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市場調查報告書
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2092983

腦健康預測分析市場預測（2034 年）—按解決方案類型、技術、應用、最終用戶和地區分類的全球分析

Brain Health Predictive Analytics Market Forecasts to 2034 - Global Analysis By Solution Type, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

全球預測性腦健康分析市場預計到 2026 年將達到 24 億美元，並在預測期內以 26.7% 的複合年成長率成長，到 2034 年將達到 160 億美元。

腦健康預測分析是指利用先進的數據驅動研究方法和計算平台，在臨床症狀出現之前預測神經系統疾病的預後，識別認知衰退的早期生物標記，並對患者患有神經退化性疾病的風險進行分層。這些系統整合了包括電子健康記錄、神經影像、基因譜、穿戴式感測器測量和數位化認知功能評估在內的多模態資料調查方法，以建立腦部健康軌跡的預測模型。該技術包含機器學習演算法、統計建模框架和臨床決策支援工具，使醫療保健專業人員能夠針對失智症、中風和其他神經系統疾病高風險族群實施預防性干預措施，並最佳化資源分配。

老化帶來的負擔

隨著全球人口老化加劇，阿茲海默症及相關失智症的發生率在已開發國家和新興國家均呈現激增趨勢，由此產生了對預測大腦健康的分析技術前所未有的需求。醫療保健系統正面臨晚期神經系統疾病治療帶來的高昂成本，促使人們增加對早期檢測和預防技術的投資。製藥公司需要預測工具來對臨床試驗參與者進行分層，並驗證治療效果。這些人口結構和商業性壓力正在推動醫院、研究機構和保險公司等機構對預測分析平台的需求持續成長。

數據整合的複雜性

分散在不同的電子健康記錄系統、影像平台和穿戴式裝置的神經系統數據，為全面實施腦健康預測分析帶來了巨大的技術障礙。各醫療機構在神經影像方案、認知功能評估工具和生物標記測量的標準化仍不完善。針對敏感腦健康資料的隱私法規限制了機構間的資料共用對於穩健的模型訓練至關重要。這些互通性挑戰限制了預測模型在資源豐富的大學醫院之外的準確性和普遍性。

與製藥業的合作

製藥業迫切需要預測性生物標記來支持中樞神經系統藥物的研發，這為腦部健康預測分析提供者帶來了巨大的商業性機會。臨床試驗申辦方正在尋求數位化終點和病患分層工具，以降低試驗失敗率並縮短法規核准時間。分析平台與藥物研發公司之間的合作，透過授權和共同開發契約，創造了持續的收入模式。這些合作正在將預測分析確立為新興精準神經療法研發管線的重要基礎設施。

監理不確定性

醫療保健領域基於人工智慧 (AI) 的診斷和預測工具的監管環境瞬息萬變，由此產生的合規風險威脅著腦健康預測分析的市場發展進程。美國食品藥物管理局(FDA) 和歐洲藥品管理局 (EMA) 關於將軟體歸類為醫療設備的指導意見仍不明確，導致檢驗要求和核准流程存在不確定性。圍繞演算法預測未來認知衰退的法律責任問題也使商業部署變得更加複雜。這些監管方面的模糊性阻礙了醫療機構的採用，並增加了技術開發商的合規成本。

新型冠狀病毒（COVID-19）的影響：

新冠疫情擾亂了常規的認知功能評估和臨床數據收集，同時也凸顯了透過預測分析平台進行遠端腦健康監測的價值。醫療機構加快了數位轉型步伐，將預測工具應用於識別新冠相關神經系統後遺症。疫情後對遠距遠端醫療基礎設施和遠端患者監護的投資，為在傳統臨床環境之外部署預測分析創造了有利條件。此次危機也產生了大規模病毒感染與認知結果連結的資料集，提高了模型訓練的準確性。

