AI Training Dataset In Healthcare Market Size, Share & Trends Analysis Report By Model (Text, Image/Video), By Dataset Type (Electronic Health Records, Medical Imaging), By Region, And Segment Forecasts, 2023 - 2030

Market Size & Trends

The global AI training dataset in healthcare market size was estimated at USD 275.8 million in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 23.1% from 2023 to 2030. There is a rising need for training datasets that enable explainable AI as the application of artificial intelligence (AI) in healthcare continues to expand. Explainable AI (XAI) datasets give detailed explanations for AI model predictions, assisting medical practitioners and patients in understanding why a specific diagnostic or treatment suggestion was made. This tendency encourages openness and confidence in AI healthcare systems, both critical for wider adoption.

Owing to the heightened importance of data privacy and security in healthcare AI training datasets, one of the prominent trends involves the meticulous de-identification and anonymization of patient data. It safeguards individuals' privacy and aligns with stringent regulations such as HIPAA and GDPR. The process typically involves eliminating or encrypting personally identifiable information (PII) while ensuring that AI models are trained on data that cannot be associated with specific patients. Due to these efforts, the healthcare industry aims to instill trust in AI-driven applications, fostering wider acceptance while upholding patient confidentiality as a top priority in healthcare AI.

Due to the rising demand for comprehensive treatment solutions for rare diseases, there is a growing need for datasets that specifically focus on rare diseases and the development of drugs for orphan conditions. These datasets contain a wide array of information, including genomic data, clinical trial outcomes, and patient records associated with uncommon medical conditions. AI models trained on these datasets play a pivotal role in aiding researchers to uncover potential treatments for rare diseases, identify opportunities for repurposing existing drugs, and enhance the recruitment of patients for clinical trials. This trend is driven by the increasing demand for advancements in the understanding and treatment of rare diseases, underscoring the significance of datasets dedicated to this critical area of medical research.

Leading global tech companies are focusing on leveraging artificial intelligence and machine learning technologies to expedite their digital transformation efforts and boost operational efficiency. For instance, in October 2020, NVIDIA partnered with global healthcare firm GSK and its AI division, dedicated to enhancing the drug and vaccine discovery process through computational methods. GSK has established an innovative AI hub in London, utilizing its substantial genetic and genomic data to streamline the creation of groundbreaking medicines and vaccines.

The challenge involves dealing with immense datasets used in drug discovery, necessitating advanced hardware and novel machine-learning software. GSK, in collaboration with NVIDIA, is addressing this issue by pooling expertise at the intersection of medicine, genetics, and artificial intelligence within the UK's thriving ecosystem. NVIDIA's role in this partnership involves leveraging its proficiency in GPU optimization and high-performance computational pipeline development, including its specialized computational drug discovery applications and frameworks known as NVIDIA Clara Discovery.

Model Insights

The image/video segment dominated the market with a revenue share of 41.3% in 2022. AI training datasets increasingly include a variety of imaging modalities, such as merging MRI, CT scans, and ultrasound. These datasets allow for the creation of AI models that can give complete diagnostic insights by combining data from several imaging sources. This method is especially useful when a comprehensive picture is required for informed decision-making in difficult medical conditions. The tendency favors the development of AI models capable of analyzing and fusing data from numerous imaging modalities.

The text segment is estimated to register the highest CAGR over the forecast period. The rising demand for Electronic health records (EHRs) and clinical note data sets are in increasing demand. They include various textual patient information, including medical notes, diagnostic reports, and patient histories. This trend addresses the increased demand for NLP models that extract significant insights from unstructured medical text, enabling applications such as automated medical coding, clinical decision support, and medical research. These datasets are critical for training AI models to efficiently understand and organize massive volumes of healthcare data.

