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市场调查报告书
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1696246

全球个人化营养人工智慧市场 - 2025 至 2032 年

Global AI in Personalized Nutrition Market - 2025-2032
出版日期: | 出版商: DataM Intelligence | 英文 180 Pages | 商品交期: 最快1-2个工作天内

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简介目录

2024 年全球个人化营养人工智慧市场规模达到 11.2 亿美元，预计到 2032 年将达到 42.6 亿美元，2025-2032 年预测期内的复合年增长率为 18.19%。

人工智慧 (AI) 正在透过先进的资料分析提供客製化的饮食建议，从而改变个人化营养市场。营养领域的人工智慧应用包括智慧和个人化营养、饮食评估、食物识别和追踪、疾病预防的预测模型以及疾病诊断和监测。例如，已经开发出基于人工智慧的智慧型手机应用程式（如PROTEIN应用程式），以提供个人化的营养和健康生活指导，反映用户的观点和行为变化。

此外，人工智慧还可以促进各种健康指标的自我监测，包括血糖水平、体重、心率、脂肪百分比、血压、活动追踪和卡路里摄取量。这项技术进步提高了饮食监测的准确性，促进了更有效的个人化营养策略。

全球个人化营养人工智慧市场动态

驱动因素 - 基于人工智慧的微生物组分析，实现超个性化饮食

人工智慧（AI）驱动的微生物组分析透过根据个人肠道菌群组成客製化营养建议，显着推动了超个人化饮食的发展。在一项多中心随机对照试验中，人工智慧辅助个人化饮食显示 88% 的参与者便秘生活品质评估 (PAC-QoL) 评分提高了 50% 以上，而对照组这一比例仅为 40% (p = 0.001)。此外，个人化营养干预显示有益的粪桿菌属数量显着增加（p = 0.04），凸显了人工智慧驱动的饮食定制的有效性。

约束条件——人工智慧驱动的饮食建议中的伦理问题

资料隐私、演算法偏见和缺乏监管监督等道德问题正在限制个性化营养中采用人工智慧驱动的饮食建议。一项研究发现，62% 的消费者担心他们的健康资料如何被用在人工智慧驱动的营养平台中，影响信任和采用率。此外，人工智慧模型中的偏见可能会导致不准确或潜在有害的饮食建议，特别是对于代表性不足的人群，从而限制了人工智慧解决方案的有效性。

目录

第 1 章：方法与范围

第 2 章：定义与概述

第 3 章：执行摘要

第 4 章：动态

  • 影响因素
    • 驱动程式
      • 人工智慧微生物组分析协助超个人化饮食
    • 限制
      • 人工智慧驱动的饮食建议中的伦理问题
    • 机会
    • 影响分析

第五章：产业分析

  • 波特五力分析
  • 供应链分析
  • 价值链分析
  • 定价分析
  • 监理与合规性分析
  • 人工智慧与自动化影响分析
  • 研发与创新分析
  • 技术分析
  • DMI 意见

第 6 章：按技术

  • 人工智慧和机器学习
  • 自然语言处理 (NLP)
  • 电脑视觉
  • 预测分析
  • 深度学习
  • 其他的

第 7 章：按部署模式

  • 基于云端的 AI 解决方案
  • 本地 AI 解决方案

第 8 章：按最终用户

  • 健身爱好者
  • 健身健康中心
  • 医疗保健提供者
  • 其他的

第九章：按应用

  • 膳食计划和建议
  • 营养分析
  • 个性化补充
  • 过敏原和敏感性检测
  • 健康监测
  • 其他的

第 10 章：按地区

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 法国
    • 义大利
    • 俄罗斯
    • 欧洲其他地区
  • 南美洲
    • 巴西
    • 阿根廷
    • 南美洲其他地区
  • 亚太
    • 中国
    • 印度
    • 日本
    • 澳洲
    • 亚太其他地区
  • 中东和非洲

第 11 章：竞争格局

  • 竞争格局
  • 市场定位/份额分析
  • 併购分析

第 12 章：公司简介

  • Nestle SA
    • 公司概况
    • 产品组合和描述
    • 财务概览
    • 关键进展
  • EatLove, Inc.
  • Season Health, Inc.
  • Hungryroot, Inc.
  • Nutrium, Lda.
  • DNAfit Life Sciences Ltd.
  • Nutrigenomix Inc.
  • Instacart
  • Weight Watchers International, Inc.
  • Daily Harvest, Inc.