預計在預測期內，預測分析平台細分市場將佔據最大的市場佔有率。

由於其全面的整合能力以及在各大醫療系統中成熟的部署經驗，預測分析平台預計將在預測期內佔據最大的市場佔有率。這些平台匯總來自多個來源的神經學數據，提供可操作的風險評分，從而支持臨床決策。健康保險公司重視預測分析在人群健康管理和透過早期療育降低成本的應用。製藥公司利用這些平台最佳化臨床試驗並產生真實世界數據 (REW)。基於雲端的預測分析基礎架構的擴充性使其能夠實現企業級部署。

在預測期內，深度學習領域預計將呈現最高的複合年成長率。

在預測期內，深度學習領域預計將呈現最高的成長率，這主要得益於其卓越的識別能力，能夠識別傳統統計方法無法檢測到的神經影像和多模態神經學資料中的複雜模式。基於海量腦部掃描和認知功能評估資料集訓練的深度神經網路，在早期失智症的診斷準確率方面已達到可與神經科專家媲美的水平。隨著訓練資料在全球醫療網路中的不斷擴展，這些模型的準確率也持續提高。深度學習與聯邦資料架構的融合，使得模型開發能夠在不洩漏病患隱私的前提下進行。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這得益於其先進的醫療保健數據基礎設施以及在神經學領域人工智慧研究方面的大量投資。美國之所以處於主導地位，是因為其建立了電子健康記錄，擁有領先的學術研究聯盟，能夠產生大規模的腦健康資料集，並為基於軟體的醫療設備提供了有利的法規結構。大型科技公司和醫療保健系統正在合作進行預測分析的先導計畫。創業投資為神經技術新創公司提供了資金支持，推動了預測分析領域的持續創新。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於中國、日本和韓國數位醫療基礎設施的快速擴張，以及各國政府對智慧醫療舉措的大力投入。該地區人口老化加劇了對失智症預防技術的迫切需求。政府資助的腦科學研究計畫正在產生大規模的國家資料集，以支持預測模型的發展。該地區的技術製造能力正在降低數據採集基礎設施的硬體成本，而不斷擴大的醫療保險覆蓋範圍也增加了患者獲得預測性篩檢服務的機會。

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    • 根據產品系列、地理覆蓋範圍和策略聯盟對領先公司進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章 全球腦健康預測分析市場：依解決方案類型分類

  • 預測分析平台
  • 腦部健康風險評估軟體
  • 臨床決策支援系統
  • 數位生物標記平台
  • 人口健康分析
  • 遠端腦監測平台

第6章：全球腦健康預測分析市場：依技術分類

  • 人工智慧
  • 機器學習
  • 深度學習
  • 巨量資料分析
  • 雲端運算
  • 數位生物標記
  • 醫療物聯網（IoMT）

第7章 全球腦健康預測分析市場：依應用領域分類

  • 阿茲海默症的預測
  • 失智症風險評估
  • 中風風險預測
  • 帕金森氏症監測
  • 預測認知衰退
  • 臨床試驗的最佳化
  • 群體腦健康管理

第8章：全球腦健康預測分析市場：以最終用戶分類

  • 醫院
  • 診斷中心
  • 研究機構
  • 製藥公司
  • 醫療保健支付方
  • 學術機構
  • 其他最終用戶

第9章 全球腦健康預測分析市場：依地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第10章 戰略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第11章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第12章：公司簡介

  • GE HealthCare
  • Siemens Healthineers AG
  • Philips Healthcare
  • Canon Medical Systems Corporation
  • FUJIFILM Healthcare
  • IBM Corporation
  • Oracle Health
  • Tempus AI
  • Verily Life Sciences
  • IQVIA Holdings Inc.
  • Cambridge Cognition Holdings plc
  • Cogstate Ltd.
  • Compumedics Limited
  • Natus Medical Incorporated
  • EMOTIV Inc.
  • BrainCo Inc.
  • Neuroelectrics
Product Code: SMRC38152

