#Natural Language Processing (NLP) in Healthcare and Life Sciences Market size

[thoratketan]

Veterinary Services Strategies In The Global Market: Key Insights From The 2025 Report

The global Natural Language Processing (Nlp) In Healthcare And Life Sciences Market, valued at USD 4.94 billion in 2023, is projected to reach USD 62.7 billion by 2032, growing at a remarkable compound annual growth rate (CAGR) of 32.6% from 2024 to 2032. This surge is driven by the increasing need for advanced technologies to manage large volumes of healthcare data, the growing adoption of artificial intelligence (AI) solutions, and the need to streamline healthcare processes through automated systems.

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Natural Language Processing, a subset of AI, enables computers to understand, interpret, and manipulate human language. In healthcare and life sciences, NLP plays a vital role in extracting valuable insights from unstructured data sources such as medical records, clinical notes, research papers, and patient interactions. As the healthcare sector continues to expand and generate vast amounts of data, NLP has become a pivotal technology in helping healthcare professionals make more informed decisions, improve patient outcomes, and enhance operational efficiencies.

Key Drivers of Market Growth

Explosion of Healthcare Data: Healthcare data is growing at an unprecedented rate due to the increasing digitization of healthcare systems, electronic health records (EHR), and the rise of telemedicine and remote patient monitoring. A large portion of this data is unstructured, such as doctors' notes, medical transcriptions, and patient conversations. NLP provides a powerful tool to analyze and extract meaningful information from this unstructured data, enabling healthcare providers to make quicker, data-driven decisions.

Rising Demand for Personalized Healthcare: Personalized medicine is gaining significant traction in healthcare as treatments are increasingly tailored to individual patients. NLP plays a key role in this shift by enabling the analysis of diverse data sources, including genomic data, medical records, and patient-reported outcomes. By leveraging NLP, healthcare providers can gain deeper insights into a patient's medical history, genetics, and lifestyle factors, helping to create more effective personalized treatment plans and improve patient outcomes.

Growth in AI and Machine Learning Adoption: The integration of AI and machine learning (ML) with NLP technologies is one of the major factors propelling the market. These technologies allow healthcare providers to automate routine tasks, such as processing clinical notes, coding diagnoses, and identifying patterns in patient data. The combination of NLP and ML can also help with predictive analytics, improving decision-making and enabling proactive care management. The increasing adoption of AI across healthcare sectors, from hospitals to pharmaceutical research, is accelerating the use of NLP solutions.

Improved Operational Efficiencies: NLP has proven to be highly effective in improving operational efficiencies within healthcare organizations. By automating administrative tasks, such as billing, coding, and data entry, NLP helps healthcare providers reduce the administrative burden on staff, streamline workflows, and improve resource allocation. This efficiency not only saves time and costs but also enhances the quality of care delivered to patients.

Increased Focus on Healthcare Research and Drug Discovery: NLP is playing an increasingly important role in accelerating research and drug discovery processes. The ability to quickly analyze vast amounts of scientific literature, clinical trials data, and electronic health records is enhancing the pace of discovery. NLP allows researchers to extract valuable insights from medical literature, identify emerging trends, and analyze clinical trial results in real-time. As the need for faster drug development and research increases, NLP’s capabilities are being harnessed to bring new treatments to market more efficiently.

Regulatory Support and Government Initiatives: Governments around the world are taking steps to support the integration of AI technologies in healthcare. In the U.S., the FDA has been exploring the use of AI and NLP to improve drug development, while various global healthcare agencies are increasingly focusing on digital health and data-driven healthcare solutions. This regulatory support has spurred innovation in the NLP field, leading to more robust, safe, and effective NLP solutions tailored to the healthcare and life sciences sectors.

Key Segments:

By Technique

Smart Assistance

Optical Character Recognition

Auto Coding

Text Analytics

Speech Analytics

Classification and Categorization

By End-use

Providers

Payers

Life Science Companies

Others

Make Enquiry about Natural Language Processing (NLP) in Healthcare and Life Sciences Market

Key Players

Key Service Providers/Manufacturers

IBM Watson Health

Merative

Linguamatics

Haptik

Deepset

Microsoft

Amazon Web Services (AWS)

Google Health

Oracle

Nabla

Corti

Tortus

Grove AI

Infinitus Systems

Regard

Lumeris

Heidi

CitiusTech

Owkin

Insilico Medicine

Conclusion

The Natural Language Processing (NLP) in healthcare and life sciences market is poised for explosive growth over the next decade, fueled by the increasing volume of healthcare data, the rising demand for personalized care, and advancements in AI technology. With a projected market size of USD 62.7 billion by 2032, NLP is set to play a crucial role in transforming healthcare operations, improving patient outcomes, and accelerating research and drug development. As more healthcare providers, pharmaceutical companies, and research institutions adopt NLP technologies, the market will continue to expand, offering innovative solutions to some of the healthcare industry's most pressing challenges.

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Artificial Intelligence In Bioinformatics Market Economic Impact and Global Trade Dynamics to 2033

Introduction

The integration of Artificial Intelligence (AI) in bioinformatics is revolutionizing the landscape of life sciences and healthcare. Bioinformatics, which traditionally involves the collection, analysis, and interpretation of biological data, particularly genetic and genomic data, has always demanded complex and large-scale data processing. With the increasing volumes of biological data generated through high-throughput techniques such as next-generation sequencing (NGS), microarrays, and proteomics, AI is becoming indispensable for effective data analysis, interpretation, and predictive modeling.

The Artificial Intelligence in Bioinformatics market is experiencing rapid growth, driven by the expanding application of AI tools in genomics, drug discovery, personalized medicine, and disease prediction. As we move toward 2032, the industry is expected to see significant advancements and investments, influenced by technological innovations, collaborative research, and increasing demand for data-driven healthcare solutions.

Market Overview

Market Size and Growth Rate

As of 2024, the global Artificial Intelligence in Bioinformatics market is valued at approximately USD 1.5 billion, and it is projected to grow at a compound annual growth rate (CAGR) of around 30%, reaching USD 15 billion by 2032. This exponential growth is fueled by the rising need for precision medicine, increased funding in genomics and life sciences, and the surge in AI adoption across various healthcare sectors.

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Key Drivers of Market Growth

1. Explosion of Biological Data

The volume of biological and biomedical data is growing at an unprecedented rate. Genomics data alone is expected to surpass several exabytes in the coming years. AI technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP) are essential in handling, analyzing, and drawing meaningful insights from these massive datasets.

2. Advancements in Genomics and Precision Medicine

AI enables the identification of genetic mutations, variants, and biomarkers that can predict the likelihood of diseases or responses to treatment. This accelerates the development of precision medicine, where treatment is tailored to individual genetic profiles. AI models can integrate multi-omics data (genomics, transcriptomics, proteomics) to provide a holistic understanding of disease mechanisms.

