헬스케어용 생성형 AI 시장 : 업계 동향과 세계 예측(-2035년) - 목적별, 오퍼링 유형별, 용도별, 최종사용자별, 주요 지역별, 주요 참여 기업별

Generative AI in Healthcare Market: Industry Trends and Global Forecasts, Till 2035 - Distribution by Purpose, Type of Offering, Application Area, End-User, Key Geographical Regions and Leading Players:

US $ 4,799 ￦ 6,829,000

PDF (Single User License)

PDF 보고서를 1명만 이용할 수 있는 라이선스입니다. 인쇄 가능하며 인쇄물의 이용 범위는 PDF 이용 범위와 동일합니다.

US $ 6,999 ￦ 9,960,000

PDF (One-Location Site License)

PDF 보고서를 동일 사업장의 특정 사업 부문에 속한 모든 분들이 이용할 수 있는 라이선스입니다. 인쇄 가능하며 인쇄물의 이용 범위는 PDF 이용 범위와 동일합니다.

US $ 10,599 ￦ 15,083,000

PDF (Department License)

PDF 보고서를 부서 단위로 12명까지 이용할 수 있는 라이선스입니다. 인쇄 가능하며 인쇄물의 이용 범위는 PDF 이용 범위와 동일합니다.

US $ 17,999 ￦ 25,614,000

PDF (Enterprise License)

PDF 보고서를 동일 기업의 모든 분이 이용할 수 있는 라이선스입니다. 인쇄 가능하며 인쇄물의 이용 범위는 PDF 이용 범위와 동일합니다.

한글목차

헬스케어용 생성형 AI 시장 개요

세계의 헬스케어용 생성형 AI 시장 규모는 현재 33억 달러로, 예측 기간 중 28%의 CAGR로 확대하며, 2035년까지 398억 달러에 달할 것으로 예측됩니다.

헬스케어 분야의 생성형 AI 시장 기회는 다음과 같은 부문으로 분류됩니다.

목적

• 임상 기반 목적
• 시스템 기반 목적

제공 제품 유형

• 기술/플랫폼
• 서비스

용도

• 신약개발과 개발
• 진단
• 치료
• 관리업무
• 기타

최종사용자

• 제약회사 및 생명과학 기업
• 의료 서비스 프로바이더
• 기타 최종사용자

주요 지역

• 북미
• 유럽
• 아시아태평양
• 중동 및 북아프리카
• 라틴아메리카

북미 시장

• 미국
• 캐나다

유럽 시장

• 독일
• 영국
• 프랑스
• 스페인
• 스위스
• 네덜란드
• 기타 유럽

아시아태평양 시장

• 중국
• 일본
• 한국
• 싱가포르
• 인도
• 기타

중동 및 북아프리카 시장

• 이스라엘
• 아랍에미리트
• 기타

라틴아메리카 시장

• 브라질
• 기타

헬스케어 분야의 생성형 AI 시장 성장과 동향

생성형 AI는 인공지능의 한 부분으로, 생성 모델을 활용하여 인사이트, 이미지, 영상, 동영상 및 기타 형태의 데이터베이스 출력을 생성합니다. 헬스케어 분야에서 이 기술은 빠르게 발전하고 있으며, 환자 관리, 연구, 치료를 변화시킬 수 있는 잠재력을 가지고 있습니다.

헬스케어 산업은 현재 임상 워크플로우의 비효율성, 치료비 상승, 인력 부족, 의료진의 소진 등 많은 문제를 안고 있는 복잡한 상황을 극복하고 있습니다. Medscape의 2024 의사 소진과 우울증 보고서에 따르면 의사의 약 49%가 소진을 느끼고 있으며, 관리 업무 부담(62%)과 장시간 노동(41%)을 느끼고 있다고 응답했습니다. 또한 기존의 신약개발 방식은 맞춤 치료 접근법에 초점을 맞추지 않고, 여전히 시간 집약적이라는 단점이 있습니다. 또한 신약 후보물질의 약 90%는 막대한 시간과 자금을 투자했음에도 불구하고 임상시험 단계에 진입하지 못하는 경우가 많습니다. 이러한 높은 실패율은 혁신을 저해할 뿐만 아니라 전 세계 헬스케어 시스템의 재정적 부담을 증가시킵니다.

이러한 문제를 해결하기 위해 여러 제약사 및 생명과학 기업이 생성형 AI 도입에 관심을 보이고 있습니다. 또한 헬스케어 산업에서 생성형 AI는 전반적인 업무 효율성 향상을 위한 관리 프로세스 자동화, 고급 영상 처리를 통한 진단 정확도 향상, 환자 참여의 개인화, 신약 개발 및 개발 가속화에 큰 잠재력을 가지고 있다는 점을 강조하는 것이 중요합니다. 특히 관리 업무에 생성형 AI를 도입하는 것만으로도 헬스케어 부문 전체에서 연간 약 1,500억 달러의 비용 절감 효과를 창출할 수 있습니다. 또한 연구에 따르면 생성형 AI는 진단 오류를 최대 85% 줄이고, 간호사의 초과 근무를 21% 줄일 수 있으며, 그 결과 병원당 3년간 약 46만 9,000달러의 비용 절감 가능성이 있다고 합니다. 그러나 헬스케어 조직이 생성형 인공지능(AI)를 시스템에 통합할 때, 윤리적 AI 사용을 보장하고 데이터 프라이버시, 알고리즘 편향성, 투명성 등 주요 우려 사항을 해결하는 견고한 거버넌스 프레임워크를 구축하는 것이 필수적입니다.

최근 여러 제약회사 및 헬스케어 기업은 다양한 AI 기업과 전략적 파트너십을 맺고 헬스케어 분야에서 생성형 AI의 적용을 모색하고 있습니다. 동시에 일부 생성형 AI 개발 기업은 다양한 의료 용도를 위한 모델 역량을 강화하기 위해 많은 자금을 확보했습니다. 투자자들의 관심 증가와 협업 환경의 확장을 고려할 때, 헬스케어 분야의 생성형 AI 시장은 향후 수년간 지속적인 성장을 이룰 준비가 되어 있습니다.