According to Stratistics MRC, the Global Brain Health Predictive Analytics Market is accounted for $2.4 billion in 2026 and is expected to reach $16.0 billion by 2034 growing at a CAGR of 26.7% during the forecast period. Brain health predictive analytics refers to advanced data-driven methodologies and computational platforms designed to forecast neurological outcomes, identify early biomarkers of cognitive decline, and stratify patient risk for neurodegenerative conditions before clinical symptoms manifest. These systems integrate multi-modal data sources, including electronic health records, neuroimaging, genetic profiles, wearable sensor outputs, and digital cognitive assessments, to build predictive models of brain health trajectories. The technology encompasses machine learning algorithms, statistical modeling frameworks, and clinical decision support tools that enable healthcare providers to implement preventive interventions and optimize resource allocation for populations at elevated risk of dementia, stroke, and other neurological disorders.

Market Dynamics:

Driver:

Aging population burden

The rapidly expanding global aging population is creating unprecedented demand for brain health predictive analytics as the incidence of Alzheimer's disease and related dementias escalates across developed and emerging economies. Healthcare systems face unsustainable costs associated with late-stage neurological care, driving investment in early identification and prevention technologies. Pharmaceutical companies require predictive tools to stratify clinical trial participants and demonstrate treatment efficacy. These demographic and commercial pressures generate sustained market expansion for predictive analytics platforms across hospital, research, and payer environments.

Restraint:

Data integration complexity

The fragmentation of neurological data across disparate electronic health record systems, imaging platforms, and wearable devices creates significant technical barriers for comprehensive brain health predictive analytics deployment. Standardization of neuroimaging protocols, cognitive assessment instruments, and biomarker measurements remains incomplete across healthcare institutions. Privacy regulations governing sensitive brain health data restrict cross-institutional data sharing necessary for robust model training. These interoperability challenges limit the accuracy and generalizability of predictive models outside well-resourced academic medical centers.

Opportunity:

Pharmaceutical partnerships

The pharmaceutical industry's urgent need for predictive biomarkers to support central nervous system drug development presents substantial commercial opportunities for brain health predictive analytics providers. Clinical trial sponsors seek digital endpoints and patient stratification tools to reduce trial failure rates and accelerate regulatory approval timelines. Partnerships between analytics platforms and drug developers create recurring revenue models through licensing agreements and joint development arrangements. These collaborations position predictive analytics as essential infrastructure for the emerging precision neurology therapeutic pipeline.

Threat:

Regulatory uncertainty

The evolving regulatory landscape for artificial intelligence-based diagnostic and predictive tools in healthcare creates compliance risks that threaten market development timelines for brain health predictive analytics. FDA and European Medicines Agency guidance on software-as-medical-device classification remains in flux, creating uncertainty regarding validation requirements and approval pathways. Liability concerns surrounding algorithmic predictions of future cognitive decline complicate commercial deployment. These regulatory ambiguities deter healthcare provider adoption and increase compliance costs for technology developers.

Covid-19 Impact:

The COVID-19 pandemic disrupted routine cognitive assessments and clinical data collection while simultaneously highlighting the value of remote brain health monitoring through predictive analytics platforms. Healthcare providers accelerated digital transformation initiatives that incorporated predictive tools for identifying COVID-related neurological sequelae. Post-pandemic, sustained investment in telehealth infrastructure and remote patient monitoring created favorable conditions for deploying predictive analytics outside traditional clinical settings. The crisis also generated large datasets linking viral infection to cognitive outcomes that improved model training.

The predictive analytics platforms segment is expected to be the largest during the forecast period

The predictive analytics platforms segment is expected to account for the largest market share during the forecast period, due to their comprehensive integration capabilities and established deployment across major healthcare systems. These platforms aggregate multi-source neurological data and deliver actionable risk scores that support clinical decision-making at the point of care. Healthcare payers value predictive analytics for population health management and early intervention cost avoidance. Pharmaceutical companies leverage these platforms for clinical trial optimization and real-world evidence generation. The scalability of cloud-based predictive analytics infrastructure supports enterprise-wide deployment.

