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市場調查報告書
商品編碼
1696246

全球個人化營養人工智慧市場 - 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個工作天內

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簡介目錄

2024 年全球個人化營養人工智慧市場規模達到 11.2 億美元，預計到 2032 年將達到 42.6 億美元，2025-2032 年預測期內的複合年成長率為 18.19%。

人工智慧 (AI) 正在透過先進的資料分析提供客製化的飲食建議，從而改變個人化營養市場。營養領域的人工智慧應用包括智慧和個人化營養、飲食評估、食物識別和追蹤、疾病預防的預測模型以及疾病診斷和監測。例如，已經開發出基於人工智慧的智慧型手機應用程式（如PROTEIN應用程式），以提供個人化的營養和健康生活指導，反映用戶的觀點和行為變化。

此外，人工智慧還可以促進各種健康指標的自我監測，包括血糖水平、體重、心率、脂肪百分比、血壓、活動追蹤和卡路里攝取量。這項技術進步提高了飲食監測的準確性，促進了更有效的個人化營養策略。

全球個人化營養人工智慧市場動態

促進因素 - 基於人工智慧的微生物組分析，實現超個性化飲食

人工智慧（AI）驅動的微生物組分析透過根據個人腸道菌群組成客製化營養建議，顯著推動了超個人化飲食的發展。在一項多中心隨機對照試驗中，人工智慧輔助個人化飲食顯示 88% 的參與者便秘生活品質評估 (PAC-QoL) 評分提高了 50% 以上，而對照組這一比例僅為 40% (p = 0.001)。此外，個人化營養干預顯示有益的糞桿菌屬數量顯著增加（p = 0.04），凸顯了人工智慧驅動的飲食客製化的有效性。

約束條件——人工智慧驅動的飲食建議中的倫理問題

資料隱私、演算法偏見和缺乏監管監督等道德問題正在限制個性化營養中採用人工智慧驅動的飲食建議。一項研究發現，62% 的消費者擔心他們的健康資料如何被用在人工智慧驅動的營養平台中，影響信任和採用率。此外，人工智慧模型中的偏見可能會導致不準確或潛在有害的飲食建議，特別是對於代表性不足的人群，從而限制了人工智慧解決方案的有效性。

目錄

第 1 章：方法與範圍

第 2 章：定義與概述

第 3 章：執行摘要

第 4 章：動態

  • 影響因素
    • 驅動程式
      • 人工智慧微生物組分析協助超個人化飲食
    • 限制
      • 人工智慧驅動的飲食建議中的倫理問題
    • 機會
    • 影響分析

第5章：產業分析

  • 波特五力分析
  • 供應鏈分析
  • 價值鏈分析
  • 定價分析
  • 監理與合規性分析
  • 人工智慧與自動化影響分析
  • 研發與創新分析
  • 技術分析
  • DMI 意見

第 6 章：按技術

  • 人工智慧和機器學習
  • 自然語言處理 (NLP)
  • 電腦視覺
  • 預測分析
  • 深度學習
  • 其他

第 7 章：按部署模式

  • 基於雲端的 AI 解決方案
  • 本地 AI 解決方案

第 8 章：按最終用戶

  • 健身愛好者
  • 健身健康中心
  • 醫療保健提供者
  • 其他

第9章：按應用

  • 膳食計劃和建議
  • 營養分析
  • 個性化補充
  • 過敏原和敏感性檢測
  • 健康監測
  • 其他

第 10 章：按地區

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 法國
    • 義大利
    • 俄羅斯
    • 歐洲其他地區
  • 南美洲
    • 巴西
    • 阿根廷
    • 南美洲其他地區
  • 亞太
    • 中國
    • 印度
    • 日本
    • 澳洲
    • 亞太其他地區
  • 中東和非洲

第 11 章：競爭格局

  • 競爭格局
  • 市場定位/佔有率分析
  • 併購分析

第 12 章：公司簡介

  • Nestle SA
    • 公司概況
    • 產品組合和描述
    • 財務概覽
    • 關鍵進展
  • 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：附錄

簡介目錄
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