세계의 의료 및 생명과학용 NLP 시장 규모 : NLP 기법별, 용도별 - 예측(-2030년)
NLP in Healthcare & Life Sciences Market by NLP Technique (OCR, NER, Sentiment Analysis), Application (Genomics & Precision Medicine, Patient Care & Engagement, Clinical Operations & Decision Support, Biomedical Research) - Global Forecast to 2030
상품코드:1741578
리서치사:MarketsandMarkets
발행일:2025년 05월
페이지 정보:영문 354 Pages
라이선스 & 가격 (부가세 별도)
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한글목차
세계의 의료 및 생명과학용 NLP 시장은 급속히 확대되고 있습니다. 시장 규모는 2025년 51억 8,000만 달러에서 2030년까지 160억 1,000만 달러에 달할 것으로 예측되며, 예측 기간에 CAGR 25.3%의 성장이 전망됩니다.
전자의무기록, 의사 진단서, 병리 보고서 등 비정형화된 임상 데이터의 대폭적인 증가는 실용적인 통찰력을 도출하는 NLP 기술에 대한 수요를 촉진하고 있습니다. 동시에 정밀의료에서 실시간 진단 및 치료 추천을 가능하게 하는 NLP를 통해 임상 의사결정 지원의 중요성이 커지고 있습니다. 그러나 구식 IT 시스템은 최신 NLP 도구와 호환되지 않을 수 있으며, 통합 및 상호운용성 문제가 발생할 수 있습니다. 의료 시스템이 현대화됨에 따라 NLP는 데이터 기반의 개인화된 의료 서비스의 기반이 될 것으로 보입니다.
조사 범위
조사 대상 연도
2020-2030년
기준 연도
2024년
예측 기간
2025-2030년
단위
10억 달러
부문
제공, 전개 방식, NLP 유형, NLP 기법, 용도, 최종사용자
대상 지역
북미, 유럽, 아시아태평양, 중동 및 아프리카, 라틴아메리카
"NLG 유형 부문이 예측 기간 동안 가장 빠른 성장률을 보일 것입니다."
자연어 생성(NLG)은 의료 및 생명과학 분야 NLP 시장에서 가장 빠르게 성장하고 있는 분야로, 구조화된 임상 데이터를 명료하고 인간과 같은 언어로 변환하여 임상 문서를 개선하고 의사의 업무 부담을 줄여주며, 환자 요약, 퇴원 기록, 개인화된 커뮤니케이션을 자동화하여 환자 참여와 치료 순응도를 높입니다. 환자 요약, 퇴원 기록, 개인화된 커뮤니케이션을 자동화하여 환자 참여와 치료 순응도를 높입니다. 의료 분야에서 효율적인 데이터 처리와 자동화에 대한 요구가 증가함에 따라 NLG의 도입이 증가하고 있습니다. 의료 서비스 제공업체들이 업무 효율성과 환자 결과 개선에 집중하는 가운데, NLG는 의료 및 생명과학 분야 NLP 시장에서 확장 가능하고 정확하며 시기적절한 임상 문서화 및 커뮤니케이션을 가능하게 하는 중요한 도구로 자리매김하고 있습니다.
"고유 표현 추출(NER) NLP 기술 부문이 예측 기간 동안 가장 큰 시장 점유율을 차지할 것"
고유 표현 추출(NER)은 비정형 의료 텍스트에서 구조화된 정보를 추출하는 데 있어 중요한 역할을 하기 때문에 의료 및 생명과학 분야 NLP에서 가장 큰 시장 점유율을 차지하고 있습니다. 의사결정, 의료 코딩, 데이터 상호운용성을 향상시킵니다. 대량의 전자 의료 기록, 임상시험 데이터, 생의학 문헌을 정확하게 처리할 수 있는 능력은 진단, 치료 계획 및 연구 개선에 필수적입니다. 의료가 데이터 기반 지식에 점점 더 의존하는 가운데, NER의 정확성과 확장성은 여러 의료 분야에 걸쳐 기초적인 도구가 되고 있습니다.