第 13 章：附录

简介目录
Product Code: FB9410

Global AI in personalized nutrition Market size reached US$ 1.12 billion in 2024 and is expected to reach US$ 4.26 billion by 2032, growing with a CAGR of 18.19% during the forecast period 2025-2032.

Artificial Intelligence (AI) is transforming the personalized nutrition market by enabling tailored dietary recommendations through advanced data analysis. AI applications in nutrition encompass smart and personalized nutrition, dietary assessment, food recognition and tracking, predictive modeling for disease prevention, and disease diagnosis and monitoring. For instance, AI-based smartphone applications like the PROTEIN app have been developed to provide personalized nutrition and healthy living guidance, reflecting users' perspectives and behavior changes.

Moreover, AI facilitates the self-monitoring of various health metrics, including blood glucose levels, body weight, heart rate, fat percentage, blood pressure, activity tracking, and calorie intake. This technological advancement enhances the accuracy of dietary monitoring, facilitating more effective personalized nutrition strategies.

Global AI in Personalized Nutrition Market Dynamics

Driver - AI-Powered Microbiome Analysis for Hyper-Personalized Diets

Artificial intelligence (AI)-powered microbiome analysis is significantly advancing hyper-personalized diets by tailoring nutritional recommendations based on individual gut flora composition. In a multicenter randomized controlled trial, an AI-assisted personalized diet demonstrated a more than 50% improvement in Patient Assessment of Constipation Quality of Life (PAC-QoL) scores for 88% of participants, compared to 40% in the control group (p = 0.001). Additionally, personalized nutrition interventions have shown a statistically significant rise in the beneficial Faecalibacterium genus (p = 0.04), highlighting the efficacy of AI-driven dietary customization.

Restraint - Ethical Concerns in AI-Driven Dietary Recommendations

Ethical concerns, including data privacy, algorithmic biases, and lack of regulatory oversight, are restraining the adoption of AI-driven dietary recommendations in personalized nutrition. A study found that 62% of consumers worry about how their health data is used in AI-driven nutrition platforms, impacting trust and adoption rates. Additionally, biases in AI models can lead to inaccurate or potentially harmful dietary suggestions, particularly for underrepresented populations, limiting the effectiveness of AI-powered solutions.

Segment Analysis

The global AI in personalized nutrition market is segmented based on technology, deployment mode, end-user, application, and region.

AI-Powered Personalized Nutrition is Gaining Traction in the Market

Artificial Intelligence (AI) and Machine Learning (ML) technologies are significantly advancing the personalized nutrition market by enabling precise dietary assessments and tailored recommendations. Advanced ML algorithms can analyze photographs of meals, providing instant, objective evaluations of portion sizes and nutrient content, thereby reducing biases inherent in traditional self-reported methods. This technological advancement enhances the accuracy of dietary monitoring, facilitating more effective personalized nutrition strategies.

In October 2023, AHARA, a leader in precision nutrition and the only evidence-based, food-first nutrition plan, has launched a free version of its leading personalized nutrition plan, empowering all individuals to take control of their health. This initiative reinforces Ahara's commitment to making customized precision nutrition preventative health plans accessible to individuals and empowering them to improve their health through a personalized food-first approach.