헬스케어 분야의 생성형 AI 시장 : 주요 인사이트

이 보고서는 헬스케어 분야의 생성형 AI 시장 현황을 살펴보고, 업계내 잠재적인 성장 기회를 파악합니다. 이 보고서의 주요 내용은 다음과 같습니다.

• 헬스케어 산업에서 생성형 AI 솔루션을 제공하는 기업의 45% 이상이 중견기업이며, 그 중 79%가 북미에 본사를 둔 기업입니다.
• >85% 이상의 기업이 다양한 헬스케어 프로세스 간소화를 위한 생성형 AI 기술/플랫폼을 제공하고 있으며, 그 중 27%의 생성형 AI 기업은 의료 제공자와 P/B 기업 모두의 진화하는 요구에 대응하고 있습니다.
• 이 분야에 대한 관심 증가는 파트너십 활동의 활성화에도 반영되고 있으며, 특히 지난 2년간 90%에 가까운 거래가 성사되고 있습니다.

• 당사의 분석에 따르면 헬스케어 시장의 생성형 AI 시장에서는 구매자의 협상력이 매우 높을 것으로 예측됩니다.
• 헬스케어의 관리 부담 증가, 자금 조달 및 투자 증가, AI와 ML의 발전은 헬스케어 분야의 생성형 AI 시장을 촉진하고 예측 가능한 미래에 꾸준한 성장을 가져올 것으로 보입니다.
• 현재 시장에서는 기술/플랫폼 분야가 75%에 가까운 점유율을 차지하고 있으며, 특히 헬스케어 분야의 생성형 AI 시장은 2035년까지 연평균 28%의 높은 성장률(CAGR)로 성장할 것으로 예측됩니다.

헬스케어용 생성형 AI 시장 : 주요 부문

목적에 따라 세계 시장은 임상 기반과 시스템 기반 목적으로 구분됩니다. 이러한 유형 중 임상 기반 목적 부문이 현재 전체 시장에서 가장 큰 점유율을 차지하고 있습니다. 이는 임상의가 충분한 정보를 바탕으로 의사결정을 내리는 데 도움이 되고, 병원이나 진료소에서 채택이 증가하여 환자 치료에 직접적인 영향을 미치기 때문으로 보입니다.

제공 제품 유형에 따라 세계 헬스케어용 생성형 AI 시장은 플랫폼/기술과 서비스로 구분됩니다. 현재 전체 시장에서 플랫폼/기술 분야가 가장 높은 점유율을 차지하고 있습니다. 그러나 서비스 부문은 예측 기간 중 상대적으로 높은 CAGR로 성장할 것으로 예상된다는 점에 유의하는 것이 중요합니다.

세계 헬스케어 생성형 AI 시장은 용도별로 신약개발 및 시장개발, 진단, 치료, 관리업무, 기타 용도로 구분됩니다. 현재 치료 분야가 헬스케어 분야의 생성형 AI 시장을 주도하고 있습니다. 이러한 추세는 앞으로도 변하지 않을 것임을 강조하고 싶습니다. 이는 생성형 AI가 치료 효과와 환자 치료를 향상시키고, 관리 부담을 줄이며, 임상 용도의 지속적인 성장을 가속한다는 사실에 기인합니다.

최종사용자별로 헬스케어용 생성형 AI 세계 시장은 제약 및 생명과학 기업, 의료 제공자, 기타 최종사용자로 구분됩니다. 현재 시장은 의료 서비스 프로바이더가 사용하기 위한 시스템을 통해 창출되는 매출에 의해 지배되고 있습니다. 또한 환자 치료 강화, 업무 효율성 향상, 데이터베이스 인사이트, 비용 절감 가능성 등 제네 AI 솔루션의 광범위한 적용 가능성으로 인해 이러한 시장 동향은 앞으로도 변하지 않을 것으로 보입니다.

주요 지역별로 시장은 북미, 유럽, 아시아태평양, 중동 및 북아프리카, 라틴아메리카로 구분됩니다. 현재 시나리오에서 북미가 가장 큰 시장 점유율을 차지할 가능성이 높습니다. 또한 아시아태평양은 예측 기간 중 상대적으로 높은 CAGR로 성장할 것으로 예상된다는 점은 주목할 만합니다.

헬스케어용 생성형 AI 시장 진출기업 사례

• Amazon Web Services
• C3 AI
• Exscientia
• Google
• Huma
• IBM
• Iktos
• LeewayHertz
• Medical IP
• Microsoft
• NVIDIA
• OpenAI
• Oracle
• PhamaX
• Syntegra

1차 조사 개요

본 조사에서 제시된 의견과 인사이트는 여러 이해관계자와의 논의를 통해 영향을 받았습니다. 조사 보고서에는 다음과 같은 업계 관계자와의 인터뷰 기록이 상세하게 수록되어 있습니다.

• 미국 중견기업, 재무담당 부사장 겸 투자자 대응 책임자
• 이스라엘 중견기업, 마케팅 디렉터, 마케팅 이사
• 프랑스 중견기업, 아시아 담당 용도 사이언티스트

헬스케어용 생성형 AI 시장 조사 대상

이 보고서는 헬스케어 분야의 생성형 AI 시장을 조사했으며, 다음과 같은 조사 결과를 제공합니다.