The deep learning segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the deep learning segment is predicted to witness the highest growth rate, driven by its superior capability to identify complex patterns in neuroimaging and multi-modal neurological data that traditional statistical methods cannot detect. Deep neural networks trained on large datasets of brain scans and cognitive assessments achieve diagnostic accuracy levels comparable to specialist neurologists for early-stage dementia detection. These models continuously improve as training data expands across global healthcare networks. The integration of deep learning with federated data architectures enables model development without compromising patient privacy.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to advanced healthcare data infrastructure and substantial investment in artificial intelligence research for neurological applications. The United States leads with established electronic health record adoption, major academic research consortia generating large-scale brain health datasets, and favorable regulatory frameworks for software-based medical devices. Major technology companies and healthcare systems collaborate on predictive analytics pilots. Venture capital funding for neurotechnology startups sustains continuous innovation in the predictive analytics space.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, due to rapidly expanding digital health infrastructure and government investment in smart healthcare initiatives across China, Japan, and South Korea. Aging populations in the region create urgent demand for dementia prevention technologies. Government-funded brain research programs generate large national datasets that support predictive model development. The region's technology manufacturing capabilities reduce hardware costs for data collection infrastructure, while growing health insurance coverage expands patient access to predictive screening services.

Key players in the market

Some of the key players in Brain Health Predictive Analytics Market include GE HealthCare, Siemens Healthineers AG, Philips Healthcare, Canon Medical Systems Corporation, FUJIFILM Healthcare, IBM Corporation, Oracle Health, Tempus AI, Verily Life Sciences, IQVIA Holdings Inc., Cambridge Cognition Holdings plc, Cogstate Ltd., Compumedics Limited, Natus Medical Incorporated, EMOTIV Inc., BrainCo Inc. and Neuroelectrics.

Key Developments:

In June 2026, GE HealthCare launched an integrated brain health predictive analytics platform combining MRI imaging data with electronic health records to generate dementia risk scores for primary care physician decision support.

In May 2026, Tempus AI expanded its neurological data analytics portfolio to include predictive models for early Alzheimer's detection based on multi-omic biomarker profiles and longitudinal cognitive assessment trajectories.

In April 2026, IBM Corporation introduced a cloud-based brain health analytics solution leveraging Watson cognitive computing to identify stroke risk patterns from emergency department admission data across hospital networks.

Solution Types Covered:

  • Predictive Analytics Platforms
  • Brain Health Risk Assessment Software
  • Clinical Decision Support Systems
  • Digital Biomarker Platforms
  • Population Health Analytics
  • Remote Brain Monitoring Platforms

Technologies Covered:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning
  • Big Data Analytics
  • Cloud Computing
  • Digital Biomarkers
  • Internet of Medical Things (IoMT)

Applications Covered:

  • Alzheimers Disease Prediction
  • Dementia Risk Assessment
  • Stroke Risk Prediction
  • Parkinsons Disease Monitoring
  • Cognitive Decline Prediction
  • Clinical Trial Optimization
  • Population Brain Health Management

End Users Covered:

  • Hospitals
  • Diagnostic Centers
  • Research Institutes
  • Pharmaceutical Companies
  • Healthcare Payers
  • Academic Institutions
  • Other End Users

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global Brain Health Predictive Analytics Market, By Solution Type

  • 5.1 Predictive Analytics Platforms
  • 5.2 Brain Health Risk Assessment Software
  • 5.3 Clinical Decision Support Systems
  • 5.4 Digital Biomarker Platforms
  • 5.5 Population Health Analytics
  • 5.6 Remote Brain Monitoring Platforms

6 Global Brain Health Predictive Analytics Market, By Technology

  • 6.1 Artificial Intelligence
  • 6.2 Machine Learning
  • 6.3 Deep Learning
  • 6.4 Big Data Analytics
  • 6.5 Cloud Computing
  • 6.6 Digital Biomarkers
  • 6.7 Internet of Medical Things (IoMT)

