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個別化栄養におけるAIの世界市場：2025年～2032年

Global AI in Personalized Nutrition Market - 2025-2032

出版日
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英文 180 Pages
納期
即日から翌営業日
カスタマイズ可能
適宜更新あり
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個別化栄養におけるAIの世界市場：2025年～2032年
出版日: 2025年03月25日
発行: DataM Intelligence
ページ情報: 英文 180 Pages
納期: 即日から翌営業日
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  • 概要
  • 目次
概要

世界の個別化栄養におけるAIの市場規模は、2024年に11億2,000万米ドルに達し、2032年には42億6,000万米ドルに達すると予測され、予測期間の2025年～2032年のCAGRは18.19%となる見込みです。

人工知能（AI）は、高度なデータ分析を通じてオーダーメイドの食事提案を可能にすることで、個別化栄養市場を変革しています。栄養学におけるAIの用途には、スマートでパーソナライズされた栄養、食事評価、食品の認識と追跡、疾病予防のための予測モデリング、疾病診断とモニタリングが含まれます。例えば、PROTEINアプリのようなAIベースのスマートフォンアプリは、ユーザーの視点や行動の変化を反映し、パーソナライズされた栄養や健康的な生活ガイダンスを提供するために開発されています。

さらにAIは、血糖値、体重、心拍数、脂肪率、血圧、活動トラッキング、摂取カロリーなど、さまざまな健康指標の自己モニタリングを容易にします。この技術的進歩は食事モニタリングの精度を高め、より効果的な個別化栄養戦略を促進します。

人工知能（AI）を活用したマイクロバイオーム解析は、個々の腸内細菌叢組成に基づいて栄養勧告を調整することで、ハイパー・パーソナライズド・ダイエットを大きく前進させています。多施設共同無作為化比較試験において、AIを活用したパーソナライズド食事療法は、参加者の88％において、便秘患者評価QOL（PAC-QoL）スコアが50％以上改善したのに対し、対照群では40％でした（p=0.001）。さらに、個別化栄養介入は、有益なFaecalibacterium属の統計的に有意な増加を示しており（p=0.04）、AI主導の食事カスタマイズの有効性を強調しています。

データのプライバシー、アルゴリズムの偏り、規制監督の欠如などの倫理的懸念が、個別化栄養学におけるAI主導の食事推奨の採用を抑制しています。ある調査によると、消費者の62％がAI主導の栄養プラットフォームで自分の健康データがどのように使用されるかを心配しており、信頼と採用率に影響を与えています。さらに、AIモデルのバイアスは、特に不特定多数の人々にとって、不正確または潜在的に有害な食事提案につながる可能性があり、AIを活用したソリューションの有効性を制限しています。

当レポートでは、世界の個別化栄養におけるAI市場について調査し、市場の概要とともに、技術別、展開モード別、エンドユーザー別、用途別、地域別動向、競合情勢、および市場に参入する企業のプロファイルなどを提供しています。

目次

第1章 調査手法と範囲

第2章 定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

  • 影響要因
    • 促進要因
    • 抑制要因
    • 機会
    • 影響分析

第5章 業界分析

  • ポーターのファイブフォース分析
  • サプライチェーン分析
  • バリューチェーン分析
  • 価格分析
  • 規制およびコンプライアンス分析
  • AIと自動化の影響分析
  • 研究開発とイノベーション分析
  • 技術分析
  • DMIオピニオン

第6章 技術別

  • AIと機械学習
  • 自然言語処理（NLP）
  • コンピュータビジョン
  • 予測分析
  • ディープラーニング
  • その他

第7章 展開モード別

  • クラウドベースのAIソリューション
  • オンプレミスAIソリューション

第8章 エンドユーザー別

  • フィットネス愛好家
  • フィットネス・ウェルネスセンター
  • 医療機関
  • その他

第9章 用途別

  • 食事の計画と推奨事項
  • 栄養分析
  • 個別化サプリメント
  • アレルゲンと感受性の検出
  • 健康モニタリング
  • その他

第10章 地域別

  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • ロシア
    • その他
  • 南米
    • ブラジル
    • アルゼンチン
    • その他
  • アジア太平洋
    • 中国
    • インド
    • 日本
    • オーストラリア
    • その他
  • 中東・アフリカ

第11章 競合情勢

  • 競合シナリオ
  • 市場ポジショニング/シェア分析
  • 合併と買収の分析

第12章 企業プロファイル

  • Nestle SA
  • 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 付録

目次
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