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1857047

全球人工智慧個人化營養平台市場：未來預測（至2032年）－按組件、技術、應用、最終用戶、經營模式和區域進行分析

AI-Personalized Nutrition Platforms Market Forecasts to 2032 - Global Analysis By Component, Technology, Application, End User, Business Model, and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，全球 AI 個人化營養平台市場預計到 2025 年將達到 14 億美元，到 2032 年將達到 76 億美元，預測期內複合年成長率為 27.5%。

人工智慧個人化營養平台是利用人工智慧技術提供客製化營養補充建議的數位平台。它們透過分析DNA、腸道菌叢和生活方式等個人數據，制定專屬的營養計劃，從而擺脫千篇一律的飲食模式，走向高度個人化的健康管理。隨著消費者尋求科學驗證的個人化健康解決方案，該市場正在不斷擴張。企業正利用這項技術提供訂閱服務、個人化食材自煮包、針對性營養補充建議等，以提高用戶參與度並改善健康狀況。

據美國營養學會稱，2023 年，人工智慧驅動的營養平台為超過 1,200 萬用戶產生了個人化的飲食建議，並在臨床研究中改善了健康結果。

慢性病盛行率不斷上升，以及對預防性照護的重視

肥胖、糖尿病和心血管疾病等生活方式相關慢性病的日益普遍，推動了對人工智慧個人化營養平台的需求。消費者和醫療保健提供者正著力於預防策略，利用人工智慧主導的洞察，根據個人的健康指標、基因和生活方式模式來最佳化飲食計畫。此外，穿戴式裝置和健康應用程式正在產生即時數據，從而實現個人化的營養建議。這種向積極主動的健康管理和精準營養的轉變，為全球平台開發商和醫療保健整合商創造了持續成長的機會。

部分人工智慧建議缺乏科學檢驗

儘管人工智慧個人化營養解決方案的應用日益廣泛，但由於臨床檢驗有限且缺乏普遍認可的膳食標準，一些解決方案仍面臨質疑。不準確或缺乏實證依據的建議會降低使用者信任度，阻礙應用推廣，並可能導致不良健康後果。此外，不同平台間演算法的不一致性以及缺乏縱向研究也會限制其與醫療保健系統的整合。供應商必須增加對研究合作、臨床試驗和監管合規的投入，以增​​強信譽並加速市場滲透。

拓展至企業健康與保險項目

為了改善員工健康、降低缺勤率和減少醫療保健成本，企業正擴大將人工智慧主導的營養平台融入員工健康舉措和醫療保險方案中。透過與穿戴式裝置和個人化健康監測的整合，可以實現可擴展的預防性干預措施，並提高員工參與度。此外，保險公司正在利用人工智慧洞察來設計客製化的計畫和獎勵，從而推動企業對企業（B2B）的應用。這種擴展不僅為平台提供者創造了持續的商機，也促進了市場的長期成長，尤其是在企業越來越重視整體社會福利和數據主導的健康解決方案的情況下。

資料安全風險和潛在演算法偏差

人工智慧個人化營養平台會收集敏感的健康和生活方式數據，使用戶和供應商面臨隱私外洩和監管審查的風險。加密不足、資料管治不善以及第三方漏洞都可能損害用戶信任，並導致經濟處罰。此外，由於資料集有限或存在偏差而導致的演算法偏差會提供不準確的建議，從而降低其有效性和可靠性。為了降低這些威脅，開發人員必須實施強力的網路安全措施、透明的人工智慧模型和持續的審核，以確保資料完整性、合乎道德的使用以及公平的結果。

新冠疫情的影響：

疫情加速了人工智慧個人化營養平台的普及，因為在健身房和診所受限的情況下，消費者尋求遠端、個人化的健康指導。封鎖措施凸顯了預防性衛生和免疫力的重要性，推動了數位工具和遠端醫療的整合。這些平台的用戶數量迅速成長，應用程式功能不斷擴展，醫療服務提供者和保險公司也增加了投資。這段時期鞏固了人工智慧主導的營養解決方案的長期價值，鼓勵了其持續應用和創新，同時也增強了消費者對數位健康技術的信任。