• 시장 규모 및 기회 분석 :(A) 목적,(B) 제공 유형,(C) 용도,(D) 최종사용자,(E) 주요 지역,(G) 주요 기업 등 주요 시장 부문에 초점을 맞추어 헬스케어 분야의 생성형 AI 시장의 현재 시장 기회와 향후 성장 가능성을 상세하게 분석합니다.
• 시장 영향 분석 : 시장 성장에 영향을 미칠 수 있는(A) 촉진요인,(B) 억제요인,(C) 기회,(D) 기존 과제 등 다양한 요인에 대한 철저한 분석.
• 시장 상황:(A) 제공 유형,(B) 용도,(C) 최종사용자 등 몇 가지 관련 매개 변수를 기반으로 의료 서비스 프로바이더의 생성형 AI를 종합적으로 평가합니다.
• 의료 제공자에서의 생성형 AI 전망:(A) 설립연도,(B) 기업 규모,(C) 본사 소재지,(D) 생성형 AI 기업 유형에 따른 분석과 함께 이 분야에 종사하는 의료 제공자에서의 생성형 AI 기업 리스트를 제공합니다.
• 경쟁 분석 :(A) 기업의 강점,(B) 서비스 포트폴리오의 강점 등 다양한 관련 파라미터를 기반으로 헬스케어용 생성형 AI 프로바이더들의 인사이트 있는 경쟁 인사이트.
• 기업 개요: A) 기업 개요, B) 재무 정보(가능한 경우), C) 헬스케어 분야에서의 생성형 AI 포트폴리오, D) 최근 동향, E) 향후 전망에 대한 정보를 제공합니다.
• 파트너십 및 협업 A)파트너십 체결 연도, B)파트너십 유형, C)파트너사 유형, D)파트너십 목적, E)지역, G)가장 활발한 기업(파트너십 수) 등 몇 가지 관련 매개변수를 기준으로 헬스케어 생성형 AI 시장의 이해관계자들 간에 체결된 파트너십에 대한 상세한 분석.
• 헬스케어 분야에서의 생성형 AI 활용 사례: 헬스케어 분야에서의 생성형 AI 활용 사례에 대한 상세한 사례 연구로, 헬스케어 분야의 다양한 생성형 AI 기업 간의 협업에 대한 정보를 제시합니다. 각 이용 사례는(A) 관련 기업의 개요,(B) 비즈니스 요구사항,(C) 달성된 목표의 세부 사항,(D) 제공된 솔루션 등 다양한 매개변수에 대한 정보를 제공합니다.

목차

섹션 1 리포트 개요

제1장 서문

제2장 조사 방법

제3장 시장 역학

• 챕터 개요
• 예측 조사 방법
• 시장 평가 프레임워크
• 예측 툴과 테크닉
• 주요 고려사항
• 제한 사항

제4장 거시경제 지표

• 챕터 개요
• 시장 역학
• 결론

섹션 2 정성적 인사이트

제5장 개요

제6장 서론

섹션 3 장 개요

제7장 경쟁 구도

• 챕터 개요
• 헬스케어용 생성형 AI 기업 : 시장 구도

제8장 기업 경쟁력 분석

• 챕터 개요
• 전제와 주요 파라미터
• 조사 방법
• 헬스케어용 생성형 AI 기업 : 기업 경쟁력 분석

섹션 4 기업 개요

제9장 북미의 헬스케어용 생성형 AI 기업

• 챕터 개요
• 북미에 기반을 둔 생성형 AI 기업의 상세한 개요
  • IBM
  • Microsoft
  • NVIDIA
  • OpenAI
• 북미에 기반을 둔 생성형 AI 기업의 간단한 개요
  • Amazon Web Services
  • C3 AI
  • Google
  • Oracle
  • Syntegra

제10장 유럽과 아시아태평양의 헬스케어용 생성형 AI 기업

• 챕터 개요
• 유럽과 아시아태평양에 기반을 둔 생성형 AI 기업의 상세한 개요
  • Huma
  • LeewayHertz
• 유럽과 아시아태평양에 기반을 둔 생성형 AI 기업의 간단한 개요
  • Exscientia
  • Iktos
  • Medical IP
  • PhamaX

섹션 5 시장 동향

제11장 파트너십과 협업

제12장 헬스케어용 생성형 AI : 사용 사례

• 챕터 개요
• 사용 사례 1 : NVIDIA와 Genentech의 협업
• 사용 사례 2 : Insilico Medicine과 Inimmune의 협업
• 사용 사례 3 : OpenAI와 Moderna의 협업
• 사용 사례 4 : Amazon Web Services와 Pfizer의 협업
• 사용 사례 5 : Suki와 Ascension Saint Thomas의 협업
• 사용 사례 6 : Abridge와 Emory Healthcare의 협업
• 사용 사례 7 : Google과 China Medical University Hospital의 제휴

섹션 6 시장 기회 분석

제13장 시장 영향 분석 : 촉진요인, 억제요인, 기회, 과제

제14장 세계의 헬스케어용 생성형 AI 시장

제15장 헬스케어용 생성형 AI 시장(목적별)

제16장 헬스케어용 생성형 AI 시장(제공 유형별)

제17장 헬스케어용 생성형 AI 시장(용도별)

제18장 헬스케어용 생성형 AI 시장(최종사용자별)

제19장 헬스케어용 생성형 AI 시장(지역별)

제20장 헬스케어용 생성형 AI 시장(주요 참여 기업별)

제21장 인접 시장 분석

섹션 7 전략 툴

제22장 Porter's Five Forces 분석

섹션 8 기타 독점적 인사이트

제23장 1차 조사로부터의 인사이트

제24장 결론

섹션 9 부록

제25장 표형식 데이터

제26장 기업·단체 리스트

제27장 커스터마이즈 기회

제28장 루트 서브스크립션 서비스

제29장 저자 상세

KSA

영문 목차

영문목차

GENERATIVE AI in HEALTHCARE MARKET: OVERVIEW

As per Roots Analysis, the global generative AI in healthcare market is currently valued at USD 3.3 billion and is projected to reach USD 39.8 billion by 2035, growing at a CAGR of 28% during the forecast period.

