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ヘルスケアにおける人工知能（AI）市場：製品別（ハードウェア、ソフトウェア、サービス）、技術別（機械学習、自然言語処理）、用途別（医療画像・診断、患者データ・リスク分析）、エンドユーザー別、地域別 - 2029年までの世界予測


Artificial Intelligence (AI) in Healthcare Market by Offering (Hardware, Software, Services), Technology (Machine Learning, Natural Language Processing), Application (Medical Imaging & Diagnostics, Patient Data & Risk Analysis), End User & Region - Global Forecast to 2029

ヘルスケアにおけるAI市場は、2024年の209億米ドルから成長し、2029年には1,484億米ドルに達すると予測され、2024年から2029年までの年平均成長率は48.1%と予測される。ヘルスケアにおけるAIへの強い注目 高齢者... もっと見る

　

　

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MarketsandMarkets
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シングルユーザライセンス
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注文方法はこちら 		360 346 英語

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サマリー

ヘルスケアにおけるAI市場は、2024年の209億米ドルから成長し、2029年には1,484億米ドルに達すると予測され、2024年から2029年までの年平均成長率は48.1%と予測される。ヘルスケアにおけるAIへの強い注目 高齢者ケアにおけるAIベースのツールの可能性の高まり、人間を意識したAIシステムの開発傾向の高まり、創薬、ゲノム、画像・診断におけるAI技術の加速が、ヘルスケアにおけるAI市場の成長を促進する。
"予測期間中、ソフトウェア市場が最大シェアを占める"
ソフトウェア分野は、AIプラットフォームとAIソリューションに分類される。ソフトウェアは、ヘルスケアにおけるAIの統合と機能性を推進する基盤となる要素である。AI頭脳の触媒として働き、自然言語処理や深層学習などの複雑な機械学習アルゴリズムの実装を可能にする。ソフトウェアが促進する効率的なデータ取り込みと管理に支えられたこれらのアルゴリズムは、AIシステムに広範な医療データセットを分析し、価値ある洞察を導き出す力を与える。実際の応用では、ソフトウェアは診断ツール、治療のパーソナライゼーション、バーチャルアシスタントにおいて極めて重要な役割を果たし、病気の発見、治療計画、患者エンゲージメントの精度を高める。さらに、ソフトウェアは、管理自動化と予測分析を通じて医療業務を最適化し、効率性の向上と積極的な患者ケアに貢献する。ヘルスケアにおけるAIのバックボーンとして、ソフトウェアは、患者ケアの強化、早期診断、個別化治療のための革新的なソリューションを提供することで、状況を一変させる。
"予測期間中、自然言語処理分野が第2位のシェアを占めると予測"
臨床および研究コミュニティは、電子健康報告書、臨床ノート、病理報告書を含む非構造化および半構造化テキスト文書の効率的な管理と開発のために、医療においてNLPを広く使用しています。このアルゴリズムは、ナラティブ・テキストの臨床文書から健康問題を抽出し、正確に解釈するために患者の電子問題リストに含めることを提案する。NLPは、文書前処理、健康問題検出、否定検出、文書後処理の4つのステップを含む。バビロンヘルス社（英国）は、チャットボットが対面診察で医師がするのと同じ質問をするためのアプリとNLPアルゴリズムを開発した。このアプリは正式な診断の概要を説明するのではなく、音声と言語処理を使って症状を抽出し、プロファイル情報を医師に転送する。NLPは、臨床データをより正確に構造化し解釈するために、医療機関から大きな需要があります。さらに、コネクテッドデバイスの利用が増加していることに加え、膨大な量の患者データがこの市場の成長を加速させている。
"予測期間中、患者データ＆リスク分析分野が主要市場シェアを占める"
ヘルスケアにおける機械学習（ML）と自然言語処理（NLP）の融合は、患者の健康に関する予測的洞察に大きな進歩をもたらす。多様なデータソースを活用するMLモデルは、医療記録、臨床検査、人口統計、社会的決定要因を分析し、特定の疾病リスクのある患者を特定し、NLPアルゴリズムは臨床記録から洞察を抽出し、疾病の初期兆候を発見する。この相乗効果により、治療効果やライフスタイルなどの要素を考慮した、パーソナライズされた治療計画が可能になる。MLは潜在的な増悪を予測し、積極的な介入を可能にし、NLPは遠隔モニタリングのためのリアルタイムデータを解釈する。その利点は、患者の転帰の改善、コストの削減、医療上の意思決定の強化などである。しかし、データのプライバシー、アルゴリズムの偏り、透明性の必要性などの課題は、医療における倫理的で責任あるAIの導入の重要性を強調している。

"予測期間中、北米が最大の市場シェアを占める見込み"
北米のヘルスケア分野では、異業種間の関与やベンチャーキャピタルからの投資の大幅な増加によって、人工知能（AI）ランドスケープへの新規参入者の流入が見られます。その一例として、AI主導のプライマリ・ケア・プラットフォームに特化した新興企業Navina（米国）は、2022年10月のシリーズB資金調達ラウンドで4,400万米ドルという多額の資金を確保した。これらの投資は、ナビナのAIと機械学習（ML）技術の進歩を推進する。もう一つの例は、AIベースの精密医療ソリューションに特化したTempus社（米国）で、同月にAres Management社やGoogle社を含む11の投資家から13億米ドルを調達している。
二次調査を通じて収集した様々なセグメントやサブセグメントの市場規模を決定・検証するため、ヘルスケアにおけるAI市場領域の主要な業界専門家に広範な一次インタビューを実施した。本レポートの主要参加者の内訳は以下の通りである：
ヘルスケア分野におけるAI市場の主要参入企業のプロファイルの内訳は以下の通りです：
- 企業タイプ別：ティア1：50％、ティア2：30％、ティア3：20
- 役職別Cレベル60%、ディレクターレベル30%、その他10
- 地域別北米：40％、欧州：20％、アジア太平洋地域：30％、ROW：10
本レポートでは、ヘルスケアにおけるAI市場の主要プレイヤーを、それぞれの市場ランキング分析とともに紹介しています。本レポートに掲載されている主要企業は、Koninklijke Philips N.V.（オランダ）、Microsoft（米国）、Siemens Healthineers AG（ドイツ）、Intel Corporation（米国）、NVIDIA Corporation（米国）、Google Inc.（米国）、GE HealthCare Technologies Inc.（米国）、Oracle（米国）、Johnson & Johnson Services, Inc.（米国）などである。
これ以外にも、Merative社（米国）、General Vision社（米国）、CloudMedx社（米国）、Oncora Medical社（米国）、Enlitic社（米国）、Lunit Inc、(韓国）、Qure.ai（インド）、Tempus（米国）、COTA（米国）、FDNA INC.（米国）、Recursion（米国）、Atomwise（米国）、Virgin Pulse（米国）、Babylon Health（英国）、MDLIVE（米国）、Stryker（米国）、Qventus（米国）、Sweetch（イスラエル）、Sirona Medical, Inc.
調査範囲この調査レポートは、ヘルスケアにおけるAI市場を提供、技術、用途、エンドユーザー、地域に基づいて分類しています。ヘルスケアにおけるAI市場に関連する主な促進要因、阻害要因、課題、機会について記載し、2029年まで同市場を予測します。これらとは別に、本レポートはヘルスケアにおけるAIエコシステムに含まれるすべての企業のリーダーシップマッピングと分析でも構成されています。
レポート購入の主な利点 本レポートは、ヘルスケアにおけるAI市場全体とサブセグメントに関する収益数の最も近い近似値に関する情報を提供することで、本市場における市場リーダー/新規参入者を支援します。本レポートは、利害関係者が競争状況を理解し、より多くの洞察を得ることで、自社のビジネスをより良く位置づけ、適切な市場参入戦略を計画するのに役立ちます。また、本レポートは、関係者が市場の鼓動を理解するのに役立ち、主要な市場促進要因、阻害要因、課題、および機会に関する情報を提供します。
本レポートは、以下のポイントに関する洞察を提供しています：
- ヘルスケアにおけるAI市場の成長に影響を与える主な促進要因の分析（大規模かつ複雑なヘルスケアデータセットの生成、医療費削減の必要性、コンピューティングパワーの向上とハードウェアコストの低下、ヘルスケア分野における異なる領域間のパートナーシップとコラボレーションの増加、医療従事者と患者の間の不均衡による改善されたヘルスケアサービスの必要性の高まり）。
- 製品開発/イノベーション：AIヘルスケア市場における今後の技術、研究開発活動、新製品・新サービスの発表に関する詳細な洞察。
- 市場開発：有利な市場に関する包括的な情報 - 当レポートでは、様々な地域におけるAI in Healthcare市場を分析しています。
- 市場の多様化：AIヘルスケア市場における新製品＆サービス、未開拓の地域、最近の開発、投資に関する詳細情報
- 競合評価：ヘルスケアにおけるAI市場におけるKoninklijke Philips N.V.（オランダ）、Microsoft（米国）、Siemens Healthineers AG（ドイツ）、Intel Corporation（米国）、NVIDIA Corporation（米国）などの主要企業の市場シェア、成長戦略、サービス内容を詳細に評価します。