3. Drug Discovery and Development

Traditional drug discovery is time-consuming and costly. AI streamlines this process by predicting molecular behavior, identifying potential drug candidates, and simulating clinical trials. AI can also analyze historical data to avoid redundant research, making drug discovery more efficient.

4. Growing Collaboration Between Tech and Biotech Companies

Tech giants like Google, IBM, Microsoft, and NVIDIA are increasingly investing in AI-driven healthcare and bioinformatics initiatives. Collaborations between tech firms and pharmaceutical or biotechnology companies are leading to innovative platforms for diagnostics, drug design, and genetic analysis.

Application Areas

1. Genomics and Genome Editing

AI plays a critical role in genomic sequence analysis, alignment, annotation, and variant calling. It enhances the capabilities of tools like CRISPR by predicting off-target effects and optimizing gene-editing strategies.

2. Structural Biology and Proteomics

AI models, such as DeepMind’s AlphaFold, have made significant progress in predicting protein structures from amino acid sequences, a task once thought to be extremely complex. This advancement opens new avenues for understanding diseases at the molecular level.

3. Personalized Medicine

By analyzing a patient’s genetic profile and clinical history, AI can suggest personalized treatment plans, dosage recommendations, and even lifestyle changes. This approach not only improves outcomes but also reduces adverse drug reactions.

4. Biomedical Imaging

AI assists in the interpretation of biomedical images, such as MRI or CT scans, by correlating visual patterns with underlying genetic or molecular data. This fusion of imaging and bioinformatics is critical in early disease detection and treatment planning.

5. Clinical Decision Support

AI-driven platforms can support clinicians by providing evidence-based suggestions, diagnostic insights, and predictive analytics. This reduces diagnostic errors and enhances clinical workflow efficiency.

Market Segmentation

By Technology

Machine Learning

Deep Learning

Natural Language Processing

Computer Vision

By Application

Genomics

Transcriptomics

Proteomics

Metabolomics

Drug Discovery

Clinical Diagnostics

By End-User

Pharmaceutical & Biotechnology Companies

Academic & Research Institutes

Hospitals & Clinics

Contract Research Organizations (CROs)

Regional Analysis

North America

North America, particularly the United States, dominates the AI in bioinformatics market due to a well-established healthcare system, advanced research infrastructure, and strong presence of tech and pharmaceutical companies. Government initiatives like the Precision Medicine Initiative and funding from NIH also bolster the growth of AI applications.

Europe

Europe is witnessing significant growth due to investments in healthcare innovation, personalized medicine, and collaborations between universities and biotech firms. The UK, Germany, and France are leading the way in genomics research and AI implementation.

Asia-Pacific

The Asia-Pacific region is poised for rapid expansion, driven by increasing investments in biotechnology, growing prevalence of chronic diseases, and supportive government policies in countries like China, India, Japan, and South Korea.

Rest of the World

Latin America and the Middle East are emerging markets, with growth potential spurred by the modernization of healthcare infrastructure and increased interest in AI technologies.

Challenges and Restraints

1. Data Privacy and Security

Handling sensitive genomic and health data requires robust cybersecurity measures. Compliance with regulations like GDPR, HIPAA, and other data protection laws is a significant challenge for companies.

2. High Cost of Implementation

AI systems require substantial investments in infrastructure, software development, and skilled personnel. This can be a barrier, especially for small- to medium-sized enterprises (SMEs) and developing countries.

3. Lack of Standardization

The absence of standardized data formats, algorithms, and interoperability among systems hampers the integration of AI across platforms and institutions.

4. Skill Gap

There is a shortage of professionals who possess both domain knowledge in life sciences and expertise in AI technologies. Bridging this gap is crucial for market growth.

Future Outlook

The future of Artificial Intelligence in Bioinformatics is exceptionally promising. Over the next decade, we can expect:

Greater integration of AI with blockchain for secure data sharing and transparency in clinical trials.

Advances in explainable AI (XAI) to improve trust and understanding among clinicians and researchers.

Development of AI-powered biosensors and wearables for continuous health monitoring and real-time data analysis.

Decentralized research models, enabled by federated learning, where data remains localized while AI models are trained collaboratively across institutions.

Global standardization efforts, led by regulatory bodies and consortiums, to ensure interoperability and ethical AI deployment.

Key Players in the Market

IBM Corporation

Google LLC (DeepMind)

NVIDIA Corporation

Microsoft Corporation

Illumina, Inc.

Thermo Fisher Scientific Inc.

BioXcel Therapeutics

BenevolentAI

Insilico Medicine

Sophia Genetics

These companies are investing in R&D, forming strategic partnerships, and developing proprietary AI platforms to stay competitive in this rapidly evolving market.

Conclusion

Artificial Intelligence is poised to transform the bioinformatics landscape, making it more intelligent, efficient, and predictive. From understanding the human genome to discovering new drugs and enabling personalized medicine, the synergy between AI and bioinformatics holds the key to future breakthroughs in healthcare and life sciences. While challenges remain, the momentum is undeniable, and by 2032, AI will be an integral part of every major bioinformatics process. Stakeholders who invest early in this space—whether through technology development, partnerships, or talent acquisition—will be best positioned to lead in the next era of medical innovation.

Read Full Report:-https://www.uniprismmarketresearch.com/verticals/healthcare/artificial-intelligence-in-bioinformatics.html

[market-insider]

Market Growth and Use Cases for Synthetic Data Generation

The global synthetic data generation market size is expected to reach USD 1,788.1 million in 2030 and is projected to grow at a CAGR of 35.3% from 2024 to 2030. Synthetic data has disrupted most industries with the affordability and accessibility of quality training data. Artificial data has gained ground to boost AI and innovation by minimizing data barriers.

Moreover, the exponential growth of smartphones and other smart devices has contributed to the growth of the industry. For instance, customers will receive an uptick from synthetic data to assess the performance of camera modules and decide the optimal camera placement in the car cabin. With the soaring demand for AI systems, synthetic data generation tools will likely gain traction.

Synthetic Data Generation Market Report Highlights

The fully synthetic data segment will grow owing to the need for increased privacy across emerging and advanced economies

Based on end-use, the healthcare & life sciences segment will witness a notable CAGR in the wake of heightened demand for privacy-protecting synthetic data

North America market value will be pronounced on the back of the rising footfall of computer vision and NLP

Geographic expansion may also be noticeable in the coming years. The BFSI, healthcare, manufacturing, and consumer electronics industries continue to rely heavily on synthetic data as a growth enabler and established. Up-and-coming players are expected to strengthen their value propositions 

For More Details or Sample Copy please visit link @: Synthetic Data Generation Market Report

It is worth noting that synthetic data is generally used in tandem with real-world data to test and develop AI algorithms. As companies across industry verticals adopt digitization, industry players are poised to emphasize artificial data to bolster strategies. Synthetic data has the innate ability to enhance the performance of computer vision algorithms to develop intelligent assistants in virtual reality and augmented reality and detect hate speech. Social media platforms, such as Meta (Facebook), could exhibit traction for synthetic data.