"북미는 선진화된 인프라와 빠른 기술 도입으로 선두를 달리고 있으며, 아시아태평양은 디지털화와 정부의 AI 이니셔티브에서 가장 빠른 성장세를 기록할 것입니다."
북미는 선진화된 의료 IT 인프라, 디지털 헬스에 대한 강력한 규제 지원, 전자의무기록과 AI의 조기 도입으로 의료 및 생명과학을 위한 NLP 시장을 선도하고 있습니다. 이 지역에는 임상 의사결정, 환자 모니터링, 관리 자동화를 위한 NLP 용도를 적극적으로 개발하고 개발하는 주요 기업 및 연구기관이 있습니다. 한편, 아시아태평양은 의료 시스템의 급속한 디지털화, AI에 대한 투자 증가, 개인화된 의료에 대한 인식 증가로 인해 가장 빠르게 성장하는 시장입니다. 중국, 인도, 일본은 의료 현대화를 최우선 과제로 삼고 있으며, 정부의 이니셔티브으로 AI 통합을 장려하고 있습니다. 이 지역은 의료 수요 증가와 기술 발전으로 인해 향후 몇 년 동안 NLP 도입의 핫스팟이 될 것입니다.
세계의 의료-생명과학용 NLP 시장에 대해 조사 분석했으며, 주요 촉진요인과 억제요인, 경쟁 구도, 향후 동향 등의 정보를 전해드립니다.
목차
제1장 서론
제2장 조사 방법
제3장 주요 요약
제4장 주요 조사 결과
의료 및 생명과학용 NLP 시장 기업에 있어서 매력적인 기회
의료 및 생명과학용 NLP 시장 : 용도별
북미의 의료 및 생명과학용 NLP 시장 : NLP 기법별, 최종사용자별
의료 및 생명과학용 NLP 시장 : 지역별
제5장 시장 개요
서론
시장 역학
성장 촉진요인
성장 억제요인
기회
과제
2025년 미국 관세의 영향 - 의료 및 생명과학용 NLP 시장
서론
주요 관세율
가격 영향 분석
국가/지역에 대한 영향
최종 이용 산업에 대한 영향
의료 및 생명과학용 NLP 시장 발전
의료 및 생명과학용 NLP 시장 : 아키텍처
공급망 분석
생태계 분석
투자 상황 및 자금조달 시나리오
사례 연구 분석
기술 분석
주요 기술
보완 기술
인접 기술
규제 상황
특허 분석
조사 방법
특허 출원 건수 : 서류 유형별
혁신 및 특허 출원
가격 분석
주요 기업 평균 판매 가격 : 제품별(2025년)
평균 판매 가격 : 용도별(2025년)
주요 컨퍼런스 및 이벤트(2025년-2026년)
의료 및 생명과학용 NLP 시장 : 비즈니스 모델
SaaS 모델
컨설팅 서비스 모델
매출 분배 모델
종량 빌링 모델
Porter의 Five Forces 분석
주요 이해관계자와 구입 기준
고객의 비즈니스에 영향을 미치는 동향/혼란
제6장 의료 및 생명과학용 NLP 시장 : 제공별
서론
소프트웨어
스탠드얼론 NLP 소프트웨어
통합형 NLP 소프트웨어
서비스
전문 서비스
매니지드 서비스
제7장 의료 및 생명과학용 NLP 시장 : 전개 방식별
서론
클라우드
On-Premise
제8장 의료 및 생명과학용 NLP 시장 : NLP 유형별
서론
자연어 이해
자연어 생성
제9장 의료 및 생명과학용 NLP 시장 : NLP 기법별
서론
OCR
고유 표현 추출
센티멘트 분석
텍스트 분류
토픽 모델링
텍스트 요약
기타 NLP 기법
제10장 의료 및 생명과학용 NLP 시장 : 용도별
서론
환자 케어 및 관여
임상 업무 및 의사결정 지원
생물의학 연구 및 의약품 개발
경영 및 업무 관리
유전체학 및 정밀의료
의학 교육 및 지식 보급
기타 용도
제11장 의료 및 생명과학용 NLP 시장 : 최종사용자별
서론
임상의
의학 연구자
의료 관리자
건강보험 및 보험 가입자
제약 기업 및 바이오테크놀러지 기업
기타 최종사용자
제12장 의료 및 생명과학용 NLP 시장 : 지역별
서론
북미
북미의 의료 및 생명과학용 NLP 시장 성장 촉진요인
북미의 거시경제 전망
미국
캐나다
유럽
유럽의 의료 및 생명과학용 NLP 시장 성장 촉진요인
유럽의 거시경제 전망
영국
독일
프랑스
이탈리아
스페인
기타 유럽
아시아태평양
아시아태평양의 의료 및 생명과학용 NLP 시장 성장 촉진요인