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目次

1 INTRODUCTION 44
1.1 STUDY OBJECTIVES 44
1.2 MARKET DEFINITION 44
1.2.1 INCLUSIONS AND EXCLUSIONS 45
1.3 STUDY SCOPE 46
1.3.1 MARKETS COVERED 46
1.3.2 REGIONAL SCOPE 47
1.3.3 YEARS CONSIDERED 47
1.4 CURRENCY CONSIDERED 47
1.5 UNITS CONSIDERED 48
1.6 LIMITATIONS 48
1.7 STAKEHOLDERS 48
1.8 SUMMARY OF CHANGES 49
1.9 IMPACT OF RECESSION 50
FIGURE 1 GDP GROWTH PROJECTION DATA FOR MAJOR ECONOMIES, 2021–2023 50
1.10 GDP GROWTH PROJECTION UNTIL 2024 FOR MAJOR ECONOMIES 51
2 RESEARCH METHODOLOGY 52
2.1 RESEARCH DATA 52
FIGURE 2 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: RESEARCH DESIGN 52
2.1.1 SECONDARY DATA 53
2.1.1.1 List of major secondary sources 53
2.1.1.2 Key data from secondary sources 54
2.1.2 PRIMARY DATA 54
2.1.2.1 List of key interview participants 54
2.1.2.2 Key data from primary sources 55
2.1.2.3 Key industry insights 55
2.1.2.4 Breakdown of primaries 56
2.1.3 SECONDARY AND PRIMARY RESEARCH 56
2.2 MARKET SIZE ESTIMATION 57
FIGURE 3 RESEARCH FLOW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SIZE ESTIMATION 57
FIGURE 4 MARKET SIZE ESTIMATION METHODOLOGY (SUPPLY SIDE): REVENUE GENERATED BY COMPANIES FROM ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET 58
2.2.1 BOTTOM-UP APPROACH 58
2.2.1.1 Approach to estimate market size using bottom-up analysis (demand side) 58
FIGURE 5 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH 59
FIGURE 6 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH (DEMAND SIDE): REVENUE GENERATED FROM ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER 59
2.2.2 TOP-DOWN APPROACH 60
2.2.2.1 Approach to estimate market size using top-down analysis (supply side) 60
FIGURE 7 MARKET SIZE ESTIMATION METHODOLOGY: TOP-DOWN APPROACH 60
2.3 DATA TRIANGULATION 61
FIGURE 8 DATA TRIANGULATION 61
2.4 RESEARCH ASSUMPTIONS 62
2.5 RISK ASSESSMENT 62
2.6 PARAMETERS CONSIDERED TO ANALYZE RECESSION IMPACT ON STUDIED MARKET 63
2.7 RESEARCH LIMITATIONS 63
3 EXECUTIVE SUMMARY 64
FIGURE 9 SOFTWARE SEGMENT TO HOLD LARGEST MARKET SHARE IN 2029 64
FIGURE 10 MACHINE LEARNING SEGMENT TO DOMINATE MARKET DURING FORECAST PERIOD 65
FIGURE 11 PATIENTS SEGMENT TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD 66
FIGURE 12 MEDICAL IMAGING & DIAGNOSTICS SEGMENT TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD 67
FIGURE 13 NORTH AMERICA ACCOUNTED FOR LARGEST MARKET SHARE OF GLOBAL ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET IN 2023 68
4 PREMIUM INSIGHTS 69
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI IN HEALTHCARE MARKET 69
FIGURE 14 INCREASING ADOPTION OF AI-BASED TOOLS IN HEALTHCARE FACILITIES TO CREATE LUCRATIVE OPPORTUNITIES FOR MARKET PLAYERS 69
4.2 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING 69
FIGURE 15 SOFTWARE SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE IN 2024 69
4.3 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY 70
FIGURE 16 MACHINE LEARNING TECHNOLOGY TO COMMAND MARKET FROM 2023 TO 2029 70
4.4 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER 70
FIGURE 17 HOSPITALS & HEALTHCARE PROVIDERS SEGMENT TO LEAD MARKET THROUGHOUT FORECAST PERIOD 70
4.5 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION 71
FIGURE 18 MEDICAL IMAGING & DIAGNOSTICS SEGMENT TO REGISTER HIGHEST GROWTH DURING FORECAST PERIOD 71
4.6 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY COUNTRY 71
FIGURE 19 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET IN MEXICO TO GROW AT HIGHEST CAGR FROM 2024 TO 2029 71
5 MARKET OVERVIEW 72
5.1 INTRODUCTION 72
5.2 MARKET DYNAMICS 72
FIGURE 20 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES 72
5.2.1 DRIVERS 73
FIGURE 21 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: DRIVERS AND THEIR IMPACT 73
5.2.1.1 Exponential growth in data volume and complexity due to surging adoption of digital technologies 73
5.2.1.2 Significant cost pressure on healthcare service providers with increasing prevalence of chronic diseases 74
5.2.1.3 Rapid proliferation of AI in healthcare sector 74
5.2.1.4 Growing need for improvised healthcare services 75
5.2.2 RESTRAINTS 76
FIGURE 22 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: RESTRAINTS AND THEIR IMPACT 76
5.2.2.1 Reluctance among medical practitioners to adopt AI-based technologies 76
5.2.2.2 Shortage of skilled AI professionals handling AI-powered solutions 77
5.2.2.3 Lack of standardized frameworks for AL and ML technologies 77
5.2.3 OPPORTUNITIES 78
FIGURE 23 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: OPPORTUNITIES AND THEIR IMPACT 78
5.2.3.1 Increasing use of AI-powered solutions in elderly care 78
5.2.3.2 Increasing focus on developing human-aware AI systems 79
5.2.3.3 Rising use of technology in pharmaceuticals industry 79
5.2.3.4 Strategic partnerships and collaborations among healthcare companies and AI technology providers 80
5.2.4 CHALLENGES 82
FIGURE 24 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: CHALLENGES AND THEIR IMPACT 82
5.2.4.1 Inaccurate predictions due to scarcity of high-quality healthcare data 82
5.2.4.2 Concerns regarding data privacy 83
FIGURE 25 DATA BREACHES IN HEALTHCARE SECTOR, 2019–2023 83
5.2.4.3 Lack of interoperability between AI solutions offered by different vendors 84
FIGURE 26 CHALLENGES ASSOCIATED WITH HEALTHCARE DATA INTEROPERABILITY 84
5.3 TRENDS/DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESSES 85
FIGURE 27 TRENDS/DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESSES 85
5.4 PRICING ANALYSIS 85
5.4.1 AVERAGE SELLING PRICE (ASP) TREND OF COMPONENTS OFFERED BY KEY PLAYERS, 2020–2029 86
FIGURE 28 AVERAGE SELLING PRICE (ASP) OF PROCESSOR COMPONENTS OFFERED BY KEY PLAYERS 86
TABLE 1 AVERAGE SELLING PRICE (ASP) OF PROCESSOR COMPONENTS OFFERED BY KEY PLAYERS 86
5.4.2 AVERAGE SELLING PRICE (ASP) TREND OF PROCESSOR COMPONENTS, BY REGION, 2020–2029 87
FIGURE 29 AVERAGE SELLING PRICE (ASP) TREND OF PROCESSOR COMPONENTS, BY REGION, 2020–2029 87
5.5 VALUE CHAIN ANALYSIS 88
FIGURE 30 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: VALUE CHAIN ANALYSIS 88
5.6 ECOSYSTEM MAPPING 89
FIGURE 31 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: ECOSYSTEM MAPPING 89
TABLE 2 COMPANIES AND THEIR ROLES IN ARTIFICIAL INTELLIGENCE IN HEALTHCARE ECOSYSTEM 90
5.7 TECHNOLOGY ANALYSIS 91
5.7.1 CLOUD COMPUTING 91
5.7.2 CLOUD GPU 91
5.7.3 GENERATIVE AI 92
5.7.4 CLOUD-BASED PACS 92
5.7.5 MULTI-CLOUD 92
5.8 PATENT ANALYSIS 93
TABLE 3 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: INNOVATIONS AND PATENT REGISTRATIONS 93
FIGURE 32 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: PATENTS GRANTED, 2013–2023 97
FIGURE 33 TOP 10 PATENT OWNERS IN LAST 10 YEARS, 2013–2023 97
TABLE 4 TOP PATENT OWNERS IN ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET IN LAST 10 YEARS 97
5.9 TRADE ANALYSIS 98
FIGURE 34 IMPORT DATA FOR HS CODE 854231-COMPLIANT PRODUCTS, BY COUNTRY, 2018–2022 (USD MILLION) 98
FIGURE 35 EXPORT DATA FOR HS CODE 854231-COMPLIANT PRODUCTS, BY COUNTRY, 2018–2022 (USD MILLION) 99
5.10 KEY CONFERENCES AND EVENTS, 2024–2025 99
TABLE 5 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: LIST OF CONFERENCES AND EVENTS, 2024–2025 99