For instance, in October 2021, Facebook was reported to have acquired AI. Reverie, a synthetic data startup. It is worth mentioning that in July 2020, AI. Reverie was awarded a USD 1.5 million Phase 2 Small Business Innovation Research (SBIR) contract by AFWERX, an innovation arm of the U.S. Air Force. The company was expected to create synthetic images to train the accuracy of navigation vision algorithms.

The IT & telecommunication sector has shown an increased inclination for artificial data for increased security, scalability, and speed. End-users are likely to seek synthetic data to do away with roadblocks of security and privacy protocols. Some factors, such as advanced privacy-preserving, anonymization, and encryption, have encouraged leading companies to inject funds into synthetic data generation tools.

For instance, in October 2021, Türk Telekom announced infusing funds into four AI-based startups, such as Syntonym, B2Metric, QuantWifi, and Optiyol. Notably, Syntonym is a synthetic data anonymization technology developer.

Asia Pacific is expected to provide lucrative growth opportunities in the wake of the rising prominence of computer vision software, predictive analytics, and natural language processing. For instance, the use of artificial data to organize training data for natural language understanding has grown in popularity. China, Australia, Japan, and India could all be searched for prominently synthetic data to streamline privacy compliance and support client-centered goods and services.

With AI, machine learning, and metaverse counting heavily on large datasets to function effectively, the need for data protection could shift attention towards artificial data. Besides, several data scientists are banking on synthetic data to propel their real-world records and garner actionable insights.

List of Key Players in Synthetic Data Generation Market

MOSTLY AI

Synthesis AI

Statice

YData

Ekobit d.o.o. (Span)

Hazy Limited

SAEC / Kinetic Vision, Inc.

kymeralabs

MDClone

Neuromation

Twenty Million Neurons GmbH (Qualcomm Technologies, Inc.)

Anyverse SL

Informatica Inc.

We have segmented the global synthetic data generation market based on data type, modeling type, offering, application, end-use, and region

[global-research-report]

Data Center Accelerator Market Analysis: Meeting the Demand for Real-Time Data Processing

The global data center accelerator market size is anticipated to reach USD 63.22 billion by 2030, according to a new report by Grand View Research, Inc. The market is expected to grow at a CAGR of 24.7% from 2025 to 2030. The demand for data center accelerators is likely to grow owing to increasing adoption of technologies such as AI, IoT, & big data analytics. The COVID-19 pandemic had a positive impact on the data center accelerator market. Factors such as increased corporate awareness of the advantages that cloud services can offer, increased board pressure to provide more secure & robust IT environments, as well as the establishment of local data centers contributed to the growth of data center accelerators. Demand for businesses that rely on digital infrastructure has increased, which has led to significant growth in demand for data center network services in many industries. Data centers are now maintaining program availability and data security as more businesses and educational institutions already moved online.

Top industries using HPC are healthcare, manufacturing aerospace, urban planning, and finance. The University of Texas at Austin researchers are advancing the science of cancer treatment through the use of HPC. In a ground-breaking 2017 project, researchers examined petabytes of data to look for connections between the genomes of cancer patients and the characteristics of their tumors. This paved the way for the university to apply HPC in additional cancer research, which has now expanded to include efforts to diagnose and treat cases of prostate, blood-related, liver, and skin cancers.

Data Center Accelerator Market Report Highlights

Based on processor, the GPU segment accounted for the maximum revenue share of 44% in 2024. This can be attributed to the increasing use of GPU acceleration in IoT computing, bitcoin mining, AI and machine learning, etc. Moreover, GPU acceleration’s parallel processing architecture is useful in life science analytics such as a genome sequencing.

Based on type, the HPC data center segment is expected to grow at the highest CAGR of 26.0% over the forecast period. This can be attributed to a rising preference for hybrid and cloud-based high performance computing (HPC) solutions, use of HPC in vaccine development, advances in virtualization, etc.

Based on application, the deep learning training segment dominated the market in 2024. This can be attributed to increasing adoption of deep learning in hybrid model integration, self-supervised learning, high performance natural language process (NLP) models, and neuroscience based deep learning.

North America held the largest share of 37.0% in 2024 and is expected to retain its position over the forecast period. Presence of several data center accelerator solution and service providers makes North America a promising region for the market.

Asia Pacific is anticipated to expand at the highest CAGR of over 27.8% over the forecast period. Suitable government policies and the need for data center infrastructure upgradation in Asia Pacific are driving the growth of the data center accelerator market in the region.

In October 2020 Intel Corporation launched Intel Xeon Scalable Platform to assist secure sensitive workloads. This platform has new features that include Intel Platform Firmware Resilience (Intel PFR), Intel Total Memory Encryption (Intel TME), and new cryptographic accelerators to support the platform and advance the overall integrity and confidentiality of data.

Data Center Accelerator Market Segmentation

Grand View Research has segmented the global data center accelerator market report based on processor, type, application, and region:

Data Center Accelerator Processor Outlook (Revenue, USD Billion, 2018 - 2030)

GPU

CPU

FPGA

ASIC

Data Center Accelerator Type Outlook (Revenue, USD Billion, 2018 - 2030)

HPC Data Center

Cloud Data Center

Data Center Accelerator Application Outlook (Revenue, USD Billion, 2018 - 2030)

Deep Learning Training

Public Cloud Interface

Enterprise Interface

Data Center Accelerator Regional Outlook (Revenue, USD Billion, 2018 - 2030)

North America

US

Canada

Mexico

Europe

UK

Germany

France

Asia Pacific

China

India

Japan

Australia

South Korea

Latin America

Brazil

Middle East & Africa (MEA)

UAE

Saudi Arabia

South Africa

List of Key Players

Advanced Micro Devices, Inc.

Dell Inc.

IBM Corporation

Intel Corporation

Lattice Semiconductor

Lenovo Ltd.

Marvell Technology Inc.

Microchip Technology Inc.

Micron Technology, Inc.

NEC Corporation

NVIDIA Corporation

Qualcomm Incorporated

Synopsys Inc.

Order a free sample PDF of the Data Center Accelerator Market Intelligence Study, published by Grand View Research.