The opportunity for generative AI in healthcare market has been distributed across the following segments:

Purpose

• Clinical-based Purpose
• System-based Purpose

Type of Offering

• Technology / Platform
• Service

Application Area

• Drug Discovery and Development
• Diagnosis
• Treatment
• Administrative Tasks
• Other Application Areas

End-User

• Pharmaceutical and Life Science Companies
• Healthcare Providers
• Other End-Users

Key Geographical Regions

• North America
• Europe
• Asia-Pacific
• Middle East and North Africa
• Latin America

Market in North America

• US
• Canada

Market in Europe

• Germany
• UK
• France
• Spain
• Switzerland
• The Netherlands
• Rest of Europe

Market in Asia-Pacific

• China
• Japan
• South Korea
• Singapore
• India
• Rest of Asia-Pacific

Market in Middle East and North Africa

• Israel
• UAE
• Rest of Middle East and North Africa

Market in Latin America

• Brazil
• Rest of Latin America

Generative AI in Healthcare Market: Growth and Trends

Generative AI is a part of artificial intelligence that utilizes generative models to create data-driven outputs, such as insights, images, videos, and other formats. In the healthcare sector, this technology is evolving rapidly, with the potential to transform patient care, research and treatment.

The healthcare industry is currently navigating a complex landscape marked by a number of challenges, including inefficiencies in clinical workflows, escalating treatment costs, staff shortages, and burnout of the healthcare workers. According to Medscape's 2024 Physician Burnout and Depression Report, nearly 49% of physicians reported feeling burnt out, with administrative burdens (62%) and long working hours (41%). In addition, the conventional drug discovery methods remain time-intensive with no focus on the personalized treatment approaches. Moreover, about 90% of drug candidates fail to progress to advanced clinical trial phases, despite significant time and financial investments. This high failure rate not only impedes innovation but also intensifies the financial strains on the global healthcare systems.

To address these challenges, several pharmaceutical and life sciences companies have increasingly shown interest in exploring the adoption of generative AI. Further, it is important to highlight that generative AI in healthcare industry holds great potential in automating administrative processes for improving the overall operational efficiency, enhancing diagnostic accuracy through advanced imaging, personalizing patient engagement, and accelerating drug discovery and development. Notably, the implementation of generative AI in administrative tasks alone could generate annual savings of approximately USD 150 billion across the healthcare sector. Additionally, studies suggest generative AI could reduce diagnostic errors by up to 85% and reduce nursing overtime by 21%, resulting in potential cost savings of nearly USD 469,000 over the span of three years per hospital. However, as healthcare organizations integrate generative AI into their systems, it is essential to establish robust governance frameworks that ensure ethical AI use and address key concerns, such as data privacy, algorithmic bias, and transparency.

In recent years, several pharmaceutical and healthcare companies have entered into strategic partnerships with various AI firms to explore applications of generative AI in healthcare. Simultaneously, several generative AI developers are securing significant funding in order to enhance their model capabilities for diverse medical applications. Given the growing interest of the investors and the expanding collaborative landscape, generative AI in healthcare market is poised for sustained growth in the coming years.

Generative AI in Healthcare Market: Key Insights

The report delves into the current state of the generative AI in healthcare market and identifies potential growth opportunities within industry. The key takeaways of the report are:

• More than 45% of the companies engaged in offering generative AI solutions in the healthcare industry are mid-sized firms; of these, 79% of the firms are headquartered in North America.
• >85% of the companies offer gen AI technology / platforms to streamline various healthcare processes; of these, 27% of the gen AI companies cater to the evolving needs of both, healthcare providers and P / B companies.
• The rising interest in this domain is reflected by the rise in partnership activity; notably, close to 90% of the deals were inked in the last two years.

• Based on our analysis, in the generative AI in healthcare market, we expect the buyers to have a very high bargaining power; any initiative taken must be carefully evaluated, considering the likely future market dynamics.
• The increasing administrative burden in healthcare, rising funding and investments, and advancements in AI and ML are likely to drive the market for gen AI in healthcare, leading to steady growth in the foreseeable future.
• The technology / platform segment dominates the current market with close to 75% of the market share; notably, generative AI in healthcare market is anticipated to grow at a lucrative growth rate (CAGR of 28%) till 2035.

Generative AI in Healthcare Market: Key Segments

Generative AI used for Clinical-based Purposes is Likely to Hold the Largest Share of the Current Market During the Forecast Period

Based on purpose, the global market is segmented into clinical-based and system-based purposes. Amongst these types, the clinical-based purpose segment occupies the largest share of the current overall market. This can be attributed to their direct impact on patient care, as these would help the clinicians make informed decisions, increasing their adoption in the hospitals and clinics.

Based on the Type of Offering, Platform / Technology Segment Captures the Majority of the Current Market Share

Based on the type of offerings, the global generative AI in healthcare market is segmented into platform / technology and service. Presently, the platform / technology segment occupies the highest share in the overall market. However, it is important to note that the services segment is anticipated to grow at a relatively higher CAGR during the forecast period.

Treatment Segment is Likely to Hold the Largest Share in the Generative AI in Healthcare Market During the Forecast Period

Based on the application area, the global generative AI in healthcare market is segmented into drug discovery and development, diagnosis, treatment, administrative tasks and other application areas. Currently, the treatment segment leads generative AI in healthcare market. It is important to highlight that this trend is unlikely to change in the future as well. This can be attributed to the fact that generative AI boosts treatment efficacy and patient care, reducing administrative burdens, driving sustained growth in clinical applications.

Generative AI in Healthcare Market for Healthcare Providers is Likely to Grow at a Relatively Faster Pace During the Forecast Period

Based on the end-user, the global generative AI in healthcare market is segmented across pharmaceutical and life science companies, healthcare providers and other end-users. Presently, the market is dominated by the revenues generated through the systems intended for use by healthcare providers. Further, this market trend is unlikely to change in the future as well owing to the wider applicability of the gen AI solutions, such as enhanced patient care, improved operational efficiency, data driven insights and cost saving potential.

North America Accounts for the Largest Share in the Market

Based on key geographical regions, the market is segmented into North America, Europe, Asia-Pacific, Middle East and North Africa and Latin America. In the current scenario, North America is likely to capture the largest market share. Further, it is worth highlighting that Asia-Pacific is expected to grow at a relatively high CAGR during the forecast period.