5.11 CASE STUDY ANALYSIS 102
5.11.1 BIOBEAT LAUNCHED HOME-BASED REMOTE PATIENT MONITORING KIT DURING PEAK WAVE OF COVID-19 102
5.11.2 MICROSOFT COLLABORATED WITH CLEVELAND CLINIC TO APPLY PREDICTIVE AND ADVANCED ANALYTICS TO IDENTIFY POTENTIAL AT-RISK PATIENTS UNDER ICU CARE 102
5.11.3 TGEN COLLABORATED WITH INTEL CORPORATION AND DELL TECHNOLOGIES TO ASSIST PHYSICIANS AND RESEARCHERS ACCELERATE DIAGNOSIS AND TREATMENT AT LOWER COST 103
5.11.4 INSILICO DEVELOPED ML-POWERED TOOLS FOR DRUG IDENTIFICATION AND CHEMISTRY42 FOR NOVEL COMPOUND DESIGN 103
5.11.5 GE HEALTHCARE IMPROVED PATIENT OUTCOMES BY REDUCING WORKFLOW PROCESSING TIME USING MEDICAL IMAGING DATA 104
5.12 TARIFFS, STANDARDS, AND REGULATORY LANDSCAPE 104
TABLE 6 MFN TARIFF FOR HS CODE 854231-COMPLIANT PRODUCTS EXPORTED BY US, 2022 104
TABLE 7 MFN TARIFF FOR HS CODE 854231-COMPLIANT PRODUCTS EXPORTED BY CHINA, 2022 105
TABLE 8 MFN TARIFF FOR HS CODE 854231-COMPLIANT PRODUCTS EXPORTED BY GERMANY, 2022 105
5.12.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 106
TABLE 9 NORTH AMERICA: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 106
TABLE 10 EUROPE: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 107
TABLE 11 ASIA PACIFIC: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 108
TABLE 12 ROW: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 109
5.12.2 STANDARDS 110
5.12.2.1 ISO 22399:2020 110
5.12.2.2 IEC 62366:2015 110
5.12.2.3 Health Insurance Portability and Accountability Act (HIPAA) 110
5.12.2.4 EU General Data Protection Regulation (GDPR) 110
5.12.2.5 Fast Healthcare Interoperability Resources (HL7 FHIR) 110
5.12.2.6 Medical Device Regulation 111
5.12.2.7 World Health Organization Artificial intelligence for Health Guide 111
5.12.2.8 Algorithmic Justice League framework for assessing AI in healthcare 111
5.12.3 GOVERNMENT REGULATIONS 111
5.12.3.1 US 111
5.12.3.2 Europe 111
5.12.3.3 China 112
5.12.3.4 Japan 112
5.12.3.5 India 112
5.13 PORTER’S FIVE FORCES ANALYSIS 112
TABLE 13 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: PORTER’S FIVE FORCES ANALYSIS 112
FIGURE 36 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: PORTER’S FIVE FORCES ANALYSIS 113
5.13.1 THREAT OF NEW ENTRANTS 113
5.13.2 THREAT OF SUBSTITUTES 114
5.13.3 BARGAINING POWER OF SUPPLIERS 114
5.13.4 BARGAINING POWER OF BUYERS 114
5.13.5 INTENSITY OF COMPETITIVE RIVALRY 114
5.14 KEY STAKEHOLDERS AND BUYING CRITERIA 115
5.14.1 KEY STAKEHOLDERS IN BUYING PROCESS 115
FIGURE 37 INFLUENCE OF KEY STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE END USERS 115
TABLE 14 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE END USERS 115
5.14.2 BUYING CRITERIA 115
FIGURE 38 KEY BUYING CRITERIA FOR TOP THREE END USERS 115
TABLE 15 KEY BUYING CRITERIA FOR TOP THREE END USERS 116
6 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING 117
6.1 INTRODUCTION 118
FIGURE 39 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING 118
FIGURE 40 SOFTWARE SEGMENT TO DOMINATE MARKET DURING FORECAST PERIOD 119
TABLE 16 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2020–2023 (USD MILLION) 119
TABLE 17 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2024–2029 (USD MILLION) 119
6.2 HARDWARE 120
TABLE 18 HARDWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 120
TABLE 19 HARDWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 120
TABLE 20 HARDWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 121
TABLE 21 HARDWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 121
6.2.1 PROCESSOR 121
6.2.1.1 Need for real-time processing of patient data to boost demand 121
TABLE 22 PROCESSOR: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (MILLION UNITS) 122
TABLE 23 PROCESSOR: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (MILLION UNITS) 123
TABLE 24 PROCESSOR: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 123
TABLE 25 PROCESSOR: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 123
6.2.1.2 MPUs/CPUs 124
TABLE 26 CASE STUDY: PHILIPS COLLABORATED WITH INTEL CORPORATION TO OPTIMIZE AI INFERENCING HEALTHCARE WORKLOADS ON INTEL XEON SCALABLE PROCESSORS USING OPENVINO TOOLKIT 124
6.2.1.3 GPUs 124
TABLE 27 CASE STUDY: DEEPPHARMA PLATFORM, OFFERED BY INSILICO, EQUIPPED WITH ADVANCED DEEP LEARNING TECHNIQUES, HELPS ANALYZE MULTI-OMICS DATA AND TISSUE-SPECIFIC PATHWAY ACTIVATION PROFILES 125
6.2.1.4 FPGAs 125
TABLE 28 CASE STUDY: INTEL CORPORATION, IN COLLABORATION WITH BROAD INSTITUTE, DEVELOPED BIGSTACK* 2.0 TO MEET EVOLVING DEMANDS OF GENOMICS RESEARCH 126
6.2.1.5 ASICs 126
6.2.2 MEMORY 127
6.2.2.1 Increasing demand for real-time medical image analysis and diagnosis support systems to drive market 127
TABLE 29 CASE STUDY: HUAWEI ASSISTED TOULOUSE UNIVERSITY HOSPITAL WITH OCEANSTOR ALL-FLASH SOLUTION THAT OFFERS LOW LATENCY AND SIMPLIFIED OPERATIONS AND MAINTENANCE MANAGEMENT 128
6.2.3 NETWORK 128
6.2.3.1 Growing need for remote patient monitoring and precision medicine to foster segmental growth 128
TABLE 30 NETWORK: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 129
TABLE 31 NETWORK: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 129
6.3 SOFTWARE 129
TABLE 32 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 130
TABLE 33 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 130
TABLE 34 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 131
TABLE 35 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 131
6.3.1 AI SOLUTION 131
6.3.1.1 Integration of non-procedural languages into AI solutions to accelerate segmental growth 131
TABLE 36 CASE STUDY: COGNIZANT LEVERAGED AZURE PLATFORM OF MICROSOFT AND DEVELOPED RESOLV, THAT EMPLOYS NATURAL LANGUAGE PROCESSING TO PROVIDE REAL-TIME RESPONSE TO ANALYTICAL QUERIES 132
TABLE 37 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR AI SOLUTIONS, BY DEPLOYMENT TYPE, 2020–2023 (USD MILLION) 132
TABLE 38 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR AI SOLUTIONS, BY DEPLOYMENT TYPE, 2024–2029 (USD MILLION) 133
6.3.1.2 On-premises 133
TABLE 39 CASE STUDY: GE HEALTHCARE ENHANCED ON-PREMISES CAPABILITY WITH SCYLLADB’S PROJECT ALTERNATOR 133
6.3.1.3 Cloud 134
TABLE 40 CASE STUDY: TAKEDA COLLABORATED WITH DELOITTE TO EMPLOY DEEP MINER TOOLKIT FOR RAPID DEVELOPMENT AND TESTING OF PREDICTIVE MODELS 134
6.3.2 AI PLATFORM 135
6.3.2.1 Increasing applications in development of toolkits for healthcare solutions to drive market 135
TABLE 41 CASE STUDY: CAYUGA MEDICAL CENTER SOUGHT SIMPLE CDI SOFTWARE SOLUTION TO IMPROVE WORKFLOWS AND REDUCE COSTS 135
TABLE 42 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR AI PLATFORMS, BY TYPE, 2020–2023 (USD MILLION) 136
TABLE 43 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR AI PLATFORMS, BY TYPE, 2024–2029 (USD MILLION) 136
6.3.2.2 Machine learning framework 136
6.3.2.3 Application program interface 137
6.4 SERVICES 137
TABLE 44 SERVICES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 137
TABLE 45 SERVICES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 138
TABLE 46 SERVICES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 138
TABLE 47 SERVICES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 138