[pranalip]

NLP in Healthcare and Life Sciences Market Global Industry Trends and Market Outlook 2024-2033 | Global Insight Services

NLP in healthcare and life sciences is the application of natural language processing (NLP) techniques to the field of healthcare and life sciences. NLP is a branch of artificial intelligence that deals with the interaction between humans and computers. NLP techniques are used to process and understand human language.

In healthcare, NLP is used to process and analyze patient records, clinical trials data, and other health-related data. NLP techniques can be used to identify patterns and trends in data, to improve disease diagnosis, and to develop new treatments. NLP is also being used to develop chatbots and digital assistants that can provide patients with information about their health and help them to manage their condition.

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In life sciences, NLP is being used to process and analyze data from genomics, proteomics, and other life science disciplines. NLP techniques are used to identify patterns and trends in data, to improve disease diagnosis, and to develop new treatments. NLP is also being used to develop chatbots and digital assistants that can provide scientists with information about their research and help them to manage their data.

Key Trends

Some of the key trends in NLP for Healthcare and Life Sciences technology include:

Increased accuracy in predictive modeling: NLP is being used increasingly to create predictive models that can more accurately identify patterns and trends in data. This is especially useful in healthcare, where data is often complex and varied.

Improved patient care: NLP is also being used to improve patient care. By understanding the natural language used in patient records, NLP can help to identify potential problems and offer suggestions for treatment.

Increased use of chatbots: Chatbots are becoming increasingly popular in healthcare, as they can provide a quick and easy way for patients to get answers to their questions. NLP is used to understand the user’s intent and provide a suitable response.

Improved decision-making: NLP is helping to improve decision-making in healthcare by providing a better understanding of the data. This can help to identify potential problems and offer suggestions for treatment.

Greater transparency: NLP is also helping to increase transparency in healthcare. By understanding the natural language used in patient records, NLP can help to identify potential problems and offer suggestions for treatment.

Key Drivers

There are several key drivers of NLP in Healthcare and Life Sciences market. Firstly, the increasing demand for healthcare services is driving the need for NLP in Healthcare and Life Sciences. Secondly, the rising cost of healthcare is another key driver of NLP in Healthcare and Life Sciences market. Thirdly, the increasing number of chronic diseases is another key driver of NLP in Healthcare and Life Sciences market. Fourthly, the increasing number of geriatric population is another key driver of NLP in Healthcare and Life Sciences market. Fifthly, the advancement in technology is another key driver of NLP in Healthcare and Life Sciences market.

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Research Objectives

Estimates and forecast the overall market size for the total market, across product, service type, type, end-user, and region

Detailed information and key takeaways on qualitative and quantitative trends, dynamics, business framework, competitive landscape, and company profiling

Identify factors influencing market growth and challenges, opportunities, drivers and restraints

Identify factors that could limit company participation in identified international markets to help properly calibrate market share expectations and growth rates

Trace and evaluate key development strategies like acquisitions, product launches, mergers, collaborations, business expansions, agreements, partnerships, and R&D activities

Thoroughly analyze smaller market segments strategically, focusing on their potential, individual patterns of growth, and impact on the overall market

To thoroughly outline the competitive landscape within the market, including an assessment of business and corporate strategies, aimed at monitoring and dissecting competitive advancements.

Identify the primary market participants, based on their business objectives, regional footprint, product offerings, and strategic initiatives

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Market Segments

The natural language processing market is segmented on the basis of component, deployment mode, organization size and region. By component, it is bifurcated into software and services. By deployment mode, it is segregated into cloud and on-premises. By organization size, it is categorized into small to medium enterprise (SMEs) and large-scale enterprise. Region-wise, it is studied across North America, Europe, Asia-Pacific, and the rest of the world.

Key Companies

The key players in the NLP in Healthcare and Life Sciences Market include 3M, Cerner Corporation, IBM Corporation, Microsoft Corporation, Health Fidelity, Inc., IQVIA Holdings Inc., Dolbey Systems, Inc., Centene Corporation, Alphabet Inc., Amazon.com, Inc., Averbis GmbH, Clinithink, Wave Health Technologies, Inovalon, Lexalytics, Conversica Inc., SparkCognition, and Hewlett Packard Enterprise Development LP.

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Research Scope

Scope – Highlights, Trends, Insights. Attractiveness, Forecast

Market Sizing – Product Type, End User, Offering Type, Technology, Region, Country, Others

Market Dynamics – Market Segmentation, Demand and Supply, Bargaining Power of Buyers and Sellers, Drivers, Restraints, Opportunities, Threat Analysis, Impact Analysis, Porters 5 Forces, Ansoff Analysis, Supply Chain

Business Framework – Case Studies, Regulatory Landscape, Pricing, Policies and Regulations, New Product Launches. M&As, Recent Developments

Competitive Landscape – Market Share Analysis, Market Leaders, Emerging Players, Vendor Benchmarking, Developmental Strategy Benchmarking, PESTLE Analysis, Value Chain Analysis

Company Profiles – Overview, Business Segments, Business Performance, Product Offering, Key Developmental Strategies, SWOT Analysis.

With Global Insight Services, you receive:

10-year forecast to help you make strategic decisions

In-depth segmentation which can be customized as per your requirements

Free consultation with lead analyst of the report

Infographic excel data pack, easy to analyze big data

Robust and transparent research methodology

Unmatched data quality and after sales service

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[jayanthitbrc]