The Ahara Basic free plan offers users an opportunity to harness AHARA's data-driven health insights without any financial barrier. With the Basic Plan, users can access a scientifically based questionnaire that delivers personalized information on the key nutrients their body needs and a practical way to achieve their nutrition goals without an in-person doctor visit or the large price tag attached.

AI in Personalized Nutrition Market Regional Analysis

Rapid Technological Advancements in North America.

Artificial Intelligence (AI) is revolutionizing personalized nutrition in North America by enabling tailored dietary recommendations through advanced data analysis. The integration of AI with digital devices facilitates real-time, multi-type data collection, enhancing the precision of nutrition care. This technological advancement allows for the development of sophisticated applications in medicine and nutrition, improving the quality and safety of nutrition support care.

Moreover, AI-powered analysis of consumer data can identify trends and predict market demands, enabling food companies to tailor their marketing campaigns to specific demographics and promote products more effectively. This capability is particularly significant in North America, where consumer preferences are diverse and rapidly evolving.

Viocare's flagship product is VioScreen, a web-based dietary assessment tool that uses a graphical food frequency questionnaire (FFQ) to collect and analyze data on food intake and nutrient consumption. VioScreen is used by leading health and nutrition researchers, such as the National Institutes of Health (NIH), top universities, and healthcare organizations. VioScreen leverages AI and machine learning to provide accurate and personalized dietary feedback and recommendations based on scientific evidence. Viocare also offers custom solutions for nutrition-based research, clinical, or wellness programs. As of 2022, Viocare has raised $2.5 million in funding from angel investors and grants. The company has not exited or been acquired yet.

Technology Analysis

Artificial Intelligence (AI) is revolutionizing personalized nutrition by enabling precise dietary assessments and tailored recommendations. Advanced machine learning algorithms can analyze photographs of meals, providing instant, objective evaluations of portion sizes and nutrient content, thereby reducing biases inherent in traditional self-reported methods. This technological advancement enhances the accuracy of dietary monitoring, facilitating more effective personalized nutrition strategies.

Moreover, AI applications extend to predictive modeling for disease prevention, integrating individual dietary patterns, health metrics, and genetic information to tailor dietary advice. These applications aim to enhance adherence to dietary guidelines and improve overall nutritional outcomes. This integration of AI into personalized nutrition signifies a shift towards more individualized and effective dietary interventions, potentially transforming public health nutrition strategies.

Competitive Landscape

The major global players in the market include Nestle S.A., EatLove, Inc., Season Health, Inc., Hungryroot, Inc., Nutrium, Lda., DNAfit Life Sciences Ltd., Nutrigenomix Inc., Instacart, Weight Watchers International, Inc., and Daily Harvest, Inc.

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Target Audience 2024

  • Manufacturers/ Buyers
  • Industry Investors/Investment Bankers
  • Research Professionals
  • Emerging Companies

Table of Contents

1. Methodology and Scope

  • 1.1. Research Methodology
  • 1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

  • 3.1. Snippet by Technology
  • 3.2. Snippet by Deployment Mode
  • 3.3. Snippet by End-User
  • 3.4. Snippet by Application
  • 3.5. Snippet by Region

4. Dynamics

  • 4.1. Impacting Factors
    • 4.1.1. Drivers
      • 4.1.1.1. AI-Powered Microbiome Analysis for Hyper-Personalized Diets
    • 4.1.2. Restraints
      • 4.1.2.1. Ethical Concerns in AI-Driven Dietary Recommendations
    • 4.1.3. Opportunity
    • 4.1.4. Impact Analysis

5. Industry Analysis

  • 5.1. Porter's Five Force Analysis
  • 5.2. Supply Chain Analysis
  • 5.3. Value Chain Analysis
  • 5.4. Pricing Analysis
  • 5.5. Regulatory and Compliance Analysis
  • 5.6. AI & Automation Impact Analysis
  • 5.7. R&D and Innovation Analysis
  • 5.8. Technology Analysis
  • 5.9. DMI Opinion