7 Global Brain Health Predictive Analytics Market, By Application

  • 7.1 Alzheimer's Disease Prediction
  • 7.2 Dementia Risk Assessment
  • 7.3 Stroke Risk Prediction
  • 7.4 Parkinson's Disease Monitoring
  • 7.5 Cognitive Decline Prediction
  • 7.6 Clinical Trial Optimization
  • 7.7 Population Brain Health Management

8 Global Brain Health Predictive Analytics Market, By End User

  • 8.1 Hospitals
  • 8.2 Diagnostic Centers
  • 8.3 Research Institutes
  • 8.4 Pharmaceutical Companies
  • 8.5 Healthcare Payers
  • 8.6 Academic Institutions
  • 8.7 Other End Users

9 Global Brain Health Predictive Analytics Market, By Geography

  • 9.1 North America
    • 9.1.1 United States
    • 9.1.2 Canada
    • 9.1.3 Mexico
  • 9.2 Europe
    • 9.2.1 United Kingdom
    • 9.2.2 Germany
    • 9.2.3 France
    • 9.2.4 Italy
    • 9.2.5 Spain
    • 9.2.6 Netherlands
    • 9.2.7 Belgium
    • 9.2.8 Sweden
    • 9.2.9 Switzerland
    • 9.2.10 Poland
    • 9.2.11 Rest of Europe
  • 9.3 Asia Pacific
    • 9.3.1 China
    • 9.3.2 Japan
    • 9.3.3 India
    • 9.3.4 South Korea
    • 9.3.5 Australia
    • 9.3.6 Indonesia
    • 9.3.7 Thailand
    • 9.3.8 Malaysia
    • 9.3.9 Singapore
    • 9.3.10 Vietnam
    • 9.3.11 Rest of Asia Pacific
  • 9.4 South America
    • 9.4.1 Brazil
    • 9.4.2 Argentina
    • 9.4.3 Colombia
    • 9.4.4 Chile
    • 9.4.5 Peru
    • 9.4.6 Rest of South America
  • 9.5 Rest of the World (RoW)
    • 9.5.1 Middle East
      • 9.5.1.1 Saudi Arabia
      • 9.5.1.2 United Arab Emirates
      • 9.5.1.3 Qatar
      • 9.5.1.4 Israel
      • 9.5.1.5 Rest of Middle East
    • 9.5.2 Africa
      • 9.5.2.1 South Africa
      • 9.5.2.2 Egypt
      • 9.5.2.3 Morocco
      • 9.5.2.4 Rest of Africa

10 Strategic Market Intelligence

  • 10.1 Industry Value Network and Supply Chain Assessment
  • 10.2 White-Space and Opportunity Mapping
  • 10.3 Product Evolution and Market Life Cycle Analysis
  • 10.4 Channel, Distributor, and Go-to-Market Assessment

11 Industry Developments and Strategic Initiatives

  • 11.1 Mergers and Acquisitions
  • 11.2 Partnerships, Alliances, and Joint Ventures
  • 11.3 New Product Launches and Certifications
  • 11.4 Capacity Expansion and Investments
  • 11.5 Other Strategic Initiatives

12 Company Profiles

  • 12.1 GE HealthCare
  • 12.2 Siemens Healthineers AG
  • 12.3 Philips Healthcare
  • 12.4 Canon Medical Systems Corporation
  • 12.5 FUJIFILM Healthcare
  • 12.6 IBM Corporation
  • 12.7 Oracle Health
  • 12.8 Tempus AI
  • 12.9 Verily Life Sciences
  • 12.10 IQVIA Holdings Inc.
  • 12.11 Cambridge Cognition Holdings plc
  • 12.12 Cogstate Ltd.
  • 12.13 Compumedics Limited
  • 12.14 Natus Medical Incorporated
  • 12.15 EMOTIV Inc.
  • 12.16 BrainCo Inc.
  • 12.17 Neuroelectrics