Uncovering Market Insights on Adaptive AI Industry

Overview and Scope Adaptive AI refers to artificial intelligence systems or algorithms that can adjust and improve performance based on experience, feedback, or environmental changes. These systems aim to support a decision-making framework centered around making faster decisions while remaining flexible to adjust as issues arise. Sizing and Forecast The adaptive AI market size has grown exponentially in recent years. It will grow from $1.07 billion in 2023 to $1.55 billion in 2024 at a compound annual growth rate (CAGR) of 44.1%.  The growth in the historic period can be attributed to growing demand for more intelligent ai systems, increasing demand for ai in various sectors, and rising adoption of self-learning platforms. To access more details regarding this report, visit the link: https://www.thebusinessresearchcompany.com/report/adaptive-ai-global-market-report Segmentation & Regional Insights The adaptive ai market covered in this report is segmented – 1) By Component: Platform, Services 2) By Technology: Machine Learning, Deep Learning, Reinforcement Learning, Natural Language Processing (NLP), Computer Vision 3) By Application: Offline Learning And Adaptation, Real-time, Context-aware Adaptation, Autonomous Decision-Making, Other Applications 4) By End-User: Banking, Financial Services and Insurance(BFSI), Healthcare And Life Sciences, Aerospace And Defense, Information Technology And Telecommunications, Media And Entertainment, Retail And E-Commerce, Manufacturing, Other End Users North America was the largest region in the adaptive AI market in 2023. The regions covered in the adaptive ai market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. Intrigued to explore the contents? Secure your hands-on sample copy of the report: https://www.thebusinessresearchcompany.com/sample.aspx?id=13767&type=smp Major Driver Impacting Market Growth The increasing government initiatives are expected to fuel the growth of the adaptive AI market going forward. Government initiatives refer to specific actions, programs, or policies undertaken by a government to address issues, achieve specific goals, or bring about positive changes in various aspects of society. Governments can provide financial support for research and development in AI, including adaptive AI, by making data and tools readily available to a wider community. Key Industry Players Major companies operating in the adaptive ai market report are Cisco Systems Inc., Tech Mahindra Limited, Databricks Inc., Ness Digital Engineering, C3.ai Inc. The adaptive ai market report table of contents includes: 1. Executive Summary 2. Market Characteristics 3. Market Trends And Strategies 4. Impact Of COVID-19 5. Market Size And Growth 6. Segmentation 7. Regional And Country Analysis . . . 27. Competitive Landscape And Company Profiles 28. Key Mergers And Acquisitions 29. Future Outlook and Potential Analysis Contact Us: The Business Research Company Europe: +44 207 1930 708 Asia: +91 88972 63534 Americas: +1 315 623 0293 Email: info@tbrc.info Follow Us On: LinkedIn: https://in.linkedin.com/company/the-business-research-company Twitter: https://twitter.com/tbrc_info Facebook: https://www.facebook.com/TheBusinessResearchCompany YouTube: https://www.youtube.com/channel/UC24_fI0rV8cR5DxlCpgmyFQ Blog: https://blog.tbrc.info/ Healthcare Blog: https://healthcareresearchreports.com/ Global Market Model: https://www.thebusinessresearchcompany.com/global-market-model

[imarcmarketreport]

Conversational AI Market Size, Share, Trends, Growth and Forecast 2024-2032

IMARC Group's report titled "Conversational AI Market by Component (Platform, Services), Type (Intelligent Virtual Assistant (IVA), Chatbots), Technology (Machine Learning, Deep Learning, Natural Language Processing, Automatic Speech Recognition), Deployment (Cloud-based, On-premises), Organization Size (Large Enterprises, Small and Medium-sized Enterprises), End User (BFSI, Retail and E-commerce, Healthcare and Life Science, Travel and Hospitality, Telecom, Media and Entertainment, and Others), and Region 2024-2032",The global conversational AI market size reached US$ 10.4 Billion in 2023. Looking forward, IMARC Group expects the market to reach US$ 117.0 Billion by 2032, exhibiting a growth rate (CAGR) of 30.69% during 2024-2032.

For an in-depth analysis, you can refer sample copy of the report: https://www.imarcgroup.com/conversational-ai-market/requestsample

Factors Affecting the Growth of the Conversational AI Industry:

Advancements in Natural Language Processing (NLP) and Machine Learning (ML):

The rapid advancements in natural language processing (NLP) and machine learning (ML) technologies is propelling the market growth. These technological improvements are enhancing the ability of conversational artificial intelligence (AI) systems to understand and respond to human language more naturally and accurately. Modern NLP algorithms can interpret and process a wide range of languages and dialects, while ML enables these systems to learn from past interactions and improve over time. This advancement is expanding the potential applications of conversational AI, making it more attractive for various industries, such as healthcare, finance, and retail. Additionally, the increasing integration of these AI systems with other technologies like voice recognition and sentiment analysis is further broadening their capabilities, making them more versatile and efficient.

Rise of Omnichannel Retail and E-commerce:

Businesses are striving to provide seamless shopping experiences across multiple channels like online, mobile, and in-store, which is catalyzing the demand for conversational AI to engage buyers effectively. These AI-driven chatbots and virtual assistants can offer personalized shopping assistance, provide product recommendations, and handle user queries around the clock, enhancing the overall buying experience. In e-commerce, conversational AI helps reduce cart abandonment rates by assisting buyers through the checkout process and addressing their concerns in real-time. Furthermore, as the lines between physical and digital shopping experiences blur, conversational AI plays a crucial role in unifying these channels, providing consistent and efficient user service, and thereby driving sales and customer loyalty.

Cost Reduction and Operational Efficiency:

The rising implementation of conversational AI in business operations to reduce costs while enhancing operational efficiency is strengthening the market growth. These systems automate routine and repetitive tasks, such as answering frequently asked questions or scheduling appointments, which traditionally require human intervention. This automation not only lowers labor costs but also allows human employees to focus on more complex and value-added activities. Additionally, conversational AI systems are scalable and can handle large volumes of interactions without the need for proportional increases in resources, making them highly cost-effective for businesses dealing with seasonal spikes in customer inquiries. This efficiency and scalability are particularly beneficial for sectors like banking, telecommunications, and healthcare, where high volumes of user interactions are common.

Leading Companies Operating in the Global Conversational AI Industry:

Amazon Web Services Inc. (Amazon.com Inc.)

Artificial Solutions

Avaamo Inc.

Conversica Inc.

Creative Virtual Ltd.

Google LLC (Alphabet Inc.)

Gupshup

International Business Machines Corporation

Jio Haptik Technologies Limited (Reliance Industries Limited)

Kore.ai Inc.

Nuance Communications Inc. (Microsoft Corporation)

Oracle Corporation

Rasa Technologies Inc.

SAP SE

Conversational AI Market Report Segmentation:

By Component:

Platform

Services

Support and Maintenance

Training and Consulting

System Integration

Platform exhibits a clear dominance in the market driven by their foundational role in providing infrastructure and integrated solutions for conversational AI applications.

By Type:

Intelligent Virtual Assistant (IVA)

Chatbots

Chatbots represent the largest segment. They are extensively used in industries for user service, engagement, and automated responses.

By Technology:

Machine Learning

Deep Learning

Natural Language Processing

Automatic Speech Recognition

Natural language processing holds the biggest market share as it is crucial for enabling AI systems to understand and respond to human language effectively.

By Deployment:

Cloud-based

On-premises

On-premises accounts for a major share owing to their perceived security benefits and control over data, which is particularly important for sensitive or regulated industries.

By Organization Size:

Large Enterprises

Small and Medium-sized Enterprises

On the basis of the organization size, the market has been bifurcated into large enterprises and small and medium-sized enterprises.

By End User:

BFSI

Retail and E-commerce

Healthcare and Life Science

Travel and Hospitality

Telecom

Media and Entertainment

Others

Retail and e-commerce represent the largest segment attributed to their high reliance on user engagement and personalized shopping experiences facilitated by conversational AI.