預計在預測期內，軟體/平台板塊將成為最大的板塊。

預計在預測期內，軟體/平台領域將佔據最大的市場佔有率。這些平台之所以佔據主導地位，是因為它們能夠將人工智慧演算法、方便用戶使用的介面和全面的膳食指導整合到一個解決方案中。與醫療保健提供者的夥伴關係以及與醫療記錄的整合進一步增強了其應用。此外，持續的軟體更新和用於膳食追蹤、營養評分和個人化建議的模組化附加元件提高了用戶留存率。其能夠滿足個人消費者、企業和保險公司等不同需求的靈活性確保了其長期的市場佔有率，使軟體/平台領域成為全球首選的個人化營養解決方案。

預計在預測期內，電腦視覺將以最高的複合年成長率成長。

預計在預測期內，電腦視覺領域將實現最高成長率。電腦視覺的普及應用主要得益於其便捷的即時膳食分析功能以及與行動應用和健康平台的整合。人工智慧影像識別、擴增實境技術的創新以及不斷擴展的食品資料庫將加速其在消費者、臨床和企業健康應用領域的普及。此外，智慧型手機普及率的提高和穿戴式裝置的廣泛應用也推動了其廣泛部署。這些因素共同促進了市場的快速成長，使電腦視覺成為全球人工智慧個人化營養平台領域成長最快的技術細分市場。

比最大的地區

在預測期內，北美預計將佔據最大的市場佔有率，這主要得益於其較高的健康意識、數位健康技術的廣泛應用以及對人工智慧醫療解決方案的大力投資。監管支援、完善的遠距遠端醫療基礎設施以及平台提供者、保險公司和健康計劃之間的夥伴關係將推動人工智慧醫療解決方案的普及。此外，較高的可支配收入和先進消費技術的廣泛應用也將促進個人化營養工具的早期應用。這些因素共同作用，將使北美保持最大的市場佔有率，並鞏固其作為人工智慧個人化營養平台主要收入來源的地位。

複合年成長率最高的地區：

預計亞太地區在預測期內將實現最高的複合年成長率。快速的都市化、日益增強的健康意識以及不斷成長的可支配收入正在推動亞太地區對人工智慧個人化營養解決方案的需求。各國政府和相關人員正在投資建設數位醫療基礎設施，智慧型手機和穿戴式裝置的廣泛應用也為可擴展平台的部署提供了支援。此外，提供在地化內容、價格親民且符合區域飲食偏好的新創新興企業也在推動市場成長。所有這些因素共同推動了高普及率，使亞太地區在預測期內成為人工智慧個人化營養平台成長最快的地區。

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目錄

第1章執行摘要

第2章 引言

  • 概述
  • 相關利益者
  • 分析範圍
  • 分析方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 分析方法
  • 分析材料
    • 原始研究資料
    • 二手研究資訊來源
    • 先決條件

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 市場機遇
  • 威脅
  • 技術分析
  • 應用分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代產品的威脅
  • 新參與企業的威脅
  • 公司間的競爭

5. 全球人工智慧個人化營養平台市場（按組件分類）

  • 軟體/平台
  • 服務

6. 全球人工智慧個人化營養平台市場（依技術分類）

  • 機器學習/深度學習
  • 自然語言處理（NLP）
  • 電腦視覺
  • 數據分析

7. 全球人工智慧個人化營養平台市場（按應用分類）

  • 疾病管理
  • 體重管理
  • 運動營養，積極生活方式
  • 一般健康與保健

8. 全球人工智慧個人化營養平台市場（依最終用戶分類）

  • 醫療保健提供者和專業人員
  • 健康與健身中心
  • 公司組織
  • 研究所

9. 全球人工智慧個人化營養平台市場（依經營模式）

  • B2C（企業對消費者）
    • 訂閱式（SaaS）
    • 單次購買
  • B2B（企業對企業）
    • 授權
    • 白牌解決方案

第10章 全球人工智慧個人化營養平台市場（按地區分類）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 亞太其他地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第11章：主要趨勢

  • 合約、商業夥伴關係和合資企業
  • 企業合併（M&A）
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第12章：公司簡介

  • Viome
  • ZOE
  • InsideTracker
  • NutriSense
  • Levels Health
  • EatLove
  • Suggestic
  • Foodsmart
  • Baze
  • Habit
  • DNAFit
  • Nutrigenomix
  • Fay
  • GenoPalate
  • Noom, Inc.
  • Medtronic plc
Product Code: SMRC31912