아시아태평양의 거시경제 전망
중국
일본
인도
한국
호주 및 뉴질랜드
ASEAN
기타 아시아태평양
중동 및 아프리카
중동 및 아프리카의 의료 및 생명과학용 NLP 시장 성장 촉진요인
중동 및 아프리카의 거시경제 전망
사우디아라비아
아랍에미리트(UAE)
남아프리카공화국
이스라엘
기타 중동 및 아프리카
라틴아메리카
라틴아메리카의 의료 및 생명과학용 NLP 시장 성장 촉진요인
라틴아메리카의 거시경제 전망
브라질
멕시코
아르헨티나
기타 라틴아메리카
제13장 경쟁 구도
개요
주요 시장 진출기업의 전략과 강점(2022년-2025년)
매출 분석(2020년-2024년)
시장 점유율 분석(2024년)
제품 비교 분석
제품 비교 분석 : 제공 별
제품 비교 분석 : 용도별
기업 평가와 재무 지표
기업 평가 매트릭스 : 주요 기업(2024년)
기업 평가 매트릭스 : 스타트업/중소기업(2024년)
경쟁 시나리오와 동향
제14장 기업 개요
서론
주요 기업
IBM
MICROSOFT
GOOGLE
AWS
IQVIA
ORACLE
INOVALON
DOLBEY SYSTEMS
AVERBIS
SAS INSTITUTE
SOLVENTUM
PRESS GANEY
ELLIPSIS HEALTH
LEXALYTICS
NVIDIA
GE HEALTHCARE
CLINITHINK
HPE
ONCORA MEDICAL
FLATIRON HEALTH
DATAVANT
EDIFECS
JOHN SNOW LABS
ITREX GROUP
KMS HEALTHCARE
APPINVENTIV
REVEAL HEALTHTECH
VERITIS
OPTUM
HEALTH CATALYST
AMBOSS
MARUTI TECHLABS
DEEPSCRIBE
기타 기업
FORESEE MEDICAL
GNANI.AI
NOTABLE HEALTH
BIOFOURMIS
SUKI AI
WAVE HEALTH TECHNOLOGIES
CORTI
CLOUDMEDX
EMTELLIGENT
ENLITIC
DEEP 6 AI
제15장 인접 시장과 관련 시장
서론
AI시장 - 세계 예측(-2030년)
시장의 정의
시장 개요
자연 언어 이해 시장 - 세계 예측(-2029년)
시장의 정의
시장 개요
제16장 부록
LSH
영문 목차
영문목차
The NLP in healthcare & life sciences market is expanding rapidly, with a projected market size rising from USD 5.18 billion in 2025 to USD 16.01 billion by 2030, at a CAGR of 25.3% during the forecast period. The substantial increase in unstructured clinical data, including electronic health records, physician notes, and pathology reports, drives the demand for NLP technologies to derive actionable insights. Simultaneously, clinical decision support is gaining importance, with NLP enabling real-time diagnostics and treatment recommendations in precision medicine. However, outdated IT systems can be incompatible with modern NLP tools, posing integration and interoperability challenges. As healthcare systems modernize, NLP is poised to become a cornerstone of data-driven, personalized care delivery.