6.4.1 DEPLOYMENT & INTEGRATION 139
6.4.1.1 Enhanced patient care along with streamlines workflows to drive demand 139
6.4.2 SUPPORT & MAINTENANCE 139
6.4.2.1 Need to evaluate performance and maintain operational stability to drive market 139
7 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY 140
7.1 INTRODUCTION 141
FIGURE 41 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY 141
FIGURE 42 MACHINE LEARNING TECHNOLOGY TO LEAD MARKET DURING FORECAST PERIOD 142
TABLE 48 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY, 2020–2023 (USD MILLION) 142
TABLE 49 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY, 2024–2029 (USD MILLION) 142
7.2 MACHINE LEARNING 143
TABLE 50 CASE STUDY: IN COLLABORATION WITH INTEL AND APOQLAR, THEBLUE.AI INTRODUCED BLUW.GDPR. EQUIPPED WITH ML ALGORITHMS ACCELERATED BY OPENVINO TOOLKIT 143
TABLE 51 MACHINE LEARNING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 144
TABLE 52 MACHINE LEARNING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 144
7.2.1 DEEP LEARNING 144
7.2.1.1 Rising applications in voice recognition, fraud detection, and recommendation engines to drive market 144
TABLE 53 WINNING HEALTH TECHNOLOGY INTRODUCED AI MEDICAL IMAGING SOLUTION BASED ON AMAX DEEP LEARNING ALL-IN-ONE TO REDUCE OVERALL MODEL INFERENCE TIME FROM OVER 0.5 HOURS TO LESS THAN 2 MINUTES FOR AI-AIDED DIAGNOSTIC IMAGING OF PULMONARY NODULES 146
7.2.2 SUPERVISED LEARNING 146
7.2.2.1 Contribution to clinical decision-making and enhancing personalized medications to boost demand 146
7.2.3 REINFORCEMENT LEARNING 147
7.2.3.1 Enhanced diagnostic accuracy in medical imaging analysis to fuel market growth 147
7.2.4 UNSUPERVISED LEARNING 147
7.2.4.1 Ability to uncover hidden patterns and handle unlabeled data challenges to boost demand 147
7.2.5 OTHERS 147
7.3 NATURAL LANGUAGE PROCESSING 148
TABLE 54 CASE STUDY: MARUTI TECHLABS ASSISTED UKHEALTH WITH ML MODEL FOR AUTOMATIC DATA EXTRACTION AND CLASSIFICATION 148
TABLE 55 NATURAL LANGUAGE PROCESSING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 149
TABLE 56 NATURAL LANGUAGE PROCESSING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 149
7.3.1 IVR 150
7.3.1.1 Enhanced operational efficiency and optimized clinical support to drive market 150
7.3.2 OCR 150
7.3.2.1 Reduced errors in data entry and streamlined administrative processes to spur demand 150
7.3.3 PATTERN AND IMAGE RECOGNITION 151
7.3.3.1 Optimized therapeutic outcomes and development of personal medication to foster segmental growth 151
7.3.4 AUTO CODING 152
7.3.4.1 Contribution to cost-saving and optimization of coding processes to drive market 152
7.3.5 CLASSIFICATION AND CATEGORIZATION 152
7.3.5.1 Accurate prediction of disease outcomes to boost demand 152
7.3.6 TEXT ANALYTICS 152
7.3.6.1 Significant contribution to drug discovery by examining extensive datasets of scientific literature to boost demand 152
7.3.7 SPEECH ANALYTICS 153
7.3.7.1 Contribution to sentiment analysis by assessing tone of patient conversations to boost demand 153
7.4 CONTEXT-AWARE COMPUTING 153
TABLE 57 CONTEXT-AWARE COMPUTING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 154
TABLE 58 CONTEXT-AWARE COMPUTING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 154
7.4.1 DEVICE CONTEXT 154
7.4.1.1 Ability to offer comprehensive view of patient data to boost demand 154
7.4.2 USER CONTEXT 155
7.4.2.1 Better predictive analysis for disease prevention to foster segmental growth 155
7.4.3 PHYSICAL CONTEXT 155
7.4.3.1 Ability to address individualized needs based on surrounding environment to boost market 155
7.5 COMPUTER VISION 155
7.5.1 ENHANCED PRECISION WITH 3D VISUALIZATIONS AND PERSONALIZED PROCEDURES TO FOSTER SEGMENTAL GROWTH 155
TABLE 59 CASE STUDY: PUNKTUM COLLABORATED WITH MAYO CLINIC TO DEVELOP CUTTING-EDGE DEEP LEARNING-BASED MODEL FOCUSED ON COMPUTER VISION FOR ACCURATE CLASSIFICATION OF ISCHEMIC STROKE ORIGINS 157
8 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION 158
8.1 INTRODUCTION 159
FIGURE 43 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION 159
FIGURE 44 MEDICAL IMAGING & DIAGNOSTICS SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE IN 2029 159
TABLE 60 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 160
TABLE 61 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 160
8.2 PATIENT DATA & RISK ANALYSIS 161
8.2.1 CONVERGENCE OF ML AND NLP TO OFFER LUCRATIVE GROWTH OPPORTUNITIES FOR PLAYERS 161
TABLE 62 CASE STUDY: MAYO CLINIC PARTNERED WITH GOOGLE TO IMPLEMENT AI MODELS AND ENHANCE PATIENT CARE 162
TABLE 63 PATIENT DATA & RISK ANALYSIS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 162
TABLE 64 PATIENT DATA & RISK ANALYSIS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 162
TABLE 65 PATIENT DATA & RISK ANALYSIS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 163
TABLE 66 PATIENT DATA & RISK ANALYSIS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 163
8.3 IN-PATIENT CARE & HOSPITAL MANAGEMENT 163
8.3.1 EASE OF PATIENT SCHEDULING WITH CHATBOTS AND VIRTUAL ASSISTANTS TO DRIVE MARKET 163
TABLE 67 CASE STUDY: PROMINENT MULTISPECIALTY HOSPITAL EMPLOYED ADOBE XD TO PREVENT RESOURCE WASTAGE AND ENHANCE EFFICIENCY 164
TABLE 68 IN-PATIENT CARE & HOSPITAL MANAGEMENT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 165
TABLE 69 IN-PATIENT CARE & HOSPITAL MANAGEMENT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 165
TABLE 70 IN-PATIENT CARE & HOSPITAL MANAGEMENT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 165
TABLE 71 IN-PATIENT CARE & HOSPITAL MANAGEMENT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 165
8.4 MEDICAL IMAGING & DIAGNOSTICS 166
8.4.1 ACCESSIBILITY IN MEDICAL IMAGING AND WORKFLOW OPTIMIZATION TO FOSTER SEGMENTAL GROWTH 166
TABLE 72 CASE STUDY: PHILIPS TRANSFORMED HEALTHCARE WITH AWS-POWERED AI SOLUTIONS 167
TABLE 73 MEDICAL IMAGING & DIAGNOSTICS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 167
TABLE 74 MEDICAL IMAGING & DIAGNOSTICS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 168
TABLE 75 MEDICAL IMAGING & DIAGNOSTICS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 168
TABLE 76 MEDICAL IMAGING & DIAGNOSTICS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 168
8.5 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING 169
8.5.1 ENHANCED PATIENT COMPLIANCE THROUGH BEHAVIORAL ANALYSIS TO BOOST DEMAND 169
TABLE 77 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 170
TABLE 78 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 171
TABLE 79 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 171
TABLE 80 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 171
8.6 VIRTUAL ASSISTANTS 171
8.6.1 ABILITY TO OFFER SIMPLIFIED COMPLEX MEDICAL INFORMATION TO DRIVE MARKET 171
TABLE 81 CASE STUDY: OSF COLLABORATED WITH GYANT TO IMPLEMENT CLARE, AI VIRTUAL CARE NAVIGATION ASSISTANT, BOOSTING DIGITAL HEALTH TRANSFORMATION 172
TABLE 82 VIRTUAL ASSISTANT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 173
TABLE 83 VIRTUAL ASSISTANT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 173
TABLE 84 VIRTUAL ASSISTANT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 173