Regional Insights:

North America (United States, Canada)

Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, Others)

Europe (Germany, France, United Kingdom, Italy, Spain, Russia, Others)

Latin America (Brazil, Mexico, Others)

Middle East and Africa

North America dominates the market due to the concentration of leading technology companies and high adoption rates of advanced AI technologies in this region.

Global Conversational AI Market Trends:

The growing integration of conversational AI with other emerging technologies, such as the internet of things (IoT), augmented reality (AR), and blockchain, is offering a favorable market outlook. Smart homes are equipped with IoT devices, where conversational AI enables users to control and interact with their devices through voice commands or text. Additionally, in retail, AR combined with conversational AI can offer immersive shopping experiences, allowing buyers to visualize products in a real-world context while receiving interactive assistance. Blockchain integration ensures secure and transparent transactions in conversational AI applications, particularly in finance and e-commerce. This convergence with cutting-edge technologies not only enhances the functionality and appeal of conversational AI but also opens new application areas.

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[latestanalysis]

Global Natural Language Processing (NLP) in Healthcare and Life Sciences Market Relevant Trend, Top Competitors, Segmented by & Region Analysis Forecast till 2027

Natural Language Processing (NLP) in Healthcare and Life Sciences Market

Natural processing language processing (NLP) platform is used in the specific areas of healthcare and life sciences sector which includes clinical documentation, electrical health records, risk management. This platform helps both consumer and service provider to easy workflow. Rise in adoption of digital technology in healthcare industry and customer preference for better healthcare services is expected to boost the market growth over the forecast Period.

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Rise in data operations and use of smart devices is expected to propel the natural language processing (NLP) in healthcare and life sciences market growth. Healthcare and life sciences sector generates large amount of data that is driven by compliance and regulatory requirements, patient care, and record keeping. Increase in trend of digitalization and data driven solutions to improve healthcare delivery system is expected to fuel the market growth. Therefore, increase data driven operations and decision making is major driving factor which is expected to drive the market growth. Moreover, many physicians prefer voice notes instead of handwritten of typed ones. Therefore natural language processing platform can be used to easily interpret speech and update patient records.

Natural language processing (NLP) platform implementation is a major challenge in healthcare and life sciences industry, which is expected to hinder the market growth during this forecast timeline. There are many countries across the globe where healthcare facilities have very little access for general population. Also, lack of healthcare expenditure will hamper the growth of global natural language processing in healthcare and life sciences market.

Market Segmentation

Natural Language Processing (NLP) in Healthcare and Life Sciences Market is segmented into type such as Statistical, Rule-Based, and Hybrid, by component such as Technology, and Services (Managed Services, Professional Services, Support & Maintenance Services, and Consulting Services), by deployment type such as Cloud, and On-Premises. Further, market is segmented into application such as Machine Translation, Question Answering, Automated Information Extraction, Email Filtering, Spelling Correction, and Predictive Risk Analytics.

Also, Natural Language Processing (NLP) in Healthcare and Life Sciences Market is segmented various regions including North America, Latin America, Europe, Asia Pacific, and Middle East & Africa.

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Market Key Players

Some of the key players operating in the Natural Language Processing (NLP) in Healthcare and Life Sciences Market report including 3M, MICROSOFT, CERNER, DOLBEY, NUANCE, IBM, APIXIO, AVERBIS, AWS, GOOGLE.

However, the natural language processing (nlp) in healthcare and life sciences market is quite fragmented and it is a tough competition which is going on, owing the presence of various established names vying for the top position. The target market is also witnessing an ingress of new entrants who are posing challenges to established names with tactical moves like product innovation & new product portfolios. Major companies are countering these challenges by having their own collaboration, merger, acquisition, and other strategies. The natural language processing (nlp) in healthcare and life sciences market report is also keeping an eye on the geographies to understand demographical features.

Browse Full Research Report @ https://qualiketresearch.com/reports-details/Natural-Language-Processing-NLP-in-Healthcare-and-Life-Sciences-Market

[cogitotech]

Information extraction in NLP training datasets works to ascertain the type of data available.  Click More 

[smithema1]

Natural Language Processing (NLP) in Healthcare and Life Sciences Market Size & Share | Industry Report, 2020-2027

Global Natural Language Processing (NLP) in Healthcare and Life Sciences Market Report Segmentation Outlook Forecast by 2020-2027 shows competitive landscape information which involving market share of top Manufacturers like – 3M, MICROSOFT, CERNER, DOLBEY, NUANCE, IBM, APIXIO, AVERBIS, AWS, GOOGLE with SWOT analysis and regional analysis. The Natural Language Processing (NLP) in Healthcare and Life Sciences Market report offers significant information on market growth, driving factors, industry-oriented challenges and latest trends.

The report on Natural Language Processing (NLP) in Healthcare and Life Sciences Market is a comprehensive study and presentation of drivers, restraints, opportunities, demand factors, market size, forecasts, and trends in the Natural Language Processing (NLP) in Healthcare and Life Sciences Market over the period of 2021 to 2027. Furthermore, the report is a collective presentation of primary & secondary research findings.

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The research report studies the Natural Language Processing (NLP) in Healthcare and Life Sciences Market with the help of different methodologies and analyses to offer accurate and in-depth analysis about the market. For a clearer picture, it is divided into several parts to cover different aspects of the market. Each area is then elaborated to help the client comprehend the growth potential of each region and its contribution to the global Natural Language Processing (NLP) in Healthcare and Life Sciences Market. The researchers have used primary & secondary methodologies to conduct the information in the report. This report is aimed at guiding people towards a better, apprehensive, and clearer knowledge of the overall market.

Natural Language Processing (NLP) in Healthcare and Life Sciences Market is segmented by company, region, by Type, and by Component. Players, stakeholders, and other participants in the Natural Language Processing (NLP) in Healthcare and Life Sciences Market will be able to gain the upper hand as they use the report as a powerful resource.

Key Players

Some of the key players operating in the  Natural Language Processing (NLP) in Healthcare and Life Sciences Market report including 3M, MICROSOFT, CERNER, DOLBEY, NUANCE, IBM, APIXIO, AVERBIS, AWS, GOOGLE.

Market Taxonomy

·         By Type Statistical Rule-Based Hybrid

·         By Component Technology Services Managed Services Professional Services Support & Maintenance Services Consulting Services

·         By Deployment Type Cloud On-Premises

·         By application Machine Translation Question Answering Automated Information Extraction Email Filtering Spelling Correction Predictive Risk Analytics

By Region

North America

Latin America

Europe

Asia Pacific

Middle East  & Africa

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[businessconsultingarticle]

Natural Language Processing (NLP) in Healthcare and Life Sciences Market Demand and Applications Forecast to 2027

The Global Natural Language Processing (NLP) in Healthcare and Life Sciences Market research report offers accurate insights for the growth prospects of the Natural Language Processing (NLP) in Healthcare and Life Sciences market in the forecast years 2020-2027. The report is an all-inclusive study of the Natural Language Processing (NLP) in Healthcare and Life Sciences market that offers the readers a better view of the current and emerging market trends through a thorough analysis of the market size, growth trends, supply and demand ratio, market share, sales network and distribution channels, and other key elements.