6. By Technology

  • 6.1. Introduction
    • 6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 6.1.2. Market Attractiveness Index, By Technology
  • 6.2. AI and Machine Learning*
    • 6.2.1. Introduction
    • 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 6.3. Natural Language Processing (NLP)
  • 6.4. Computer Vision
  • 6.5. Predictive Analytics
  • 6.6. Deep Learning
  • 6.7. Others

7. By Deployment Mode

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
    • 7.1.2. Market Attractiveness Index, By Deployment Mode
  • 7.2. Cloud-Based AI Solutions*
    • 7.2.1. Introduction
    • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 7.3. On-Premise AI Solutions

8. By End-User

  • 8.1. Introduction
    • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 8.1.2. Market Attractiveness Index, By End-User
  • 8.2. Fitness Enthusiasts *
    • 8.2.1. Introduction
    • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 8.3. Fitness and Wellness Centers
  • 8.4. Healthcare Providers
  • 8.5. Others

9. By Application

  • 9.1. Introduction
    • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 9.1.2. Market Attractiveness Index, By Application
  • 9.2. Meal Planning and Recommendations*
    • 9.2.1. Introduction
    • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 9.3. Nutrient Analysis
  • 9.4. Personalized Supplementation
  • 9.5. Allergen and Sensitivity Detection
  • 9.6. Health Monitoring
  • 9.7. Others

10. By Region

  • 10.1. Introduction
    • 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
    • 10.1.2. Market Attractiveness Index, By Region
  • 10.2. North America
    • 10.2.1. Introduction
    • 10.2.2. Key Region-Specific Dynamics
    • 10.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 10.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
    • 10.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.2.7.1. U.S.
      • 10.2.7.2. Canada
      • 10.2.7.3. Mexico
  • 10.3. Europe
    • 10.3.1. Introduction
    • 10.3.2. Key Region-Specific Dynamics
    • 10.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 10.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
    • 10.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.3.7.1. Germany
      • 10.3.7.2. UK
      • 10.3.7.3. France
      • 10.3.7.4. Italy
      • 10.3.7.5. Russia
      • 10.3.7.6. Rest of Europe
  • 10.4. South America
    • 10.4.1. Introduction
    • 10.4.2. Key Region-Specific Dynamics
    • 10.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 10.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
    • 10.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.4.7.1. Brazil
      • 10.4.7.2. Argentina
      • 10.4.7.3. Rest of South America
  • 10.5. Asia-Pacific
    • 10.5.1. Introduction
    • 10.5.2. Key Region-Specific Dynamics
    • 10.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 10.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
    • 10.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.5.7.1. China
      • 10.5.7.2. India
      • 10.5.7.3. Japan
      • 10.5.7.4. Australia
      • 10.5.7.5. Rest of Asia-Pacific
  • 10.6. Middle East and Africa
    • 10.6.1. Introduction
    • 10.6.2. Key Region-Specific Dynamics
    • 10.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 10.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Mode
    • 10.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application

11. Competitive Landscape

  • 11.1. Competitive Scenario
  • 11.2. Market Positioning/Share Analysis
  • 11.3. Mergers and Acquisitions Analysis

12. Company Profiles

  • 12.1. Nestle S.A.*
    • 12.1.1. Company Overview
    • 12.1.2. Product Portfolio and Description
    • 12.1.3. Financial Overview
    • 12.1.4. Key Developments
  • 12.2. EatLove, Inc.
  • 12.3. Season Health, Inc.
  • 12.4. Hungryroot, Inc.
  • 12.5. Nutrium, Lda.
  • 12.6. DNAfit Life Sciences Ltd.
  • 12.7. Nutrigenomix Inc.
  • 12.8. Instacart
  • 12.9. Weight Watchers International, Inc.
  • 12.10. Daily Harvest, Inc.

LIST NOT EXHAUSTIVE

13. Appendix

  • 13.1. About Us and Services
  • 13.2. Contact Us