Scope of the Report
Years Considered for the Study
2020-2030
Base Year
2024
Forecast Period
2025-2030
Units Considered
USD (Billion)
Segments
Offering, Deployment Mode, NLP type, NLP technique, Application and End User
Regions covered
North America, Europe, Asia Pacific, Middle East & Africa, Latin America
"NLG type segment to account for the fastest growth rate during the forecast period"
Natural language generation (NLG) is the fastest-growing segment in the NLP in healthcare and life sciences market. NLG transforms structured clinical data into clear, human-like narratives, improving clinical documentation and reducing physician workload. It automates the creation of patient summaries, discharge notes, and personalized communications, enhancing patient engagement and adherence to treatment. The rising need for efficient data handling and automation in healthcare drives NLG adoption. As healthcare providers focus on improving operational efficiency and patient outcomes, NLG becomes a critical tool for enabling scalable, accurate, and timely clinical documentation and communication within the NLP in healthcare and life sciences market.
"Named entity recognition (NER) NLP technique segment to hold the largest market share during the forecast period"
Named entity recognition (NER) holds the largest market share in NLP in healthcare and life sciences because it is critical in extracting structured information from unstructured medical texts. NER enhances clinical decision-making, medical coding, and data interoperability by identifying and classifying key entities such as diseases, drugs, procedures, and patient information. Its ability to accurately process large volumes of electronic health records, clinical trial data, and biomedical literature makes it essential for improving diagnostics, treatment planning, and research. As healthcare increasingly relies on data-driven insights, NER's precision and scalability make it a foundational tool across multiple healthcare applications.
"North America leads with advanced infrastructure and early tech adoption, while Asia Pacific records fastest growth with digitalization and government AI initiatives"
North America leads the NLP in healthcare and life sciences market due to its advanced healthcare IT infrastructure, strong regulatory support for digital health, and early adoption of electronic health records and AI. The region is home to key industry players and research institutions that actively develop and deploy NLP applications for clinical decision-making, patient monitoring, and administrative automation. Meanwhile, Asia Pacific is the fastest-growing market, driven by rapid digitization of healthcare systems, increasing investments in AI, and growing awareness of personalized medicine. China, India, and Japan prioritize healthcare modernization, with government initiatives encouraging AI integration. The region's rising healthcare demands and technological advancements make it a hotspot for NLP adoption in the coming years.
Breakdown of Primaries
In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the NLP in healthcare & life sciences market.
By Company: Tier I - 35%, Tier II - 45%, and Tier III - 20%
By Designation: C-Level Executives - 35%, D-Level Executives -25%, and others - 40%
By Region: North America - 40%, Europe - 30%, Asia Pacific - 20%, Middle East & Africa - 5%, and Latin America - 5%
The report includes a study of key players offering NLP in healthcare & life sciences. It profiles major vendors in the NLP in healthcare & life sciences market. The major market players include Microsoft (US), Google (US), IBM (US), AWS (US), IQVIA (US), Oracle (US), Inovalon (US), Dolbey Systems (US), Averbis (Germany), SAS Institute (US), Solventum (US), Press Ganey (US), Ellipsis Health (US), Lexalytics (US), NVIDIA (US), GE Healthcare (US), Clinithink (US), HPE (US), Oncora Medical (US), Flatiron Health (US), Datavant (US), Edifecs (US), John Snow Labs (US), ITRex Group (US), KMS Healthcare (US), Appinventiv (India), Reveal HealthTech (US), Veritis (US), Optum (US), Health Catalyst (US), AMBOSS (Germany), Maruti Techlabs (India), DeepScribe (US), ForeSee Medical (US), Gnani.ai (India), Notable Health (US), Biofourmis (US), Suki AI (US), Wave Health Technologies (US), Corti (Denmark), CloudMex (US), Emtelligent (Canada), Enlitic (US), and Deep 6 AI (US).
Research Coverage
This research report categorizes the NLP in healthcare & life sciences market based on Offering (software and services), Deployment mode (cloud & on-premises), NLP type (natural language understanding (NLU) and natural language generation (NLG)), NLP technique (optical character recognition (OCR), named entity recognition (NER), sentiment analysis, text classification, topic modeling, text summarization, other NLP techniques), Application (patient care & engagement, clinical operations & decision support, biomedical research & drug development, administrative & operations management, genomics & precision medicine, medical education & knowledge dissemination and other applications), End User (enterprise (clinical practitioners, healthcare researchers, healthcare administrators, health insurance & payer professional, pharmaceutical & biotech companies and other end users), and Region (North America, Europe, Asia Pacific, Middle East & Africa, and Latin America).