According to Stratistics MRC, the Global AI-Personalized Nutrition Platforms Market is accounted for $1.4 billion in 2025 and is expected to reach $7.6 billion by 2032 growing at a CAGR of 27.5% during the forecast period. AI-personalized nutrition platforms are digital platforms using AI to deliver customized dietary and supplement advice. By analyzing individual data like DNA, gut microbiome, and lifestyle, they create tailored nutrition plans. This moves beyond one-size-fits-all diets to hyper-personalized wellness. The market is growing as consumers seek scientifically-backed, individualized health solutions. Companies leverage this technology to offer subscription services, personalized meal kits, and targeted supplement recommendations, driving engagement and better health outcomes.

According to the American Society for Nutrition, AI-powered nutrition platforms generated personalized diet recommendations for more than 12 million users in 2023, improving health outcomes in clinical studies.

Market Dynamics:

Driver:

Rising prevalence of chronic diseases and preventive healthcare focus

The increasing incidence of lifestyle-related chronic diseases such as obesity, diabetes, and cardiovascular disorders is fueling demand for AI-personalized nutrition platforms. Consumers and healthcare providers are focusing on preventive strategies, leveraging AI-driven insights to optimize diet plans based on individual health metrics, genetics, and lifestyle patterns. Moreover, wearable devices and health apps generate real-time data, enabling personalized nutrition recommendations. This shift toward proactive wellness and precision nutrition is creating sustained growth opportunities for platform developers and healthcare integrators globally.

Restraint:

Limited scientific validation for some AI recommendations

Despite growing adoption, certain AI-personalized nutrition solutions face skepticism due to limited clinical validation and lack of universally accepted dietary standards. Inaccurate or non-evidence-based recommendations can reduce user trust, hinder adoption, and potentially lead to adverse health outcomes. Additionally, discrepancies in algorithms across platforms and lack of longitudinal studies may constrain integration with healthcare systems. Vendors must invest in research collaborations, clinical trials, and regulatory compliance to strengthen credibility and encourage wider market penetration.

Opportunity:

Expansion into corporate wellness and insurance programs

Companies are increasingly incorporating AI-driven nutrition platforms into employee wellness initiatives and health insurance programs to improve workforce health, reduce absenteeism, and lower healthcare costs. Integration with wearable devices and personalized health monitoring enables scalable preventive interventions, enhancing employee engagement. Moreover, insurers are exploring AI insights to design tailored plans and incentives, driving B2B adoption. This corporate expansion offers recurring revenue opportunities for platform providers while reinforcing long-term market growth, especially as organizations emphasize holistic well-being and data-driven health solutions.

Threat:

Data security risks and potential algorithm biases

AI-personalized nutrition platforms collect sensitive health and lifestyle data, exposing users and providers to privacy breaches and regulatory scrutiny. Inadequate encryption, poor data governance, and third-party vulnerabilities can erode trust and lead to financial penalties. Additionally, algorithmic biases due to limited or skewed datasets may deliver inaccurate recommendations, reducing efficacy and credibility. To mitigate these threats, developers must implement robust cybersecurity measures, transparent AI models, and continuous auditing to ensure data integrity, ethical use, and equitable outcomes.

Covid-19 Impact:

The pandemic accelerated adoption of AI-personalized nutrition platforms as consumers sought remote, tailored health guidance while accessing gyms and clinics was restricted. Lockdowns highlighted the importance of preventive health and immunity, driving engagement with digital tools and telehealth integration. Platforms experienced rapid user growth, expansion in app features, and increased investment from healthcare providers and insurers. This period reinforced the long-term relevance of AI-driven nutrition solutions, prompting sustained adoption and innovation while fostering consumer trust in digital health technologies.

The software/platforms segment is expected to be the largest during the forecast period

The software/platforms segment is expected to account for the largest market share during the forecast period. These platforms dominate due to their ability to combine AI algorithms, user-friendly interfaces, and comprehensive dietary guidance in a single solution. Partnerships with healthcare providers and integration with medical records further strengthen adoption. Additionally, continuous software updates and modular add-ons for meal tracking, nutrition scoring, and personalized recommendations enhance user retention. Their versatility across individual consumers, corporates, and insurers solidifies long-term market share, making the software/platforms segment the preferred choice for personalized nutrition solutions worldwide.