Example Players in the Generative AI in Healthcare Market

• Amazon Web Services
• C3 AI
• Exscientia
• Google
• Huma
• IBM
• Iktos
• LeewayHertz
• Medical IP
• Microsoft
• NVIDIA
• OpenAI
• Oracle
• PhamaX
• Syntegra

Primary Research Overview

The opinions and insights presented in this study were influenced by discussions conducted with multiple stakeholders. The research report features detailed transcripts of interviews held with the following industry stakeholders:

• Vice President, Finance and Head of Investor Relations, Mid-sized Company in the US
• Marketing Director, Mid-sized Company in Israel
• Application Scientist of Asia, Mid-sized Company in France

Generative AI in Healthcare Market: Research Coverage

The report on generative AI in healthcare market features insights into various sections, including:

• Market Sizing and Opportunity Analysis: An in-depth analysis of current market opportunity and the future growth potential of generative AI in healthcare market, focusing on key market segments, including [A] purpose, [B] type of offering, [C] application area, [D] end-user, [E] key geographical regions, and [G] leading players.
• Market Impact Analysis: A thorough analysis of various factors, such as [A] drivers, [B] restraints, [C] opportunities, and [D] existing challenges that are likely to impact market growth.
• Market Landscape: A comprehensive evaluation of generative AI in healthcare providers, based on several relevant parameters, such as [A] type of offering, [B] application area, and [C] end-user.
• Generative AI in Healthcare Providers Landscape: The report features a list of generative AI in healthcare providers engaged in this domain, along with analyses based on [A] year of establishment, [B] company size [C] location of headquarters, and [D] type of generative AI company.
• Company Competitiveness Analysis: An insightful competitiveness analysis of the generative AI in healthcare providers, based on various relevant parameters, such as [A] company strength, and [B] service portfolio strength.
• Company Profiles: Comprehensive profiles of key industry players in the generative AI in the healthcare domain, featuring information on [A] company overview, [B] financial information (if available), [C] generative AI in healthcare portfolio, [D] recent developments, and [E] future outlook statements.
• Partnerships and Collaborations: A detailed analysis of partnerships inked between stakeholders in the generative AI in healthcare market, based on several relevant parameters, such as [A] year of partnership, [B] type of partnership, [C] type of partner company, [D] purpose of partnership, [E] geography, and [G] most active players (in terms of number of partnerships).
• Generative AI in Healthcare Use Cases: A detailed case study of the use cases of generative AI in healthcare, presenting information on collaborations inked between various generative AI companies in healthcare. Each use case provides information on various parameters, such as [A] a brief overview of the companies involved, [B] business needs [C] details on the objectives achieved, and [D] solutions provided.

Key Questions Answered in this Report

• How many companies are currently engaged in this market?
• Which are the leading companies in this market?
• What factors are likely to influence the evolution of this market?
• What is the current and future market size?
• What is the CAGR of this market?
• How is the current and future market opportunity likely to be distributed across key market segments?

Reasons to Buy this Report

• The report provides a comprehensive market analysis, offering detailed revenue projections of the overall market and its specific sub-segments. This information is valuable to both established market leaders and emerging entrants.
• Stakeholders can leverage the report to gain a deeper understanding of the competitive dynamics within the market. By analyzing the competitive landscape, businesses can make informed decisions to optimize their market positioning and develop effective go-to-market strategies.
• The report offers stakeholders a comprehensive overview of the market, including key drivers, barriers, opportunities, and challenges. This information empowers stakeholders to stay abreast of market trends and make data-driven decisions to capitalize on growth prospects.

Additional Benefits

• Complimentary PPT Insights Packs
• Complimentary Excel Data Packs for all Analytical Modules in the Report
• 15% Free Content Customization
• Detailed Report Walkthrough Session with Research Team
• Free Updated report if the report is 6-12 months old or older

TABLE OF CONTENTS

SECTION I: REPORT OVERVIEW

1. PREFACE

• 1.1. Introduction
• 1.2. Market Share Insights
• 1.3. Key Market Insights
• 1.4. Report Coverage
• 1.5. Key Questions Answered
• 1.6. Chapter Outlines

2. RESEARCH METHODOLOGY

• 2.1. Chapter Overview
• 2.2. Research Assumptions
  • 2.2.1. Market Landscape and Market Trends
  • 2.2.2. Market Forecast and Opportunity Analysis
  • 2.2.3. Comparative Analysis
• 2.3. Database Building
  • 2.3.1. Data Collection
  • 2.3.2. Data Validation
  • 2.3.3. Data Analysis
• 2.4. Project Methodology
  • 2.4.1. Secondary Research
    • 2.4.1.1. Annual Reports
    • 2.4.1.2. Academic Research Papers
    • 2.4.1.3. Company Websites
    • 2.4.1.4. Investor Presentations
    • 2.4.1.5. Regulatory Filings
    • 2.4.1.6. White Papers
    • 2.4.1.7. Industry Publications
    • 2.4.1.8. Conferences and Seminars
    • 2.4.1.9. Government Portals
    • 2.4.1.10. Media and Press Releases
    • 2.4.1.11. Newsletters
    • 2.4.1.12. Industry Databases
    • 2.4.1.13. Roots Proprietary Databases
    • 2.4.1.14. Paid Databases and Sources
    • 2.4.1.15. Social Media Portals
    • 2.4.1.16. Other Secondary Sources
  • 2.4.2. Primary Research
    • 2.4.2.1. Types of Primary Research
      • 2.4.2.1.1. Qualitative Research
      • 2.4.2.1.2. Quantitative Research
      • 2.4.2.1.3. Hybrid Approach
    • 2.4.2.2. Advantages of Primary Research
    • 2.4.2.3. Techniques for Primary Research
      • 2.4.2.3.1. Interviews
      • 2.4.2.3.2. Surveys
      • 2.4.2.3.3. Focus Groups
      • 2.4.2.3.4. Observational Research
      • 2.4.2.3.5. Social Media Interactions
    • 2.4.2.4. Key Opinion Leaders Considered in Primary Research
      • 2.4.2.4.1. Company Executives (CXOs)
      • 2.4.2.4.2. Board of Directors
      • 2.4.2.4.3. Company Presidents and Vice Presidents
      • 2.4.2.4.4. Research and Development Heads
      • 2.4.2.4.5. Technical Experts
      • 2.4.2.4.6. Subject Matter Experts
      • 2.4.2.4.7. Scientists
      • 2.4.2.4.8. Doctors and Other Healthcare Providers
    • 2.4.2.5. Ethics and Integrity
      • 2.4.2.5.1. Research Ethics
      • 2.4.2.5.2. Data Integrity
  • 2.4.3. Analytical Tools and Databases
• 2.5. Robust Quality Control