The scope of the report covers detailed information regarding the drivers, restraints, challenges, and opportunities influencing the growth of the NLP in healthcare & life sciences market. A detailed analysis of the key industry players was done to provide insights into their business overview, solutions, and services; key strategies; contracts, partnerships, agreements, new product & service launches, and mergers and acquisitions; and recent developments associated with the market. This report also covered the competitive analysis of upcoming startups in the market ecosystem.
Key Benefits of Buying the Report
The report will provide the market leaders/new entrants with information on the closest approximations of the revenue numbers for the overall NLP in healthcare & life sciences market and its subsegments. It will help stakeholders understand the competitive landscape and gain more insights to better position their business and plan suitable go-to-market strategies. It will also help stakeholders understand the market's pulse and provide them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
Analysis of key drivers (NLP makes EHR data actionable by extracting insights from text and large datasets, enhanced decision-making by analyzing complex, unstructured clinical data and supports predictive analytics to identify trends and reduce risks).), restraints (Legacy IT systems hinder compatibility with modern NLP tools and high deployment costs and need for skilled personnel) ), opportunities (Real-time, personalized clinical decision support from diverse data, AI assistants streamlines patient engagement and documentation and multilingual translation improves global healthcare access)), and challenges (Integration with existing workflows requires major adjustments and lack of standardized frameworks for validating NLP effectiveness)).
Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the NLP in healthcare & life sciences market.
Market Development: Comprehensive information about lucrative markets - the report analyzes the NLP in healthcare & life sciences market across varied regions.
Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the NLP in healthcare & life sciences market.
Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading players like Microsoft (US), Google (US), IBM (US), AWS (US), IQVIA (US), Oracle (US), Inovalon (US), Dolbey Systems (US), Averbis (Germany), SAS Institute (US), Solventum (US), Press Ganey (US), Ellipsis Health (US), Lexalytics (US), NVIDIA (US), GE Healthcare (US), Clinithink (US), HPE (US), Oncora Medical (US), Flatiron Health (US), Datavant (US), Edifecs (US), John Snow Labs (US), ITRex Group (US), KMS Healthcare (US), Appinventiv (India), Reveal HealthTech (US), Veritis (US), Optum (US), Health Catalyst (US), AMBOSS (Germany), Maruti Techlabs (India), DeepScribe (US), ForeSee Medical (US), Gnani.ai (India), Notable Health (US), Biofourmis (US), Suki AI (US), Wave Health Technologies (US), Corti (Denmark), CloudMex (US), Emtelligent (Canada), Enlitic (US), and Deep 6 AI (US). The report also helps stakeholders understand the pulse of the NLP in healthcare & life sciences market and provides them with information on key market drivers, restraints, challenges, and opportunities.
