis

• 6.1: Growth Opportunity Analysis
  • 6.1.1: Growth Opportunities for the Global Healthcare Data Collection and Labeling Market by Data Type
  • 6.1.2: Growth Opportunities for the Global Healthcare Data Collection and Labeling Market by End Use
  • 6.1.3: Growth Opportunities for the Global Healthcare Data Collection and Labeling Market by Region
• 6.2: Emerging Trends in the Global Healthcare Data Collection and Labeling Market
• 6.3: Strategic Analysis
  • 6.3.1: New Product Development
  • 6.3.2: Capacity Expansion of the Global Healthcare Data Collection and Labeling Market
  • 6.3.3: Mergers, Acquisitions, and Joint Ventures in the Global Healthcare Data Collection and Labeling Market
  • 6.3.4: Certification and Licensing

7. Company Profiles of Leading Players

• 7.1: Alegion
• 7.2: Labelbox
• 7.3: iMerit
• 7.4: Cogito Tech
• 7.5: Appen
• 7.6: Shaip
• 7.7: Snorkel AI
• 7.8: Infloks
• 7.9: Datalabeller
• 7.10: Centaur Labs