3. MARKET DYNAMICS

• 3.1. Chapter Overview
• 3.2. Forecast Methodology
  • 3.2.1. Top-down Approach
  • 3.2.2. Bottom-up Approach
  • 3.2.3. Hybrid Approach
• 3.3. Market Assessment Framework
  • 3.3.1. Total Addressable Market (TAM)
  • 3.3.2. Serviceable Addressable Market (SAM)
  • 3.3.3. Serviceable Obtainable Market (SOM)
  • 3.3.4. Currently Acquired Market (CAM)
• 3.4. Forecasting Tools and Techniques
  • 3.4.1. Qualitative Forecasting
  • 3.4.2. Correlation
  • 3.4.3. Regression
  • 3.4.4. Extrapolation
  • 3.4.5. Convergence
  • 3.4.6. Sensitivity Analysis
  • 3.4.7. Scenario Planning
  • 3.4.8. Data Visualization
  • 3.4.9. Time Series Analysis
  • 3.4.10. Forecast Error Analysis
• 3.5. Key Considerations
  • 3.5.1. Demographics
  • 3.5.2. Government Regulations
  • 3.5.3. Reimbursement Scenarios
  • 3.5.4. Market Access
  • 3.5.5. Supply Chain
  • 3.5.6. Industry Consolidation
  • 3.5.7. Pandemic / Unforeseen Disruptions Impact
• 3.6. Limitations

4. MACRO-ECONOMIC INDICATORS

• 4.1. Chapter Overview
• 4.2. Market Dynamics
  • 4.2.1. Time Period
    • 4.2.1.1. Historical Trends
    • 4.2.1.2. Current and Forecasted Estimates
  • 4.2.2. Currency Coverage
    • 4.2.2.1. Major Currencies Affecting the Market
    • 4.2.2.2. Factors Affecting Currency Fluctuations on the Industry
    • 4.2.2.3. Impact of Currency Fluctuations on the Industry
  • 4.2.3. Foreign Currency Exchange Rate
    • 4.2.3.1. Impact of Foreign Exchange Rate Volatility on the Market
    • 4.2.3.2. Strategies for Mitigating Foreign Exchange Risk
  • 4.2.4. Recession
    • 4.2.4.1. Assessment of Current Economic Conditions and Potential Impact on the Market
    • 4.2.4.2. Historical Analysis of Past Recessions and Lessons Learnt
  • 4.2.5. Inflation
    • 4.2.5.1. Measurement and Analysis of Inflationary Pressures in the Economy
    • 4.2.5.2. Potential Impact of Inflation on the Market Evolution
  • 4.2.6. Interest Rates
    • 4.2.6.1. Interest Rates and Their Impact on the Market
    • 4.2.6.2. Strategies for Managing Interest Rate Risk
  • 4.2.7. Commodity Flow Analysis
    • 4.2.7.1. Type of Commodity
    • 4.2.7.2. Origins and Destinations
    • 4.2.7.3. Values and Weights
    • 4.2.7.4. Modes of Transportation
  • 4.2.8. Global Trade Dynamics
    • 4.2.8.1. Import Scenario
    • 4.2.8.2. Export Scenario
    • 4.2.8.3. Trade Policies
    • 4.2.8.4. Strategies for Mitigating the Risks Associated with Trade Barriers
    • 4.2.8.5. Impact of Trade Barriers on the Market
  • 4.2.9. War Impact Analysis
    • 4.2.9.1. Russian-Ukraine War
    • 4.2.9.2. Israel-Hamas War
  • 4.2.10. COVID Impact / Related Factors
    • 4.2.10.1. Global Economic Impact
    • 4.2.10.2. Industry-specific Impact
    • 4.2.10.3. Government Response and Stimulus Measures
    • 4.2.10.4. Future Outlook and Adaptation Strategies
  • 4.2.11. Other Indicators
    • 4.2.11.1. Fiscal Policy
    • 4.2.11.2. Consumer Spending
    • 4.2.11.3. Gross Domestic Product
    • 4.2.11.4. Employment
    • 4.2.11.5. Taxes
    • 4.2.11.6. Stock Market Performance
    • 4.2.11.7. Cross Border Dynamics
• 4.3. Conclusion

SECTION II: QUALITATIVE INSIGHTS

5. EXECUTIVE SUMMARY

6. INTRODUCTION

• 6.1. Chapter Overview
• 6.2. Introduction to Generative AI
• 6.3. Evolution of AI
• 6.4. Applications of Generative AI in Healthcare
  • 6.4.1. Healthcare Research
    • 6.4.1.1. Drug Discovery and Development
  • 6.4.2. Disease Diagnosis and Prognosis
  • 6.4.3. Treatment and Medical Care
  • 6.4.4. Marketing and Administrative Tasks
• 6.5. Challenges Associated with the Adoption of Generative AI
• 6.6. Future Perspectives

SECTION III: MARKET OVERVIEW

7. COMPETITIVE LANDSCAPE

• 7.1. Chapter Overview
• 7.2. Generative AI Companies in Healthcare: Overall Market Landscape
  • 7.2.1. Analysis by Year of Establishment
  • 7.2.2. Analysis by Company Size
  • 7.2.3. Analysis by Location of Headquarters
  • 7.2.4. Analysis by Company Size and Location of Headquarters (Region)
  • 7.2.5. Analysis by Type of Generative AI Company
  • 7.2.6. Analysis by Type of Offering
  • 7.2.7. Analysis by Application Area
  • 7.2.8. Analysis by End-user