TABLE OF CONTENTS
1 INTRODUCTION
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.3 MARKET SCOPE
1.3.1 MARKET SEGMENTATION AND REGIONAL SCOPE
1.3.2 INCLUSIONS AND EXCLUSIONS
1.3.3 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 STAKEHOLDERS
1.6 SUMMARY OF CHANGES
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.1.1 Key data from secondary sources
2.1.2 PRIMARY DATA
2.1.2.1 List of primary participants
2.1.2.2 Breakdown of primaries
2.1.2.3 Key data from primary sources
2.1.2.4 Key industry insights
2.2 MARKET BREAKUP AND DATA TRIANGULATION
2.3 MARKET SIZE ESTIMATION
2.3.1 TOP-DOWN APPROACH
2.3.2 BOTTOM-UP APPROACH
2.4 MARKET FORECAST
2.5 RESEARCH ASSUMPTIONS
2.6 RESEARCH LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN NLP IN HEALTHCARE & LIFE SCIENCES MARKET
4.2 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY APPLICATION
4.3 NORTH AMERICA: NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY NLP TECHNIQUE AND END USER
4.4 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY REGION
5 MARKET OVERVIEW
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Surging volume of unstructured clinical data
5.2.1.2 Rising demand for enhanced care delivery and patient engagement
5.2.1.3 Need for predictive analytics to improve significant health concerns
5.2.1.4 Increasing focus on enhancing clinical decision support
5.2.2 RESTRAINTS
5.2.2.1 Clinical accuracy and reliability concerns
5.2.2.2 Issues related to domain-specific language and medical terminology in NLP model development
5.2.2.3 Complexity in integrating NLP with established healthcare system
5.2.3 OPPORTUNITIES
5.2.3.1 Rising adoption of computer-assisted coding to enhance productivity
5.2.3.2 Emergence of advanced AI technology for generating valuable insights for healthcare
5.2.3.3 Emergence of cognitive computing for medicine applications
5.2.4 CHALLENGES
5.2.4.1 Model training data limitations
5.2.4.2 High cost of implementation and maintenance of NLP technology
5.2.4.3 Explainability and interpretability issues while deploying NLP algorithms
5.3 IMPACT OF 2025 US TARIFF-NLP IN HEALTHCARE & LIFE SCIENCES MARKET
5.3.1 INTRODUCTION
5.3.2 KEY TARIFF RATES
5.3.3 PRICE IMPACT ANALYSIS
5.3.3.1 Strategic shifts and emerging trends
5.3.4 IMPACT ON COUNTRY/REGION
5.3.4.1 US
5.3.4.1.1 Strategic shifts and key observations
5.3.4.2 China
5.3.4.2.1 Strategic shifts and key observations
5.3.4.3 Europe
5.3.4.3.1 Strategic shifts and key observations
5.3.4.4 India
5.3.4.4.1 Strategic shifts and key observations
5.3.5 IMPACT ON END-USE INDUSTRIES
5.3.5.1 Clinical practitioners
5.3.5.2 Healthcare Researchers
5.3.5.3 Pharmaceutical & Biotech companies
5.4 EVOLUTION OF NLP IN HEALTHCARE & LIFE SCIENCES MARKET
5.5 NLP IN HEALTHCARE & LIFE SCIENCES MARKET: ARCHITECTURE
5.6 SUPPLY CHAIN ANALYSIS
5.7 ECOSYSTEM ANALYSIS
5.7.1 SOFTWARE & SERVICE PROVIDERS BY APPLICATION
5.7.1.1 Patient care & engagement
5.7.1.2 Clinical operations & decision support