The computer vision segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the computer vision segment is predicted to witness the highest growth rate. Adoption of computer vision is fueled by the convenience of instant dietary analysis and integration with mobile apps and health platforms. Innovations in AI image recognition, augmented reality features, and database expansion for food items accelerate adoption across consumer, clinical, and corporate wellness applications. Additionally, growing smartphone penetration and wearable device usage enable widespread deployment. These factors collectively contribute to rapid market growth, positioning computer vision as the fastest-expanding technology segment in AI-personalized nutrition platforms globally.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share due to high health awareness, widespread adoption of digital health technologies, and strong investment in AI healthcare solutions. Regulatory support, well-established telehealth infrastructure, and partnerships between platform providers, insurers, and wellness programs drive adoption. Moreover, high disposable incomes and advanced consumer tech penetration allow for early uptake of personalized nutrition tools. These factors collectively contribute to North America maintaining the largest market share, solidifying its position as a key revenue hub for AI-personalized nutrition platforms.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. Rapid urbanization, increasing health consciousness, and rising disposable incomes fuel demand for AI-personalized nutrition solutions in Asia Pacific. Governments and private stakeholders are investing in digital healthcare infrastructure, while smartphone and wearable device adoption support scalable platform deployment. Furthermore, localized content, affordable pricing models, and emerging startups catering to regional dietary preferences accelerate market growth. Collectively, these factors drive high adoption rates, positioning Asia Pacific as the fastest-growing region for AI-personalized nutrition platforms during the forecast period.

Key players in the market

Some of the key players in AI-Personalized Nutrition Platforms Market include Viome, ZOE, InsideTracker, NutriSense, Levels Health, EatLove, Suggestic, Foodsmart, Baze, Habit, DNAFit, Nutrigenomix, Fay, GenoPalate, Noom, Inc., and Medtronic plc.

Key Developments:

In July 2025, Viome, a life sciences startup founded by veteran tech entrepreneur Naveen Jain, announced collaboration with Microsoft to scale its molecular analysis platform - part of what Viome describes as a new era of AI-powered preventive health and wellness. Viome says Microsoft's cloud and AI infrastructure specially tuned for its purposes in conjunction with the tech giant will allow it to process biological data more efficiently. The idea is to expand access, reduce costs, and accelerate data processing and diagnostics.

In April 2025, InsideTracker, a leader in data-driven health technology, is pleased to introduce Terra, a first-of-its-kind virtual coach that enables its members to dive deep into their own body. Terra builds on the success of Ask InsideTracker, a native AI tool released last year and now one of the platform's most popular features. With this major version update, Terra becomes a personalized health coach with the ability to access information and offer recommendations typically limited to high-end concierge medicine.

Components Covered:

  • Software/Platforms
  • Services

Technologies Covered:

  • Machine Learning & Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Data Analytics

Applications Covered:

  • Disease Management
  • Weight Management
  • Sports Nutrition & Active Lifestyle
  • General Health & Wellness

End Users Covered:

  • Healthcare Providers & Professionals
  • Wellness & Fitness Centers
  • Corporate Organizations
  • Research Institutions

Business Models Covered:

  • Business-to-Consumer (B2C)
  • Business-to-Business (B2B)

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & 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 2024, 2025, 2026, 2028, and 2032
  • 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

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI-Personalized Nutrition Platforms Market, By Component

  • 5.1 Introduction
  • 5.2 Software/Platforms
  • 5.3 Services

6 Global AI-Personalized Nutrition Platforms Market, By Technology

  • 6.1 Introduction
  • 6.2 Machine Learning & Deep Learning
  • 6.3 Natural Language Processing (NLP)
  • 6.4 Computer Vision
  • 6.5 Data Analytics

7 Global AI-Personalized Nutrition Platforms Market, By Application

  • 7.1 Introduction
  • 7.2 Disease Management
  • 7.3 Weight Management
  • 7.4 Sports Nutrition & Active Lifestyle
  • 7.5 General Health & Wellness