8. COMPANY COMPETITIVENESS ANALYSIS

• 8.1. Chapter Overview
• 8.2. Assumptions and Key Parameters
• 8.3. Methodology
• 8.4. Generative AI Companies in Healthcare: Company Competitiveness Analysis
  • 8.4.1. Generative AI Companies based in North America
  • 8.4.2. Generative AI Companies based in Europe and Asia-Pacific

SECTION IV: COMPANY PROFILES

9. NORTH AMERICA BASED GENERATIVE AI COMPANIES IN HEALTHCARE

• 9.1. Chapter Overview
• 9.2. Detailed Profiles of Generative AI Companies Based in North America
  • 9.2.1. IBM
    • 9.2.1.1. Company Overview
    • 9.2.1.2. Management Team
    • 9.2.1.3. Contact Details
    • 9.2.1.4. Financial Performance
    • 9.2.1.5. Operating Business Segments
    • 9.2.1.6. Generative AI in Healthcare Portfolio
    • 9.2.1.7. Recent Developments and Future Outlook
  • 9.2.2. Microsoft
  • 9.2.3. NVIDIA
  • 9.2.4. OpenAI
• 9.3. Short Profiles of Generative AI Companies Based in North America
  • 9.3.1. Amazon Web Services
  • 9.3.2. C3 AI
  • 9.3.3. Google
  • 9.3.4. Oracle
  • 9.3.5. Syntegra

10. EUROPE AND ASIA-PACIFIC BASED GENERATIVE AI COMPANIES IN HEALTHCARE

• 10.1. Chapter Overview
• 10.2. Detailed Profiles of Generative AI Companies Based in Europe and Asia-Pacific
  • 10.2.1. Huma
    • 10.2.1.1. Company Overview
    • 10.2.1.2. Management Team
    • 10.2.1.3. Contact Details
    • 10.2.1.4. Generative AI in Healthcare Portfolio
    • 10.2.1.5. Recent Developments and Future Outlook
  • 10.2.2. LeewayHertz
• 10.3. Short Profiles of Generative AI Companies Based in Europe and Asia-Pacific
  • 10.3.1. Exscientia
  • 10.3.2. Iktos
  • 10.3.3. Medical IP
  • 10.3.4. PhamaX

SECTION V: MARKET TRENDS

11. PARTNERSHIPS AND COLLABORATIONS

• 11.1. Chapter Overview
• 11.2. Partnership Models
• 11.3. Generative AI in Healthcare Providers: Partnerships and Collaborations
  • 11.3.1. Analysis by Year of Partnership
  • 11.3.2. Analysis by Type of Partnership
  • 11.3.3. Analysis by Year and Type of Partnership
  • 11.3.4. Analysis by Type of Partner Company
  • 11.3.5. Analysis by Purpose of Partnership
  • 11.3.6. Analysis by Geography
    • 11.3.6.1. Local and International Agreements
    • 11.3.6.2. Intracontinental and Intercontinental Agreements
  • 11.3.7. Most Active Players: Analysis by Number of Partnerships

12. GENERATIVE AI IN HEALTHCARE: USE CASES

• 12.1. Chapter Overview
• 12.2. Use Case 1: Collaboration between NVIDIA and Genentech
  • 12.2.1. NVIDIA
  • 12.2.2. Genentech
  • 12.2.3. Business Needs
  • 12.2.4. Objectives Achieved and Solutions Provided
• 12.3. Use Case 2: Collaboration between Insilico Medicine and Inimmune
  • 12.3.1. Insilico Medicine
  • 12.3.2. Inimmune
  • 12.3.3. Business Needs
  • 12.3.4. Objectives Achieved and Solutions Provided
• 12.4. Use Case 3: Collaboration between OpenAI and Moderna
  • 12.4.1. OpenAI
  • 12.4.2. Moderna
  • 12.4.3. Business Needs
  • 12.4.4. Objectives Achieved and Solutions Provided
• 12.5. Use Case 4: Collaboration between Amazon Web Services and Pfizer
  • 12.5.1. Amazon Web Services
  • 12.5.2. Pfizer
  • 12.5.3. Business Needs
  • 12.5.4. Objectives Achieved and Solutions Provided
• 12.6. Use Case 5: Collaboration between Suki and Ascension Saint Thomas
  • 12.6.1. Suki
  • 12.6.2. Ascension Saint Thomas
  • 12.6.3. Business Needs
  • 12.6.4. Objectives Achieved and Solutions Offered
• 12.7. Use Case 6: Collaboration between Abridge and Emory Healthcare
  • 12.7.1. Abridge
  • 12.7.2. Emory Healthcare
  • 12.7.3. Business Needs
  • 12.7.4. Objectives Achieved and Solutions Offered
• 12.8. Use Case 7: Collaboration between Google and China Medical University Hospital
  • 12.8.1. Google
  • 12.8.2. China Medical University Hospital
  • 12.8.3. Business Needs
  • 12.8.4. Objectives Achieved and Solutions Provided

SECTION VI: MARKET OPPORTUNITY ANALYSIS

13. MARKET IMPACT ANALYSIS: DRIVERS, RESTRAINTS, OPPORTUNITIES AND CHALLENGES

• 13.1. Chapter Overview
• 13.2. Market Drivers
• 13.3. Market Restraints
• 13.4. Market Opportunities
• 13.5. Market Challenges
• 13.6. Conclusion

14. GLOBAL GENERATIVE AI IN HEALTHCARE MARKET

• 14.1. Chapter Overview
• 14.2. Key Assumptions and Methodology
• 14.3. Global Generative AI in Healthcare Market, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 14.4. Multivariate Scenario Analysis
  • 14.4.1. Conservative Scenario
  • 14.4.2. Optimistic Scenario
• 14.5. Key Market Segmentations

