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市场调查报告书
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1857047

全球人工智慧个人化营养平台市场：未来预测（至2032年）－按组件、技术、应用、最终用户、经营模式和区域进行分析

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个工作天内

价格

根据 Stratistics MRC 的数据，全球 AI 个人化营养平台市场预计到 2025 年将达到 14 亿美元，到 2032 年将达到 76 亿美元，预测期内复合年增长率为 27.5%。

人工智慧个人化营养平台是利用人工智慧技术提供客製化营养补充建议的数位平台。它们透过分析DNA、肠道菌丛和生活方式等个人数据，制定专属的营养计划，从而摆脱千篇一律的饮食模式，走向高度个人化的健康管理。随着消费者寻求科学验证的个人化健康解决方案，该市场正在不断扩张。企业正利用这项技术提供订阅服务、个人化食材自煮包、针对性营养补充建议等，以提高用户参与度并改善健康状况。

据美国营养学会称，2023 年，人工智慧驱动的营养平台为超过 1,200 万用户产生了个人化的饮食建议，并在临床研究中改善了健康结果。

慢性病盛行率不断上升，以及对预防性照护的重视

肥胖、糖尿病和心血管疾病等生活方式相关慢性病的日益普遍，推动了对人工智慧个人化营养平台的需求。消费者和医疗保健提供者正着力于预防策略，利用人工智慧主导的洞察，根据个人的健康指标、基因和生活方式模式来优化饮食计画。此外，穿戴式装置和健康应用程式正在产生即时数据，从而实现个人化的营养建议。这种向积极主动的健康管理和精准营养的转变，为全球平台开发商和医疗保健整合商创造了持续成长的机会。

部分人工智慧建议缺乏科学检验

儘管人工智慧个人化营养解决方案的应用日益广泛，但由于临床检验有限且缺乏普遍认可的膳食标准，一些解决方案仍面临质疑。不准确或缺乏实证依据的建议会降低使用者信任度，阻碍应用推广，并可能导致不良健康后果。此外，不同平台间演算法的不一致性以及缺乏纵向研究也会限制其与医疗保健系统的整合。供应商必须增加对研究合作、临床试验和监管合规的投入，以增​​强信誉并加速市场渗透。

拓展至企业健康与保险项目

为了改善员工健康、降低缺勤率和减少医疗保健成本，企业正越来越多地将人工智慧主导的营养平台融入员工健康倡议和医疗保险方案中。透过与穿戴式装置和个人化健康监测的集成，可以实现可扩展的预防性干预措施，并提高员工参与度。此外，保险公司正在利用人工智慧洞察来设计客製化的计画和奖励，从而推动企业对企业（B2B）的应用。这种扩展不仅为平台提供者创造了持续的商机，也促进了市场的长期成长，尤其是在企业越来越重视整体社会福利和数据主导的健康解决方案的情况下。

资料安全风险和潜在演算法偏差

人工智慧个人化营养平台会收集敏感的健康和生活方式数据，使用户和供应商面临隐私外洩和监管审查的风险。加密不足、资料管治不善以及第三方漏洞都可能损害用户信任，并导致经济处罚。此外，由于资料集有限或存在偏差而导致的演算法偏差会提供不准确的建议，从而降低其有效性和可靠性。为了降低这些威胁，开发人员必须实施强有力的网路安全措施、透明的人工智慧模型和持续的审核，以确保资料完整性、合乎道德的使用以及公平的结果。

新冠疫情的影响：

疫情加速了人工智慧个人化营养平台的普及，因为在健身房和诊所受限的情况下，消费者寻求远端、个人化的健康指导。封锁措施凸显了预防性卫生和免疫力的重要性，推动了数位工具和远端医疗的整合。这些平台的用户数量迅速增长，应用程式功能不断扩展，医疗服务提供者和保险公司也增加了投资。这段时期巩固了人工智慧主导的营养解决方案的长期价值，鼓励了其持续应用和创新，同时也增强了消费者对数位健康技术的信任。

预计在预测期内，软体/平台板块将成为最大的板块。

预计在预测期内，软体/平台领域将占据最大的市场份额。这些平台之所以占据主导地位，是因为它们能够将人工智慧演算法、用户友好的介面和全面的膳食指导整合到一个解决方案中。与医疗保健提供者的伙伴关係以及与医疗记录的整合进一步增强了其应用。此外，持续的软体更新和用于膳食追踪、营养评分和个人化建议的模组化附加元件提高了用户留存率。其能够满足个人消费者、企业和保险公司等不同需求的灵活性确保了其长期的市场份额，使软体/平台领域成为全球首选的个人化营养解决方案。

预计在预测期内，电脑视觉将以最高的复合年增长率成长。

预计在预测期内，电脑视觉领域将实现最高成长率。电脑视觉的普及应用主要得益于其便捷的即时膳食分析功能以及与行动应用和健康平台的整合。人工智慧影像识别、扩增实境技术的创新以及不断扩展的食品资料库将加速其在消费者、临床和企业健康应用领域的普及。此外，智慧型手机普及率的提高和穿戴式装置的广泛应用也推动了其广泛部署。这些因素共同促进了市场的快速成长，使电脑视觉成为全球人工智慧个人化营养平台领域成长最快的技术细分市场。

比最大的地区

在预测期内，北美预计将占据最大的市场份额，这主要得益于其较高的健康意识、数位健康技术的广泛应用以及对人工智慧医疗解决方案的大力投资。监管支援、完善的远距远端医疗基础设施以及平台提供者、保险公司和健康计划之间的伙伴关係将推动人工智慧医疗解决方案的普及。此外，较高的可支配收入和先进消费技术的广泛应用也将促进个人化营养工具的早期应用。这些因素共同作用，将使北美保持最大的市场份额，并巩固其作为人工智慧个人化营养平台主要收入来源的地位。

复合年增长率最高的地区：

预计亚太地区在预测期内将实现最高的复合年增长率。快速的都市化、日益增强的健康意识以及不断增长的可支配收入正在推动亚太地区对人工智慧个人化营养解决方案的需求。各国政府和相关人员正在投资建设数位医疗基础设施，智慧型手机和穿戴式装置的广泛应用也为可扩展平台的部署提供了支援。此外，提供在地化内容、价格亲民且符合区域饮食偏好的新创新兴企业也在推动市场成长。所有这些因素共同推动了高普及率，使亚太地区在预测期内成为人工智慧个人化营养平台成长最快的地区。

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目录

第一章执行摘要

第二章 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 分析方法
  • 分析材料
    • 原始研究资料
    • 二手研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 技术分析
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 公司间的竞争

5. 全球人工智慧个人化营养平台市场（按组件划分）

  • 软体/平台
  • 服务

6. 全球人工智慧个人化营养平台市场（依技术划分）

  • 机器学习/深度学习
  • 自然语言处理（NLP）
  • 电脑视觉
  • 数据分析

7. 全球人工智慧个人化营养平台市场（按应用划分）

  • 疾病管理
  • 体重管理
  • 运动营养，积极生活方式
  • 一般健康与保健

8. 全球人工智慧个人化营养平台市场（依最终用户划分）

  • 医疗保健提供者和专业人员
  • 健康与健身中心
  • 公司组织
  • 研究所

9. 全球人工智慧个人化营养平台市场（依经营模式）

  • B2C（企业对消费者）
    • 订阅式（SaaS）
    • 单次购买
  • B2B（企业对企业）
    • 授权
    • 白牌解决方案

第十章 全球人工智慧个人化营养平台市场（按地区划分）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併（M&A）
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十二章：公司简介

  • 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.