Furthermore, the report is furnished with the current market scenario with regards to the COVID-19 pandemic. The global public health emergency has disrupted the lives of everyone across the globe. It has also had a substantial impact on the Natural Language Processing (NLP) in Healthcare and Life Sciences industry as global supply chains have been disrupted, and the financial scenario is tight. The report assesses the initial and future impact of the pandemic on the market and its key segments to offer an accurate futuristic outlook for remuneration in a post-COVID-19 scenario.

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Scope of the Market Report: The report offers a holistic overview of the Natural Language Processing (NLP) in Healthcare and Life Sciences industry that offers all the essential information. Furthermore, the market is segmented into product types, applications, end-user industries, and regions. The report is formulated by extensive primary and secondary research and backed by the verified data from industry experts and professionals. It also sheds light on the market size and analyzes the market position. Along with this, the report extensively studies the competitive landscape in the market and profiles the key players of the industry.

The report covers extensive analysis of the key market players in the market, along with their business overview, expansion plans, and strategies. The key players studied in the report include: Cerner Corporation, 3M, Nuance Communications, Inc., IBM Corporation, Heath Fidelity, Microsoft Corporation, Linguamatics, Apixio, Clinithink Inc., and Mmodal IP PLC.

For the purpose of this report, global NLP in healthcare and life sciences market according to Component, Type, Deployment Mode, Application, and Region:

Component Type Outlook (Revenue, USD Billion; 2016-2026)

Service

Support and Maintenance Services

Professional Services

Technology

Optical Character Recognition

Auto Coding

Interactive Voice Response

Pattern and Image Recognition

Text Analytics

Classification and Categorization

Speech Analytics

Type Outlook (Revenue, USD Billion; 2016-2026)

Statistical NLP

Rule-Based NLP

Hybrid NLP

Deployment Mode Type Outlook (Revenue, USD Billion; 2016-2026)

On-Premise

Cloud

Application Type Outlook (Revenue, USD Billion; 2016-2026)

Machine Translation

Predictive Risk Analytics

Report Generation

Automated Information Extraction

Others

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Additionally, the report covers an analysis of the key market regions where the market has substantial size and share. Along with the regions, the report also offers a country-wise analysis to enable better comprehension of the market competition. The key regions covered in the report are as follows:

North America     (U.S., Canada)

Europe     (U.K., Italy, Germany, France, Rest of EU)

Asia Pacific     (India, Japan, China, South Korea, Australia, Rest of APAC)

Latin America     (Chile, Brazil, Argentina, Rest of Latin America)

Middle East & Africa (Saudi Arabia, U.A.E., South Africa, Rest of MEA)

Summary of the Report:

The report offers a growth estimate for 8 years along     with a forecast for market valuation

A thorough qualitative and quantitative analysis of the     Global Natural Language Processing (NLP) in Healthcare and Life Sciences     Market

Advanced analytical tools such as SWOT analysis and     Porter’s Five Forces analysis along with feasibility analysis and     investment return analysis

Comprehensive study of the prominent players engaged in     the market along with their product portfolio and business expansion     strategies

Strategic recommendations for new entrants and     established players

Extensive analysis of drivers, restraints, limitations,     growth prospects and opportunities, and threats

In-depth regional analysis to offer a panoramic view of     the Natural Language Processing (NLP) in Healthcare and Life Sciences     market along with a better understanding of the market’s competitive     scenario

To read more about the report, visit @ https://www.reportsanddata.com/report-detail/natural-language-processing-nlp-in-healthcare-and-life-sciences-market

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[freesuitwhispers]

The “Natural Language Processing (NLP) in Healthcare and Life Sciences Market ”report provides a detailed analysis of global market size, regional and country-level market size, segmentation, market growth, market share, and competitive Landscape.

[aianalytics]

Natural Language Processing: A breakthrough technology in healthcare

Have you ever tried to converse with a computer in the language you speak?

Sounds like a fascinating concept? Well, it is! Lately, there have been noteworthy discoveries in engaging computers to comprehend the language similarly as we do, well known as Natural Language Processing. However, It is anything but a simple errand training machines to see how we, humans convey.

What is Natural Language Processing?

Natural Language Processing, popularly known as NLP, is the field of Artificial Intelligence which gives the computers the ability to understand, read, analyze and interpret the human language. Simply put, it is the relation between the computers and human language. It has several sub-disciplines, including Natural Language Understanding (NLU), Natural Language Generation (NLG), and Natural Language Query (NLQ). Combining the intensity of Artificial Intelligence, computational phonetics, and computer science, NLP permits a machine to comprehend human language which only humans could possibly do, until now. It is viewed as a difficult issue in computer science due to the nature of human language, which makes it troublesome.

The global market size of NLP is growing at a CAGR of 18.6% for period of 2020-2026 and by 2026, it is estimated to reach to USD 27.6 Billion, from USD 9.9 Billion in 2020. The NLP market consists of major growth factors like the increase in smart device usage, growth in the adoption of cloud-based solutions and NLP-based applications to improve customer service, as well as the increase in technological investments in the healthcare industry.