Dataset Type Insights

Based on dataset type, the medical imaging segment dominated the market with a revenue share of 29.5% in 2022. The emergence of 3D and 4D medical imaging technologies has given rise to datasets that accommodate these data modalities. These datasets contain volumetric and time-dependent medical images, such as 3D CT scans and 4D MRI sequences. AI models trained on such datasets can offer more detailed and precise diagnostics in radiology and cardiology. These datasets are pivotal for advancing the accuracy and reliability of AI-driven medical imaging analysis, where the additional dimensions provide a deeper understanding of anatomical structures and physiological processes.

One notable development is the production of datasets, including data from various sensors in wearable devices. Data from sensors such as heart rate monitors, accelerometers, and temperature sensors are included in these databases. The movement intends to allow AI models to analyze data from several sensors simultaneously, delivering a more comprehensive and nuanced picture of a patient's health. This multi-sensor integration improves the overall accuracy of AI-driven healthcare systems by enabling applications such as fall detection, activity monitoring, and health status tracking.

Regional Insights

The North America segment dominated the market with a revenue share of 35.8% in 2022. Personalized medicine is a prominent trend in North America, and AI training datasets are adapting to include genomic data. These datasets contain information on an individual's genetic makeup and may be linked to their clinical data. The trend enables the development of AI models that can offer personalized treatment recommendations and predict disease susceptibility based on genetics. The region's emphasis on precision medicine and genomics research underscores the significance of datasets that integrate clinical and genomic data for more tailored healthcare solutions.

The APAC is estimated to register the highest CAGR over the forecast period. The APAC region places significant importance on traditional medicine systems such as Ayurveda, Traditional Chinese Medicine, and Kampo. Datasets now combine information from traditional medical practices with modern medical data. The trend enables AI models to provide holistic healthcare solutions that combine the best of traditional and modern medicine, addressing the unique healthcare landscape of the region.

Key Companies & Market Share Insights

The industry is characterized by intense rivalry, with a specific set of worldwide leaders controlling a substantial portion of the market. The primary focus is on leading innovations in product development and promoting collaborations among major players in the industry. For instance, In January 2023,BioNTech acquired InstaDeep to enhance its pioneering role in utilizing AI for drug discovery, design, and development. This purchase enables the establishment of an all-encompassing capability to discover, design, and create advanced immunotherapies on a large scale by harnessing artificial intelligence and machine learning technologies throughout BioNTech's therapeutic platforms and operations.

In another instance, in May 2023,BeeKeeperAI, Inc., a trailblazer in real-world data collaboration software focused on zero-trust principles, unveiled the widespread accessibility of EscrowAI, a zero-trust collaboration platform safeguarded by patents. This innovative platform utilizes Azure confidential computing to address data sovereignty, privacy, and security issues. EscrowAI empowers HIPAA-compliant research involving complete PHI (Personal Health Information) without compromising the confidentiality of patient data. It significantly shortens the AI development timeline by simplifying collaboration agreements and providing access to more precise data. Some prominent players in the global AI training dataset in healthcare market include:

• Alegion 

• Amazon Web Services, Inc

• Appen Limited

• Cogito Tech LLC

• Deep Vision Data

• Google, LLC (Kaggle)

• Lionbridge Technologies, Inc.

• Microsoft Corporation

• Samasource Inc.

• Scale AI, Inc. 

AI Training Dataset In Healthcare Market Report Scope

Global AI Training Dataset In Healthcare Market Report Segmentation

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

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

  • Text

  • Image/Video

  • Others (Audio, Structured Data, etc.)

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

  • Electronic Health Records

  • Medical Imaging

  • Wearable Devices

  • Telemedicine

  • Others

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

  • North America

    • U.S.

    • Canada

  • Europe

    • Germany

    • UK

    • France

  • Asia Pacific

    • China

    • Japan

    • India

    • South Korea

    • Australia

  • Latin America

    • Mexico

    • Brazil

  • Middle East and Africa

    • Kingdom of Saudi Arabia (KSA)

    • UAE

    • South Africa