5.7.1.3 Biomedical research & drug development
5.7.1.4 Administrative & operations management
5.7.1.5 Genomics & precision medicine
5.7.1.6 Medical education & knowledge dissemination
5.8 INVESTMENT LANDSCAPE AND FUNDING SCENARIO
5.9 CASE STUDY ANALYSIS
5.9.1 CASE STUDY 1: CSL BEHRING COLLABORATED WITH IQVIA'S NLP TEAM, LINGUAMATICS, TO CREATE PROOF OF CONCEPT
5.9.2 CASE STUDY 2: ATRIUS HEALTH USED LINGUAMATICS I2E TO CREATE QUERIES TO EXTRACT CLINICAL DATA FROM FREE-TEXT FIELDS WITHIN CLINICIAN PROGRESS NOTES AND CLINICAL REPORTS
5.9.3 CASE STUDY 3: HUMANA ADOPTED WATSON'S VOICE AGENT TO OFFER ENHANCED SELF-SERVICE CAPABILITIES TO HEALTHCARE PROVIDERS
5.9.4 CASE STUDY 4: BIOPHARMACEUTICAL COMPANY DEPLOYED IQVIA'S SOLUTIONS TO CONDUCT HEALTH TECHNOLOGY ASSESSMENT
5.9.5 CASE STUDY 5: PHILIPS ADOPTED AMAZON'S ELASTIC COMPUTE CLOUD (AMAZON EC2) TO ATTAIN SECURE, RESIZABLE COMPUTING CAPACITY
5.10 TECHNOLOGY ANALYSIS
5.10.1 KEY TECHNOLOGIES
5.10.1.1 Generative AI
5.10.1.2 Natural Language Processing (NLP)
5.10.1.3 Machine Learning
5.10.1.4 Computer Vision
5.10.2 COMPLIMENTARY TECHNOLOGIES
5.10.2.1 Conversational AI
5.10.2.2 Emotion AI
5.10.2.3 Cloud computing
5.10.3 ADJACENT TECHNOLOGIES
5.10.3.1 Edge AI
5.10.3.2 Blockchain
5.10.3.3 AR/VR
5.11 REGULATORY LANDSCAPE
5.11.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
5.11.1.1 North America
5.11.1.1.1 US
5.11.1.1.2 Canada
5.11.1.2 Europe
5.11.1.3 Asia Pacific
5.11.1.3.1 China
5.11.1.3.2 India
5.11.1.3.3 Japan
5.11.1.3.4 South Korea
5.11.1.4 Middle East & Africa
5.11.1.4.1 UAE
5.11.1.4.2 KSA
5.11.1.5 Latin America
5.11.1.5.1 Brazil
5.11.1.5.2 Mexico
5.12 PATENT ANALYSIS
5.12.1 METHODOLOGY
5.12.2 PATENTS FILED, BY DOCUMENT TYPE
5.12.3 INNOVATION AND PATENT APPLICATIONS
5.13 PRICING ANALYSIS
5.13.1 AVERAGE SELLING PRICE OF KEY PLAYERS, BY OFFERING, 2025
5.13.2 AVERAGE SELLING PRICE, BY APPLICATION, 2025
5.14 KEY CONFERENCES AND EVENTS, 2025-2026
5.15 NLP IN HEALTHCARE & LIFE SCIENCES MARKET: BUSINESS MODELS
5.15.1 SAAS MODEL
5.15.2 CONSULTING SERVICES MODEL
5.15.3 REVENUE SHARING MODEL
5.15.4 PAY-PER-USE MODEL
5.16 PORTER'S FIVE FORCES ANALYSIS
5.16.1 THREAT OF NEW ENTRANTS
5.16.2 THREAT OF SUBSTITUTES
5.16.3 BARGAINING POWER OF SUPPLIERS
5.16.4 BARGAINING POWER OF BUYERS
5.16.5 INTENSITY OF COMPETITIVE RIVALRY
5.17 KEY STAKEHOLDERS AND BUYING CRITERIA
5.17.1 KEY STAKEHOLDERS IN BUYING PROCESS
5.17.2 BUYING CRITERIA
5.18 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
6 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY OFFERING
6.1 INTRODUCTION
6.1.1 OFFERING: NLP IN HEALTHCARE & LIFE SCIENCES MARKET DRIVERS
6.2 SOFTWARE
6.2.1 STANDALONE NLP SOFTWARE
6.2.1.1 Deliver precise, customized, and secure NLP solutions
6.2.2 INTEGRATED NLP SOFTWARE
6.2.2.1 Enhance clinical workflows and insights
6.3 SERVICES
6.3.1 PROFESSIONAL SERVICES
6.3.1.1 Empower healthcare & life sciences with expert service solutions
6.3.1.2 Training & Consulting Services
6.3.1.3 System integration & implementation
6.3.1.4 Support & maintenance