15. GENERATIVE AI IN HEALTHCARE MARKET, BY PURPOSE

• 15.1. Chapter Overview
• 15.2. Key Assumptions and Methodology
• 15.3. Generative AI in Healthcare Market: Distribution by Purpose
  • 15.3.1. Generative AI in Healthcare Market for Clinical-based Purpose, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 15.3.2. Generative AI in Healthcare Market for System-based Purpose, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 15.4. Data Triangulation and Validation
  • 15.4.1. Secondary Sources
  • 15.4.2. Primary Sources

16. GENERATIVE AI IN HEALTHCARE MARKET, BY TYPE OF OFFERING

• 16.1. Chapter Overview
• 16.2. Key Assumptions and Methodology
• 16.3. Generative AI in Healthcare Market: Distribution by Type of Offering
  • 16.3.1. Generative AI in Healthcare Market for Technology / Platform, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 16.3.2. Generative AI in Healthcare Market for Services, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 16.4. Data Triangulation and Validation
  • 16.4.1. Secondary Sources
  • 16.4.2. Primary Sources

17. GENERATIVE AI IN HEALTHCARE MARKET, BY APPLICATION AREA

• 17.1. Chapter Overview
• 17.2. Key Assumptions and Methodology
• 17.3. Generative AI in Healthcare Market: Distribution by Application Area
  • 17.3.1. Generative AI in Healthcare Market for Drug Discovery and Development, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 17.3.2. Generative AI in Healthcare Market for Diagnosis, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 17.3.3. Generative AI in Healthcare Market for Treatment, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 17.3.4. Generative AI in Healthcare Market for Administrative Tasks, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 17.3.5. Generative AI in Healthcare Market for Other Applications, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 17.4. Data Triangulation and Validation
  • 17.4.1. Secondary Sources
  • 17.4.2. Primary Sources

18. GENERATIVE AI IN HEALTHCARE MARKET, BY END-USER

• 18.1. Chapter Overview
• 18.2. Key Assumptions and Methodology
• 18.3. Generative AI in Healthcare Market: Distribution by End-user
  • 18.3.1. Generative AI in Healthcare Market for Pharmaceutical and Life Science Companies, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 18.3.2. Generative AI in Healthcare Market for Healthcare Providers, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 18.3.3. Generative AI in Healthcare Market for Other End-users, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 18.4. Data Triangulation and Validation
  • 18.4.1. Secondary Sources
  • 18.4.2. Primary Sources

19. GENERATIVE AI IN HEALTHCARE MARKET, BY GEOGRAPHICAL REGIONS

• 19.1. Chapter Overview
• 19.2. Key Assumptions and Methodology
• 19.3. Generative AI in Healthcare Market: Distribution by Geographical Regions
  • 19.3.1. Generative AI in Healthcare Market in North America, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.1.1. Generative AI in Healthcare Market in the US, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.1.2. Generative AI in Healthcare Market in Canada, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 19.3.2. Generative AI in Healthcare Market in Europe, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.2.1. Generative AI in Healthcare Market in Germany, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.2.2. Generative AI in Healthcare Market in the UK, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.2.3. Generative AI in Healthcare Market in France, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.2.4. Generative AI in Healthcare Market in Spain, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.2.5. Generative AI in Healthcare Market in Switzerland, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.2.6. Generative AI in Healthcare Market in the Netherlands, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.2.7. Generative AI in Healthcare Market in Rest of Europe, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 19.3.3. Generative AI in Healthcare Market in Asia-Pacific, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.3.1. Generative AI in Healthcare Market in China, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.3.2. Generative AI in Healthcare Market in Japan, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.3.3. Generative AI in Healthcare Market in South Korea, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.3.4. Generative AI in Healthcare Market in Singapore, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.3.5. Generative AI in Healthcare Market in India, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.3.6. Generative AI in Healthcare Market in Rest of Asia-Pacific, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 19.3.4. Generative AI in Healthcare Market in Middle East and North Africa, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.4.1. Generative AI in Healthcare Market in Israel, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.4.2. Generative AI in Healthcare Market in UAE, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.4.3. Generative AI in Healthcare Market in Rest of Middle East and North Africa, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 19.3.5. Generative AI in Healthcare Market in Latin America, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.5.1. Generative AI in Healthcare Market in Brazil, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 19.3.5.2. Generative AI in Healthcare Market in Rest of Latin America, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 19.4. Generative AI in Healthcare Market, By Geographical Regions: Market Dynamics Assessment
  • 19.4.1. Penetration-Growth (P-G) Matrix
  • 19.4.2. Market Movement Analysis
• 19.5. Data Triangulation and Validation
  • 19.5.1. Secondary Sources
  • 19.5.2. Primary Sources

20. GENERATIVE AI IN HEALTHCARE MARKET, BY LEADING PLAYERS

• 20.1. Chapter Overview
• 20.2. Key Assumptions and Methodology
• 20.3. Generative AI in Healthcare Market: Distribution by Leading Generative AI Companies
• 20.4. Data Triangulation and Validation

21. ADJACENT MARKET ANALYSIS

SECTION VII: STRATEGIC TOOLS

22. PORTER'S FIVE FORCES ANALYSIS

• 22.1. Chapter Overview
• 22.2. Significance of Porter's Five Forces Analysis
• 22.3. Methodology and Assumptions
• 22.4. Porter's Five Forces
  • 22.4.1. Threats of New Entrants
  • 22.4.2. Bargaining Power of Buyers
  • 22.4.3. Bargaining Power of Generative AI Companies
  • 22.4.4. Threats of Substitute Products
  • 22.4.5. Rivalry Among Existing Competitors
• 22.5. Concluding Remarks

SECTION VIII: OTHER EXCLUSIVE INSIGHTS

23. INSIGHTS FROM PRIMARY RESEARCH

24. CONCLUDING REMARKS

SECTION IX: APPENDIX

25. TABULATED DATA

26. LIST OF COMPANIES AND ORGANIZATIONS

27. CUSTOMIZATION OPPORTUNITIES

28. ROOTS SUBSCRIPTION SERVICES

29. AUTHOR DETAILS

관련자료