TABLE 85 VIRTUAL ASSISTANT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 174
8.7 DRUG DISCOVERY 174
8.7.1 ACCELERATED IDENTIFICATION OF POTENTIAL DRUG CANDIDATES TO BOOST DEMAND 174
TABLE 86 CASE STUDY: AZOTHBIO UTILIZED RESCALE’S PLATFORM TO ENHANCE R&D AGILITY 175
TABLE 87 DRUG DISCOVERY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 175
TABLE 88 DRUG DISCOVERY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 175
TABLE 89 DRUG DISCOVERY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 176
TABLE 90 DRUG DISCOVERY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 176
8.8 RESEARCH 176
8.8.1 GROWING IMPORTANCE IN ANALYSIS OF SEQUENCE AND FUNCTIONAL PATTERNS FROM SEQUENCE DATABASES TO ACCELERATE DEMAND 176
TABLE 91 RESEARCH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 177
TABLE 92 RESEARCH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 177
TABLE 93 RESEARCH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 177
TABLE 94 RESEARCH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 178
8.9 HEALTHCARE ASSISTANCE ROBOTS 178
8.9.1 USE TO REVOLUTIONIZE PATIENT CARE BY STREAMLINING TASKS AND ENABLING REAL-TIME DATA ANALYSIS AND ENHANCE HEALTHCARE EXPERIENCES TO DRIVE MARKET 178
TABLE 95 HEALTHCARE ASSISTANCE ROBOTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 179
TABLE 96 HEALTHCARE ASSISTANCE ROBOTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 180
TABLE 97 HEALTHCARE ASSISTANCE ROBOTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 180
TABLE 98 HEALTHCARE ASSISTANCE ROBOTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 180
8.10 PRECISION MEDICINES 180
8.10.1 PERSONALIZED HEALTHCARE BY STREAMLINING CLINICAL TRIALS TO ACCELERATE DEMAND 180
TABLE 99 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 181
TABLE 100 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 181
TABLE 101 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 182
TABLE 102 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 182
8.11 EMERGENCY ROOMS & SURGERIES 182
8.11.1 QUICK IDENTIFICATION OF LIFE-THREATENING PATHOLOGIES TO FOSTER SEGMENTAL GROWTH 182
TABLE 103 EMERGENCY ROOMS & SURGERIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 183
TABLE 104 EMERGENCY ROOMS & SURGERIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 183
TABLE 105 EMERGENCY ROOMS & SURGERIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 183
TABLE 106 EMERGENCY ROOMS & SURGERIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 183
8.12 WEARABLES 184
8.12.1 PERSONALIZED TREATMENT STRATEGIES AND REAL-TIME INSIGHTS TO BOOST DEMAND 184
TABLE 107 CASE STUDY: KENSCI COLLABORATED WITH MICROSOFT TO ASSIST US NATIONAL GOVERNMENT IN IDENTIFYING PATIENTS WITH COPD 184
TABLE 108 WEARABLES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 185
TABLE 109 WEARABLES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 185
TABLE 110 WEARABLES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 185
TABLE 111 WEARABLES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 186
8.13 MENTAL HEALTH 186
8.13.1 PRESSING NEED TO DETECT DEPRESSION AND IDENTIFY SUICIDE RISKS THROUGH TEXT ANALYSIS TO DRIVE MARKET 186
TABLE 112 MENTAL HEALTH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 187
TABLE 113 MENTAL HEALTH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 187
TABLE 114 MENTAL HEALTH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 187
TABLE 115 MENTAL HEALTH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 188
8.14 CYBERSECURITY 188
8.14.1 PREVENTION OF INFILTRATION ATTEMPTS AND ENHANCED SPEED OF THREAT DETECTION TO BOOST DEMAND 188
TABLE 116 CASE STUDY: SNORKEL FLOW CREATED HIGH-ACCURACY ML MODELS TO OVERCOME HAND-LABELING CHALLENGES 189
TABLE 117 CYBERSECURITY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 189
TABLE 118 CYBERSECURITY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 189
TABLE 119 CYBERSECURITY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 190
TABLE 120 CYBERSECURITY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 190
9 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER 191
9.1 INTRODUCTION 192
FIGURE 45 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER 193
FIGURE 46 HOSPITALS & HEALTHCARE PROVIDERS TO HOLD LARGEST MARKET SHARE IN 2029 193
TABLE 121 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 194
TABLE 122 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 194
9.2 HOSPITALS & HEALTHCARE PROVIDERS 194
9.2.1 INCREASING USE IN MINING MEDICAL DATA AND STUDYING GENOMICS-BASED DATA FOR PERSONALIZED MEDICINE TO BOOST MARKET GROWTH 194
TABLE 123 CASE STUDY: UNIVERSITY COLLEGE LONDON, KING’S COLLEGE LONDON, AND NATIONAL HEALTH SERVICE COLLABORATION RESULTED IN DEVELOPMENT OF COGSTACK, THAT REVOLUTIONIZED HEALTHCARE DATA UTILIZATION 196
TABLE 124 HOSPITALS & HEALTHCARE PROVIDERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 196
TABLE 125 HOSPITALS & HEALTHCARE PROVIDERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 197
TABLE 126 HOSPITALS & HEALTHCARE PROVIDERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 197
TABLE 127 HOSPITALS & HEALTHCARE PROVIDERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 197
9.3 PATIENTS 198
9.3.1 RISE IN USE OF AI IN MENTAL HEALTH SUPPORT APPLICATIONS THROUGH CHATBOTS AND VIRTUAL THERAPISTS TO BOOST MARKET GROWTH 198
TABLE 128 CASE STUDY: COGNIZANT PARTNERED WITH ONE OF CLIENTS TO ENHANCE CALLER SELF-SERVICE AND IMPROVE MEMBER EXPERIENCE METRICS 199
TABLE 129 PATIENTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 199
TABLE 130 PATIENTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 199
TABLE 131 PATIENTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 200
TABLE 132 PATIENTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 200
9.4 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES 200
9.4.1 GROWING PARTNERSHIPS AMONG PLAYERS TO OFFER LUCRATIVE GROWTH OPPORTUNITIES TO PLAYERS 200
TABLE 133 CASE STUDY: AZURE MACHINE LEARNING-BASED INTELLIGENT SYSTEM ASSISTED LEADING PHARMA COMPANY TO AUTO-CLASSIFY PRODUCTS INTO MARKET-RELATED CATEGORIES THAT BOOSTED OPERATIONAL EFFICIENCY 202
TABLE 134 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 202
TABLE 135 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 203
TABLE 136 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 203
TABLE 137 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 203
9.5 HEALTHCARE PAYERS 204
9.5.1 FAST AND ACCURATE CLAIM PROCESSING AND ENHANCED FRAUD DETECTION BENEFITS TO BOOST DEMAND 204
TABLE 138 HEALTHCARE PAYERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 205
TABLE 139 HEALTHCARE PAYERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 205
TABLE 140 HEALTHCARE PAYERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 205
TABLE 141 HEALTHCARE PAYERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 206
9.6 OTHERS 206
TABLE 142 OTHERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 207
TABLE 143 OTHERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 207
TABLE 144 OTHERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 207
TABLE 145 OTHERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 208