6.3.2 MANAGED SERVICES
6.3.2.1 Reliable NLP operations with comprehensive managed healthcare services
7 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY DEPLOYMENT MODE
7.1 INTRODUCTION
7.1.1 DEPLOYMENT MODE: NLP IN HEALTHCARE & LIFE SCIENCES MARKET DRIVERS
7.2 CLOUD
7.2.1 LEVERAGE CLOUD-BASED NLP FOR SCALABLE AND COST-EFFECTIVE DATA PROCESSING SOLUTIONS
7.3 ON-PREMISES
7.3.1 SECURE ON-PREMISES NLP DEPLOYMENT FOR COMPLIANCE AND DATA SOVEREIGNTY IN HEALTHCARE
8 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY NLP TYPE
8.1 INTRODUCTION
8.1.1 NLP TYPE: NLP IN HEALTHCARE & LIFE SCIENCES MARKET DRIVERS
8.2 NATURAL LANGUAGE UNDERSTANDING
8.2.1 HARNESSING CLINICAL INSIGHTS BY UNDERSTANDING COMPLEX MEDICAL LANGUAGE IN HEALTHCARE
8.3 NATURAL LANGUAGE GENERATION
8.3.1 DRIVING HEALTHCARE EFFICIENCY AND PATIENT ENGAGEMENT THROUGH ADVANCED SOLUTIONS
9 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY NLP TECHNIQUE
9.1 INTRODUCTION
9.1.1 NLP TECHNIQUE: NLP IN HEALTHCARE & LIFE SCIENCES MARKET DRIVERS
9.2 OPTICAL CHARACTER RECOGNITION
9.2.1 FOCUS ON ENHANCING DATA PROCESSING INTO DIGITAL CONTENT TO DRIVE ADOPTION IN HEALTHCARE SERVICES
9.3 NAMED ENTITY RECOGNITION
9.3.1 GROWING NEED TO ENHANCE DATA ORGANIZATION FOR IMPROVED PATIENT CARE TO PROPEL MARKET
9.4 SENTIMENT ANALYSIS
9.4.1 NEED FOR IMPROVEMENT IN PATIENT CARE AND COMMUNICATION STRATEGIES TO DRIVE MARKET
9.5 TEXT CLASSIFICATION
9.5.1 EMPHASIS ON EMPOWERING HEALTHCARE ORGANIZATIONS FOR ADVANCED ANALYSIS TO BOOST DEMAND
9.6 TOPIC MODELING
9.6.1 NEED FOR UNCOVERING INSIGHTS AND TRENDS FROM TEXTUAL DATA TO DRIVE MARKET
9.7 TEXT SUMMARIZATION
9.7.1 STREAMLINING MEDICAL INSIGHTS IN HEALTHCARE AND LIFE SCIENCES TO DRIVE MARKET
9.8 OTHER NLP TECHNIQUES
10 NLP IN HEALTHCARE & LIFE SCIENCES MARKET, BY APPLICATION
10.1 INTRODUCTION
10.1.1 APPLICATION: NLP IN HEALTHCARE & LIFE SCIENCES MARKET DRIVERS
10.2 PATIENT CARE & ENGAGEMENT
10.2.1 EMPOWERING PATIENT CARE AND ENGAGEMENT THROUGH ADVANCED NLP IN HEALTHCARE SYSTEMS
10.2.2 CONVERSATIONAL AI & VIRTUAL ASSISTANTS
10.2.3 REMOTE MONITORING & TELEHEALTH SUPPORT
10.2.4 PATIENT FEEDBACK & SENTIMENT ANALYSIS
10.2.5 HEALTH RISK ASSESSMENT
10.2.6 OTHERS
10.3 CLINICAL OPERATIONS & DECISION SUPPORT
10.3.1 UNLOCKING CLINICAL INSIGHTS AND STREAMLINING OPERATIONS WITH NLP FOR ENHANCED DECISION-MAKING AND EFFICIENCY
10.3.2 CLINICAL DOCUMENTATION & TRANSCRIPTION
10.3.3 MEDICAL CODING & BILLING AUTOMATION
10.3.4 CLINICAL DECISION SUPPORT
10.3.5 CLINICAL TRIAL MATCHING
10.3.6 OTHERS
10.4 BIOMEDICAL RESEARCH & DRUG DEVELOPMENT
10.4.1 ACCELERATING DRUG DISCOVERY AND RESEARCH INSIGHTS USING NLP IN HEALTHCARE
10.4.2 LITERATURE MINING & KNOWLEDGE EXTRACTION
10.4.3 DRUG DISCOVERY & REPURPOSING
10.4.4 CLINICAL TRIAL DESIGN & OPTIMIZATION
10.4.5 PHARMACOVIGILANCE & SAFETY MONITORING
10.4.6 OTHERS
10.5 ADMINISTRATIVE & OPERATIONS MANAGEMENT
10.5.1 STREAMLINING ADMINISTRATIVE WORKFLOWS WITH NLP TO BOOST EFFICIENCY AND ACCURACY IN HEALTHCARE OPERATIONS