List of Tables

  • Table 1 Global Brain Health Predictive Analytics Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global Brain Health Predictive Analytics Market Outlook, By Solution Type (2023-2034) ($MN)
  • Table 3 Global Brain Health Predictive Analytics Market Outlook, By Predictive Analytics Platforms (2023-2034) ($MN)
  • Table 4 Global Brain Health Predictive Analytics Market Outlook, By Brain Health Risk Assessment Software (2023-2034) ($MN)
  • Table 5 Global Brain Health Predictive Analytics Market Outlook, By Clinical Decision Support Systems (2023-2034) ($MN)
  • Table 6 Global Brain Health Predictive Analytics Market Outlook, By Digital Biomarker Platforms (2023-2034) ($MN)
  • Table 7 Global Brain Health Predictive Analytics Market Outlook, By Population Health Analytics (2023-2034) ($MN)
  • Table 8 Global Brain Health Predictive Analytics Market Outlook, By Remote Brain Monitoring Platforms (2023-2034) ($MN)
  • Table 9 Global Brain Health Predictive Analytics Market Outlook, By Technology (2023-2034) ($MN)
  • Table 10 Global Brain Health Predictive Analytics Market Outlook, By Artificial Intelligence (2023-2034) ($MN)
  • Table 11 Global Brain Health Predictive Analytics Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 12 Global Brain Health Predictive Analytics Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 13 Global Brain Health Predictive Analytics Market Outlook, By Big Data Analytics (2023-2034) ($MN)
  • Table 14 Global Brain Health Predictive Analytics Market Outlook, By Cloud Computing (2023-2034) ($MN)
  • Table 15 Global Brain Health Predictive Analytics Market Outlook, By Digital Biomarkers (2023-2034) ($MN)
  • Table 16 Global Brain Health Predictive Analytics Market Outlook, By Internet of Medical Things (IoMT) (2023-2034) ($MN)
  • Table 17 Global Brain Health Predictive Analytics Market Outlook, By Application (2023-2034) ($MN)
  • Table 18 Global Brain Health Predictive Analytics Market Outlook, By Alzheimer's Disease Prediction (2023-2034) ($MN)
  • Table 19 Global Brain Health Predictive Analytics Market Outlook, By Dementia Risk Assessment (2023-2034) ($MN)
  • Table 20 Global Brain Health Predictive Analytics Market Outlook, By Stroke Risk Prediction (2023-2034) ($MN)
  • Table 21 Global Brain Health Predictive Analytics Market Outlook, By Parkinson's Disease Monitoring (2023-2034) ($MN)
  • Table 22 Global Brain Health Predictive Analytics Market Outlook, By Cognitive Decline Prediction (2023-2034) ($MN)
  • Table 23 Global Brain Health Predictive Analytics Market Outlook, By Clinical Trial Optimization (2023-2034) ($MN)
  • Table 24 Global Brain Health Predictive Analytics Market Outlook, By Population Brain Health Management (2023-2034) ($MN)
  • Table 25 Global Brain Health Predictive Analytics Market Outlook, By End User (2023-2034) ($MN)
  • Table 26 Global Brain Health Predictive Analytics Market Outlook, By Hospitals (2023-2034) ($MN)
  • Table 27 Global Brain Health Predictive Analytics Market Outlook, By Diagnostic Centers (2023-2034) ($MN)
  • Table 28 Global Brain Health Predictive Analytics Market Outlook, By Research Institutes (2023-2034) ($MN)
  • Table 29 Global Brain Health Predictive Analytics Market Outlook, By Pharmaceutical Companies (2023-2034) ($MN)
  • Table 30 Global Brain Health Predictive Analytics Market Outlook, By Healthcare Payers (2023-2034) ($MN)
  • Table 31 Global Brain Health Predictive Analytics Market Outlook, By Academic Institutions (2023-2034) ($MN)
  • Table 32 Global Brain Health Predictive Analytics Market Outlook, By Other End Users (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) Regions are also represented in the same manner as above.