　

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Summary

The AI in Healthcare market is projected to grow from USD 20.9 billion in 2024 and is projected to reach USD 148.4 billion by 2029; it is expected to grow at a CAGR of 48.1% from 2024 to 2029. Strong focus on AI in Healthcare rising potential of AI-based tools for elderly care, increasing trend towards developing human-aware AI systems, and acceleration of AI technology in drug discovery, genomics, and imaging & diagnostics to fuel the growth of AI in Healthcare market.
“Market for Software to hold the largest share during the forecast period.”
The software segment is categorized into AI Platform and AI Solution. Software is the foundational element driving the integration and functionality of AI in healthcare. Acting as the catalyst for the AI brain, it enables the implementation of intricate machine learning algorithms such as natural language processing and deep learning. These algorithms, supported by efficient data ingestion and management facilitated by software, empower AI systems to analyze extensive medical datasets and derive valuable insights. In practical application, software plays a pivotal role in diagnostic tools, treatment personalization, and virtual assistants, enhancing the accuracy in disease detection, treatment planning, and patient engagement. Additionally, the software optimizes healthcare operations through administrative automation and predictive analytics, contributing to improved efficiency and proactive patient care. As the backbone of AI in healthcare, software transforms the landscape by offering innovative solutions for enhanced patient care, early diagnosis, and personalized treatment.
“Market for Natural Language Processing segment is projected to hold for second-largest share during the forecast timeline.”
The clinical and research community widely uses NLP in healthcare for efficient managing and development of unstructured and semi-structured textual documents, including electronic health reports, clinical notes, and pathology reports. The algorithm extracts health problems from narrative text clinical documents and proposes inclusion in a patient’s electronic problem list to interpret accurately. NLP involves four steps: document pre-processing, health problem detection, negation detection, and document post-processing. Babylon Health (UK) has developed an app and NLP algorithms to help a chatbot ask the same questions a doctor would ask during in-person examination. The app does not outline an official diagnosis; rather it uses speech and language processing to extract symptoms and forward the profile information to a doctor. NLP is experiencing significant demand from healthcare institutions for structuring and interpretation of clinical data more accurately. Moreover, the rising usage of connected devices, along with the massive volume of patients’ data, accelerates the growth of this market.
“Market for patient data & risk analysis segment holds for major market share during the forecast period.”
The convergence of machine learning (ML) and natural language processing (NLP) in healthcare offers significant advancements in predictive insights for patient health. Utilizing diverse data sources, ML models analyze medical records, lab tests, demographics, and social determinants to identify patients at risk of specific diseases, while NLP algorithms extract insights from clinical notes to spot early signs of illness. This synergy enables personalized treatment plans, considering factors like treatment response and lifestyle. ML predicts potential exacerbations, allowing proactive interventions, and NLP interprets real-time data for remote monitoring. The benefits include improved patient outcomes, reduced costs, and enhanced medical decision-making. However, challenges like data privacy, algorithmic bias, and the need for transparency underscore the importance of ethical and responsible AI implementation in healthcare.

“North America is expected to have the largest market share during the forecast period.”
The healthcare sector in North America is witnessing an influx of new entrants into the Artificial Intelligence (AI) landscape, driven by cross-industry involvement and a substantial rise in venture capital investments. An example is Navina (US), a startup dedicated to an AI-driven primary care platform, securing a substantial USD 44 million in its series B funding round in October 2022. These investments propel Navina's AI and Machine Learning (ML) technology advancements. Another illustration is Tempus (US), specializing in AI-based precision medicine solutions, securing a notable USD 1.3 billion from 11 investors, including Ares Management and Google, in the same month.
Extensive primary interviews were conducted with key industry experts in the AI in Healthcare market space to determine and verify the market size for various segments and subsegments gathered through secondary research. The break-up of primary participants for the report has been shown below:
The break-up of the profile of primary participants in the AI in Healthcare market:
• By Company Type: Tier 1 – 50%, Tier 2 – 30%, and Tier 3 – 20%
• By Designation: C Level – 60%, Director Level – 30%, Others-10%
• By Region: North America – 40%, Europe – 20%, Asia Pacific – 30%, ROW- 10%
The report profiles key players in the AI in Healthcare market with their respective market ranking analysis. Prominent players profiled in this report are Koninklijke Philips N.V. (Netherlands), Microsoft (US), Siemens Healthineers AG (Germany), Intel Corporation (US), NVIDIA Corporation (US), Google Inc. (US), GE HealthCare Technologies Inc. (US), Oracle (US), and Johnson & Johnson Services, Inc. (US) among others.
Apart from this, Merative (US), General Vision, Inc., (US), CloudMedx (US), Oncora Medical (US), Enlitic (US), Lunit Inc., (South Korea), Qure.ai (India), Tempus (US), COTA (US), FDNA INC. (US), Recursion (US), Atomwise (US), Virgin Pulse (US), Babylon Health (UK), MDLIVE (US), Stryker (US), Qventus (US), Sweetch (Israel), Sirona Medical, Inc. (US), Ginger (US), Biobeat (Israel) are among a few emerging companies in the AI in Healthcare market.
Research Coverage: This research report categorizes the AI in Healthcare market based on offering, technology, application, end user, and region. The report describes the major drivers, restraints, challenges, and opportunities pertaining to the AI in Healthcare market and forecasts the same till 2029. Apart from these, the report also consists of leadership mapping and analysis of all the companies included in the AI in Healthcare ecosystem.
Key Benefits of Buying the Report The report will help the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall AI in Healthcare market and the subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and plan suitable go-to-market strategies. The report also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
• Analysis of key drivers (Generation of large and complex healthcare datasets, Pressing need to reduce healthcare costs, Improving computing power and declining hardware cost, Rising number of partnerships and collaborations among different domains in healthcare sector, and Growing need for improvised healthcare services due to imbalance between healthcare workforce and patients) influencing the growth of the AI in Healthcare market.
• Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the AI in Healthcare market.
• Market Development: Comprehensive information about lucrative markets – the report analysis the AI in Healthcare market across varied regions
• Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the AI in Healthcare market
• Competitive Assessment: In-depth assessment of market shares, growth strategies, and service offerings of leading players like Koninklijke Philips N.V. (Netherlands), Microsoft (US), Siemens Healthineers AG (Germany), Intel Corporation (US), NVIDIA Corporation (US) among others in the AI in Healthcare market.