NLP in Healthcare Natural Language Processing is rapidly being adopted in Healthcare industry. It has realized the potential of this cutting edge technology in streamlining the processes. Healthcare industry is quick in understanding the significance of information, gathering data from EMRs/EHRs and other sources. Huge volumes of unstructured patient data is inputted into EHRs on a daily basis, but it’s hard for a computer to help physicians aggregate that critical data. Structured data like claims or CCDAs / FHIR APIs may help determine disease burden, but gives us a limited view of the actual patient record. With the fragmented nature of the healthcare industry, large amount of data is collected in silos having as much as 80 percent of data unstructured and of poor quality. This brings us to a pertinent challenge of data extraction and utilization in the healthcare space through NLP in Healthcare. This unstructured data will take a lot of time and effort if humans try to structure it manually, making the data unusable. This hinders us from taking effective decisions through analytics because of the form of our data. Therefore, NLP can help to leverage this unstructured data as we make a shift gradually from fee for service model to value based care. For healthcare and life sciences, by 2025 the global market size of NLP is assessed to reach USD 3.7 billion from current size in 2020 that is USD 1.5 billion. Also, it is growing at CAGR of 20.5%. The healthcare and life sciences NLP market include the factors such as increasing use of predictive analytics to enhance health outcomes and growing demand for improving Electronic Health Record (EHR) data usability to enhance patient care. The worldwide healthcare and life sciences NLP market consists of important vendors such as the vendors from United States of America include 3M, Lexalytics, AWS, Google, Nuance, Microsoft, IBM and others. Clinithink is a vendor from Georgia. From Germany, Averbis is a major vendor. Linguamatics is a vendor from the United Kingdom.  Use Cases Primarily, this technology is helping the healthcare in the following ways 1.      Comprehending human speech and extracting its meaning 2.      Unlocking unstructured data in databases and documents by mapping out essential concepts and values and allowing physicians to use this information for decision making and analytics Likewise, some of the Use cases in healthcare can be broadly highlighted in the following 3 groups: 1.      Mainstay Use Cases for NLP which has a proven ROI A.     Speech Recognition: NLP allows the transcription of huge amount of clinical notes from speech to text. This reduces the task of physicians to dictate notes and therefore, saves time by avoiding duplication. Many companies are working in medical transcription space like Acusis, SmartMD, IKS Health, Aquity and many more B.     Clinical Documentation: NLP helps the physicians freeing up from the manual and complex structure of EMRs allowing them to focus more on patient care. Nuance and M*Modal have technologies that work in tandem with their speech recognition technologies to capture structured data at the point of care and standardized terminologies for future use In the future, NLP tools could be applied to social media and other public data sets to determine social determinants of health (SDOH) as well as the effectiveness of wellness-based programs and initiatives. C.     Computer Assisted Coding (CAC): Computer-assisted coding extracts information about procedures and therapies to capture every code and maximize claims D.     Data Mining: NLP helps to mine the unstructured patient data allowing organizations to reduce the levels of subjectivity in decision-making and improve the quality of patient care 2.      Emerging Use cases of NLP A.     Clinical Trial matching: Using NLP and machine learning in healthcare to identify patients for a clinical trial with the help of NLP engines for trial matching B.    Pharmacovigilance: IQVIA is trying to apply machine learning with natural language processing to transform many Pharmacovigilance functions for greater accuracy and speed [10] C.     Prior Authorization: NLP modules can be used by Payer to determine prior authorization rapidly. Companies like IBM Watson and Anthem are working on these NLP modules D.     Clinical Decision Support (CDS): CDS help physicians to make better decisions. Also, it is being used to aid clinicians in checking symptoms and diagnosis E.      Risk Adjustment and Hierarchical Condition Categories (HCC): HCC relies on ICD-10 coding to assign risk scores to each patient. NLP can help assign patients a risk factor and use their score to predict the costs of healthcare 3.      Next gen Use cases of NLP A.     Ambient Virtual Scribe: NLP can be used to develop a speech recognition software for clinical documentation giving rise to Virtual scribes limiting the need for human scribes B.    Precision Medicine: NLP can be used for Computational Phenotyping and Biomarker Discovery C.     Population Surveillance: An application of NLP to EMRs can be identifying a subset of an ethnic or racial group for eventually documenting and mapping health disparities

Challenges A key challenge in widespread application of NLP is adapting existing systems to new clinical settings

The Future

NLP technologies finds its applications for a range of purposes in Healthcare and Research, including Clinical documentation, medical transcription, clinical trials, Decision Support System and many more. However, the real-life implementation is still facing obstacles. Even though, NLP has the potential to revolutionize with their breakthrough technology and change the landscape of healthcare industry. It is creating new and exciting opportunities in healthcare delivery and patient experience. Natural Language Processing is here to stay, a technology that gets smarter with time, as it empowers the providers to positively influence the health outcomes.

[meditech-insights]

Virtual Clinical Trials Market is estimated close to $8 Bn in 2020 and is likely to grow at a healthy CAGR of above 10% by 2025

A virtual clinical trial (also known as remote/decentralized clinical trials) refers to digitally empowered clinical trial processes.

GlobalVirtual Clinical Trials Marketis estimated close to $8 Bn (2020) growing above 10% CAGR. The market growth is driven by rising adoption of virtual trials following outbreak of COVID-19 pandemic.

Virtual trials enable easy patient enrolment and remote monitoring

Virtual trials are based on telemedicine and utilize specialised applications for patient engagement and tracking. They significantly reduce the time and financial costs. Additionally, using AI technology helps speed up the process of synthesising and analyzing ever-growing data. Clinical trial sponsors/life science companies are becoming ever more interested in virtual clinical trials due to these advantages.

“AI likely to play important role in virtual clinical trials. Conversational AI for natural language processing (NLP) and image-based AI will increasingly become part of virtual trials. Conversational AI help maintain the patient-clinician engagement while imaging AI to play key role in trials that require radiology”– CTO, Leading Clinical Trial Solutions Company

VC fundings increased significantly post-covid

The number of venture capital funding deals have increased post covid. There were six deals in 2018 which increased to 18 in 2020. Most VC funding occurred in Q3 2020 (10 deals). Notably, Medable - one of the fastest growing decentralized trials platform providers - raised $524 million from various rounds since 2020. One of the biggest rounds occurred in October 2021, where the company received a huge $304 million Series D funding. Growing VC funding will allow companies to develop new technologies and expand their customer and geographic reach. This will then increase overall adoption and virtual clinical trials market growth.

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Regional Adoption: Virtual Clinical Trials Market

The US is now the largest market for virtual clinical trials, according to a comprehensive geographical analysis of the global virtual clinical trials market. On the other hand, European life science/medical device companies have not moved as rapidly to accept virtual trials as U.S. However, there have been developments taking place in EU those are expected to propel the adoption steadily.

For instance,

In September 2019, the Trials@Home project was launched by the public-private partnership viz. Innovative Medicines Initiative (IMI) - to augment the use of digital tools in conduct of clinical trials.

There are 31 members in this large-scale collaborative project from the European Federation of Pharmaceutical Industries and Associations (EFPIA), academics, small and mid-sized life science companies, and patient organizations.

Competitive Landscape Analysis of Virtual Clinical Trials Market

Some of the key players in global virtual clinical trials market are ICON, Parexel, IQVIA, Covance, Thermo Fisher, LEO Innovation Lab, Huma, Medidata, Oracle, CRF Health, Medable, Signant Health, and Clinical Ink.

For More Detailed Insights, Contact Us @ https://meditechinsights.com/contact-us/

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Medi-Tech Insights is a healthcare-focused business research & insights firm. Our clients include Fortune 500 companies, blue-chip investors & hyper-growth start-ups. We have completed 100+ projects in Digital Health, Healthcare IT, Medical Technology, Medical Devices & Pharma Services.

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