8 Global AI-Personalized Nutrition Platforms Market, By End User

  • 8.1 Introduction
  • 8.2 Healthcare Providers & Professionals
  • 8.3 Wellness & Fitness Centers
  • 8.4 Corporate Organizations
  • 8.5 Research Institutions

9 Global AI-Personalized Nutrition Platforms Market, By Business Model

  • 9.1 Introduction
  • 9.2 Business-to-Consumer (B2C)
    • 9.2.1 Subscription-based (SaaS)
    • 9.2.2 One-time Purchase
  • 9.3 Business-to-Business (B2B)
    • 9.3.1 Licensing
    • 9.3.2 White-label Solutions

10 Global AI-Personalized Nutrition Platforms Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 Viome
  • 12.2 ZOE
  • 12.3 InsideTracker
  • 12.4 NutriSense
  • 12.5 Levels Health
  • 12.6 EatLove
  • 12.7 Suggestic
  • 12.8 Foodsmart
  • 12.9 Baze
  • 12.10 Habit
  • 12.11 DNAFit
  • 12.12 Nutrigenomix
  • 12.13 Fay
  • 12.14 GenoPalate
  • 12.15 Noom, Inc.
  • 12.16 Medtronic plc

List of Tables

  • Table 1 Global AI-Personalized Nutrition Platforms Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI-Personalized Nutrition Platforms Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI-Personalized Nutrition Platforms Market Outlook, By Software/Platforms (2024-2032) ($MN)
  • Table 4 Global AI-Personalized Nutrition Platforms Market Outlook, By Services (2024-2032) ($MN)
  • Table 5 Global AI-Personalized Nutrition Platforms Market Outlook, By Technology (2024-2032) ($MN)
  • Table 6 Global AI-Personalized Nutrition Platforms Market Outlook, By Machine Learning & Deep Learning (2024-2032) ($MN)
  • Table 7 Global AI-Personalized Nutrition Platforms Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 8 Global AI-Personalized Nutrition Platforms Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 9 Global AI-Personalized Nutrition Platforms Market Outlook, By Data Analytics (2024-2032) ($MN)
  • Table 10 Global AI-Personalized Nutrition Platforms Market Outlook, By Application (2024-2032) ($MN)
  • Table 11 Global AI-Personalized Nutrition Platforms Market Outlook, By Disease Management (2024-2032) ($MN)
  • Table 12 Global AI-Personalized Nutrition Platforms Market Outlook, By Weight Management (2024-2032) ($MN)
  • Table 13 Global AI-Personalized Nutrition Platforms Market Outlook, By Sports Nutrition & Active Lifestyle (2024-2032) ($MN)
  • Table 14 Global AI-Personalized Nutrition Platforms Market Outlook, By General Health & Wellness (2024-2032) ($MN)
  • Table 15 Global AI-Personalized Nutrition Platforms Market Outlook, By End User (2024-2032) ($MN)
  • Table 16 Global AI-Personalized Nutrition Platforms Market Outlook, By Healthcare Providers & Professionals (2024-2032) ($MN)
  • Table 17 Global AI-Personalized Nutrition Platforms Market Outlook, By Wellness & Fitness Centers (2024-2032) ($MN)
  • Table 18 Global AI-Personalized Nutrition Platforms Market Outlook, By Corporate Organizations (2024-2032) ($MN)
  • Table 19 Global AI-Personalized Nutrition Platforms Market Outlook, By Research Institutions (2024-2032) ($MN)
  • Table 20 Global AI-Personalized Nutrition Platforms Market Outlook, By Business Model (2024-2032) ($MN)
  • Table 21 Global AI-Personalized Nutrition Platforms Market Outlook, By Business-to-Consumer (B2C) (2024-2032) ($MN)
  • Table 22 Global AI-Personalized Nutrition Platforms Market Outlook, By Subscription-based (SaaS) (2024-2032) ($MN)
  • Table 23 Global AI-Personalized Nutrition Platforms Market Outlook, By One-time Purchase (2024-2032) ($MN)
  • Table 24 Global AI-Personalized Nutrition Platforms Market Outlook, By Business-to-Business (B2B) (2024-2032) ($MN)
  • Table 25 Global AI-Personalized Nutrition Platforms Market Outlook, By Licensing (2024-2032) ($MN)
  • Table 26 Global AI-Personalized Nutrition Platforms Market Outlook, By White-label Solutions (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.