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Table of Contents

1 INTRODUCTION 44
1.1 STUDY OBJECTIVES 44
1.2 MARKET DEFINITION 44
1.2.1 INCLUSIONS AND EXCLUSIONS 45
1.3 STUDY SCOPE 46
1.3.1 MARKETS COVERED 46
1.3.2 REGIONAL SCOPE 47
1.3.3 YEARS CONSIDERED 47
1.4 CURRENCY CONSIDERED 47
1.5 UNITS CONSIDERED 48
1.6 LIMITATIONS 48
1.7 STAKEHOLDERS 48
1.8 SUMMARY OF CHANGES 49
1.9 IMPACT OF RECESSION 50
FIGURE 1 GDP GROWTH PROJECTION DATA FOR MAJOR ECONOMIES, 2021–2023 50
1.10 GDP GROWTH PROJECTION UNTIL 2024 FOR MAJOR ECONOMIES 51
2 RESEARCH METHODOLOGY 52
2.1 RESEARCH DATA 52
FIGURE 2 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: RESEARCH DESIGN 52
2.1.1 SECONDARY DATA 53
2.1.1.1 List of major secondary sources 53
2.1.1.2 Key data from secondary sources 54
2.1.2 PRIMARY DATA 54
2.1.2.1 List of key interview participants 54
2.1.2.2 Key data from primary sources 55
2.1.2.3 Key industry insights 55
2.1.2.4 Breakdown of primaries 56
2.1.3 SECONDARY AND PRIMARY RESEARCH 56
2.2 MARKET SIZE ESTIMATION 57
FIGURE 3 RESEARCH FLOW: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET SIZE ESTIMATION 57
FIGURE 4 MARKET SIZE ESTIMATION METHODOLOGY (SUPPLY SIDE): REVENUE GENERATED BY COMPANIES FROM ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET 58
2.2.1 BOTTOM-UP APPROACH 58
2.2.1.1 Approach to estimate market size using bottom-up analysis (demand side) 58
FIGURE 5 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH 59
FIGURE 6 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH (DEMAND SIDE): REVENUE GENERATED FROM ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER 59
2.2.2 TOP-DOWN APPROACH 60
2.2.2.1 Approach to estimate market size using top-down analysis (supply side) 60
FIGURE 7 MARKET SIZE ESTIMATION METHODOLOGY: TOP-DOWN APPROACH 60
2.3 DATA TRIANGULATION 61
FIGURE 8 DATA TRIANGULATION 61
2.4 RESEARCH ASSUMPTIONS 62
2.5 RISK ASSESSMENT 62
2.6 PARAMETERS CONSIDERED TO ANALYZE RECESSION IMPACT ON STUDIED MARKET 63
2.7 RESEARCH LIMITATIONS 63
3 EXECUTIVE SUMMARY 64
FIGURE 9 SOFTWARE SEGMENT TO HOLD LARGEST MARKET SHARE IN 2029 64
FIGURE 10 MACHINE LEARNING SEGMENT TO DOMINATE MARKET DURING FORECAST PERIOD 65
FIGURE 11 PATIENTS SEGMENT TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD 66
FIGURE 12 MEDICAL IMAGING & DIAGNOSTICS SEGMENT TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD 67
FIGURE 13 NORTH AMERICA ACCOUNTED FOR LARGEST MARKET SHARE OF GLOBAL ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET IN 2023 68
4 PREMIUM INSIGHTS 69
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI IN HEALTHCARE MARKET 69
FIGURE 14 INCREASING ADOPTION OF AI-BASED TOOLS IN HEALTHCARE FACILITIES TO CREATE LUCRATIVE OPPORTUNITIES FOR MARKET PLAYERS 69
4.2 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING 69
FIGURE 15 SOFTWARE SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE IN 2024 69
4.3 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY 70
FIGURE 16 MACHINE LEARNING TECHNOLOGY TO COMMAND MARKET FROM 2023 TO 2029 70
4.4 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER 70
FIGURE 17 HOSPITALS & HEALTHCARE PROVIDERS SEGMENT TO LEAD MARKET THROUGHOUT FORECAST PERIOD 70
4.5 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION 71
FIGURE 18 MEDICAL IMAGING & DIAGNOSTICS SEGMENT TO REGISTER HIGHEST GROWTH DURING FORECAST PERIOD 71
4.6 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY COUNTRY 71
FIGURE 19 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET IN MEXICO TO GROW AT HIGHEST CAGR FROM 2024 TO 2029 71
5 MARKET OVERVIEW 72
5.1 INTRODUCTION 72
5.2 MARKET DYNAMICS 72
FIGURE 20 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES 72
5.2.1 DRIVERS 73
FIGURE 21 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: DRIVERS AND THEIR IMPACT 73
5.2.1.1 Exponential growth in data volume and complexity due to surging adoption of digital technologies 73
5.2.1.2 Significant cost pressure on healthcare service providers with increasing prevalence of chronic diseases 74
5.2.1.3 Rapid proliferation of AI in healthcare sector 74
5.2.1.4 Growing need for improvised healthcare services 75
5.2.2 RESTRAINTS 76
FIGURE 22 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: RESTRAINTS AND THEIR IMPACT 76
5.2.2.1 Reluctance among medical practitioners to adopt AI-based technologies 76
5.2.2.2 Shortage of skilled AI professionals handling AI-powered solutions 77
5.2.2.3 Lack of standardized frameworks for AL and ML technologies 77
5.2.3 OPPORTUNITIES 78
FIGURE 23 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: OPPORTUNITIES AND THEIR IMPACT 78
5.2.3.1 Increasing use of AI-powered solutions in elderly care 78
5.2.3.2 Increasing focus on developing human-aware AI systems 79
5.2.3.3 Rising use of technology in pharmaceuticals industry 79
5.2.3.4 Strategic partnerships and collaborations among healthcare companies and AI technology providers 80
5.2.4 CHALLENGES 82
FIGURE 24 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: CHALLENGES AND THEIR IMPACT 82
5.2.4.1 Inaccurate predictions due to scarcity of high-quality healthcare data 82
5.2.4.2 Concerns regarding data privacy 83
FIGURE 25 DATA BREACHES IN HEALTHCARE SECTOR, 2019–2023 83
5.2.4.3 Lack of interoperability between AI solutions offered by different vendors 84
FIGURE 26 CHALLENGES ASSOCIATED WITH HEALTHCARE DATA INTEROPERABILITY 84
5.3 TRENDS/DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESSES 85
FIGURE 27 TRENDS/DISRUPTIONS IMPACTING CUSTOMERS’ BUSINESSES 85
5.4 PRICING ANALYSIS 85
5.4.1 AVERAGE SELLING PRICE (ASP) TREND OF COMPONENTS OFFERED BY KEY PLAYERS, 2020–2029 86
FIGURE 28 AVERAGE SELLING PRICE (ASP) OF PROCESSOR COMPONENTS OFFERED BY KEY PLAYERS 86
TABLE 1 AVERAGE SELLING PRICE (ASP) OF PROCESSOR COMPONENTS OFFERED BY KEY PLAYERS 86
5.4.2 AVERAGE SELLING PRICE (ASP) TREND OF PROCESSOR COMPONENTS, BY REGION, 2020–2029 87
FIGURE 29 AVERAGE SELLING PRICE (ASP) TREND OF PROCESSOR COMPONENTS, BY REGION, 2020–2029 87
5.5 VALUE CHAIN ANALYSIS 88
FIGURE 30 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: VALUE CHAIN ANALYSIS 88
5.6 ECOSYSTEM MAPPING 89
FIGURE 31 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: ECOSYSTEM MAPPING 89
TABLE 2 COMPANIES AND THEIR ROLES IN ARTIFICIAL INTELLIGENCE IN HEALTHCARE ECOSYSTEM 90
5.7 TECHNOLOGY ANALYSIS 91
5.7.1 CLOUD COMPUTING 91
5.7.2 CLOUD GPU 91
5.7.3 GENERATIVE AI 92
5.7.4 CLOUD-BASED PACS 92
5.7.5 MULTI-CLOUD 92
5.8 PATENT ANALYSIS 93
TABLE 3 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: INNOVATIONS AND PATENT REGISTRATIONS 93
FIGURE 32 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: PATENTS GRANTED, 2013–2023 97
FIGURE 33 TOP 10 PATENT OWNERS IN LAST 10 YEARS, 2013–2023 97
TABLE 4 TOP PATENT OWNERS IN ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET IN LAST 10 YEARS 97
5.9 TRADE ANALYSIS 98
FIGURE 34 IMPORT DATA FOR HS CODE 854231-COMPLIANT PRODUCTS, BY COUNTRY, 2018–2022 (USD MILLION) 98
FIGURE 35 EXPORT DATA FOR HS CODE 854231-COMPLIANT PRODUCTS, BY COUNTRY, 2018–2022 (USD MILLION) 99
5.10 KEY CONFERENCES AND EVENTS, 2024–2025 99
TABLE 5 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: LIST OF CONFERENCES AND EVENTS, 2024–2025 99
5.11 CASE STUDY ANALYSIS 102
5.11.1 BIOBEAT LAUNCHED HOME-BASED REMOTE PATIENT MONITORING KIT DURING PEAK WAVE OF COVID-19 102
5.11.2 MICROSOFT COLLABORATED WITH CLEVELAND CLINIC TO APPLY PREDICTIVE AND ADVANCED ANALYTICS TO IDENTIFY POTENTIAL AT-RISK PATIENTS UNDER ICU CARE 102
5.11.3 TGEN COLLABORATED WITH INTEL CORPORATION AND DELL TECHNOLOGIES TO ASSIST PHYSICIANS AND RESEARCHERS ACCELERATE DIAGNOSIS AND TREATMENT AT LOWER COST 103
5.11.4 INSILICO DEVELOPED ML-POWERED TOOLS FOR DRUG IDENTIFICATION AND CHEMISTRY42 FOR NOVEL COMPOUND DESIGN 103
5.11.5 GE HEALTHCARE IMPROVED PATIENT OUTCOMES BY REDUCING WORKFLOW PROCESSING TIME USING MEDICAL IMAGING DATA 104
5.12 TARIFFS, STANDARDS, AND REGULATORY LANDSCAPE 104
TABLE 6 MFN TARIFF FOR HS CODE 854231-COMPLIANT PRODUCTS EXPORTED BY US, 2022 104
TABLE 7 MFN TARIFF FOR HS CODE 854231-COMPLIANT PRODUCTS EXPORTED BY CHINA, 2022 105
TABLE 8 MFN TARIFF FOR HS CODE 854231-COMPLIANT PRODUCTS EXPORTED BY GERMANY, 2022 105
5.12.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 106
TABLE 9 NORTH AMERICA: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 106
TABLE 10 EUROPE: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 107
TABLE 11 ASIA PACIFIC: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 108
TABLE 12 ROW: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 109
5.12.2 STANDARDS 110
5.12.2.1 ISO 22399:2020 110
5.12.2.2 IEC 62366:2015 110
5.12.2.3 Health Insurance Portability and Accountability Act (HIPAA) 110
5.12.2.4 EU General Data Protection Regulation (GDPR) 110
5.12.2.5 Fast Healthcare Interoperability Resources (HL7 FHIR) 110
5.12.2.6 Medical Device Regulation 111
5.12.2.7 World Health Organization Artificial intelligence for Health Guide 111
5.12.2.8 Algorithmic Justice League framework for assessing AI in healthcare 111
5.12.3 GOVERNMENT REGULATIONS 111
5.12.3.1 US 111
5.12.3.2 Europe 111
5.12.3.3 China 112
5.12.3.4 Japan 112
5.12.3.5 India 112
5.13 PORTER’S FIVE FORCES ANALYSIS 112
TABLE 13 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: PORTER’S FIVE FORCES ANALYSIS 112
FIGURE 36 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET: PORTER’S FIVE FORCES ANALYSIS 113
5.13.1 THREAT OF NEW ENTRANTS 113
5.13.2 THREAT OF SUBSTITUTES 114
5.13.3 BARGAINING POWER OF SUPPLIERS 114
5.13.4 BARGAINING POWER OF BUYERS 114
5.13.5 INTENSITY OF COMPETITIVE RIVALRY 114
5.14 KEY STAKEHOLDERS AND BUYING CRITERIA 115
5.14.1 KEY STAKEHOLDERS IN BUYING PROCESS 115
FIGURE 37 INFLUENCE OF KEY STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE END USERS 115
TABLE 14 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE END USERS 115
5.14.2 BUYING CRITERIA 115
FIGURE 38 KEY BUYING CRITERIA FOR TOP THREE END USERS 115
TABLE 15 KEY BUYING CRITERIA FOR TOP THREE END USERS 116
6 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING 117
6.1 INTRODUCTION 118
FIGURE 39 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING 118
FIGURE 40 SOFTWARE SEGMENT TO DOMINATE MARKET DURING FORECAST PERIOD 119
TABLE 16 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2020–2023 (USD MILLION) 119
TABLE 17 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY OFFERING, 2024–2029 (USD MILLION) 119
6.2 HARDWARE 120
TABLE 18 HARDWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 120
TABLE 19 HARDWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 120
TABLE 20 HARDWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 121
TABLE 21 HARDWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 121
6.2.1 PROCESSOR 121
6.2.1.1 Need for real-time processing of patient data to boost demand 121
TABLE 22 PROCESSOR: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (MILLION UNITS) 122
TABLE 23 PROCESSOR: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (MILLION UNITS) 123
TABLE 24 PROCESSOR: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 123
TABLE 25 PROCESSOR: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 123
6.2.1.2 MPUs/CPUs 124
TABLE 26 CASE STUDY: PHILIPS COLLABORATED WITH INTEL CORPORATION TO OPTIMIZE AI INFERENCING HEALTHCARE WORKLOADS ON INTEL XEON SCALABLE PROCESSORS USING OPENVINO TOOLKIT 124
6.2.1.3 GPUs 124
TABLE 27 CASE STUDY: DEEPPHARMA PLATFORM, OFFERED BY INSILICO, EQUIPPED WITH ADVANCED DEEP LEARNING TECHNIQUES, HELPS ANALYZE MULTI-OMICS DATA AND TISSUE-SPECIFIC PATHWAY ACTIVATION PROFILES 125
6.2.1.4 FPGAs 125
TABLE 28 CASE STUDY: INTEL CORPORATION, IN COLLABORATION WITH BROAD INSTITUTE, DEVELOPED BIGSTACK* 2.0 TO MEET EVOLVING DEMANDS OF GENOMICS RESEARCH 126
6.2.1.5 ASICs 126
6.2.2 MEMORY 127
6.2.2.1 Increasing demand for real-time medical image analysis and diagnosis support systems to drive market 127
TABLE 29 CASE STUDY: HUAWEI ASSISTED TOULOUSE UNIVERSITY HOSPITAL WITH OCEANSTOR ALL-FLASH SOLUTION THAT OFFERS LOW LATENCY AND SIMPLIFIED OPERATIONS AND MAINTENANCE MANAGEMENT 128
6.2.3 NETWORK 128
6.2.3.1 Growing need for remote patient monitoring and precision medicine to foster segmental growth 128
TABLE 30 NETWORK: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 129
TABLE 31 NETWORK: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 129
6.3 SOFTWARE 129
TABLE 32 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 130
TABLE 33 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 130
TABLE 34 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 131
TABLE 35 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 131
6.3.1 AI SOLUTION 131
6.3.1.1 Integration of non-procedural languages into AI solutions to accelerate segmental growth 131
TABLE 36 CASE STUDY: COGNIZANT LEVERAGED AZURE PLATFORM OF MICROSOFT AND DEVELOPED RESOLV, THAT EMPLOYS NATURAL LANGUAGE PROCESSING TO PROVIDE REAL-TIME RESPONSE TO ANALYTICAL QUERIES 132
TABLE 37 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR AI SOLUTIONS, BY DEPLOYMENT TYPE, 2020–2023 (USD MILLION) 132
TABLE 38 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR AI SOLUTIONS, BY DEPLOYMENT TYPE, 2024–2029 (USD MILLION) 133
6.3.1.2 On-premises 133
TABLE 39 CASE STUDY: GE HEALTHCARE ENHANCED ON-PREMISES CAPABILITY WITH SCYLLADB’S PROJECT ALTERNATOR 133
6.3.1.3 Cloud 134
TABLE 40 CASE STUDY: TAKEDA COLLABORATED WITH DELOITTE TO EMPLOY DEEP MINER TOOLKIT FOR RAPID DEVELOPMENT AND TESTING OF PREDICTIVE MODELS 134
6.3.2 AI PLATFORM 135
6.3.2.1 Increasing applications in development of toolkits for healthcare solutions to drive market 135
TABLE 41 CASE STUDY: CAYUGA MEDICAL CENTER SOUGHT SIMPLE CDI SOFTWARE SOLUTION TO IMPROVE WORKFLOWS AND REDUCE COSTS 135
TABLE 42 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR AI PLATFORMS, BY TYPE, 2020–2023 (USD MILLION) 136
TABLE 43 SOFTWARE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET FOR AI PLATFORMS, BY TYPE, 2024–2029 (USD MILLION) 136
6.3.2.2 Machine learning framework 136
6.3.2.3 Application program interface 137
6.4 SERVICES 137
TABLE 44 SERVICES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 137
TABLE 45 SERVICES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 138
TABLE 46 SERVICES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 138
TABLE 47 SERVICES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 138
6.4.1 DEPLOYMENT & INTEGRATION 139
6.4.1.1 Enhanced patient care along with streamlines workflows to drive demand 139
6.4.2 SUPPORT & MAINTENANCE 139
6.4.2.1 Need to evaluate performance and maintain operational stability to drive market 139
7 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY 140
7.1 INTRODUCTION 141
FIGURE 41 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY 141
FIGURE 42 MACHINE LEARNING TECHNOLOGY TO LEAD MARKET DURING FORECAST PERIOD 142
TABLE 48 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY, 2020–2023 (USD MILLION) 142
TABLE 49 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TECHNOLOGY, 2024–2029 (USD MILLION) 142
7.2 MACHINE LEARNING 143
TABLE 50 CASE STUDY: IN COLLABORATION WITH INTEL AND APOQLAR, THEBLUE.AI INTRODUCED BLUW.GDPR. EQUIPPED WITH ML ALGORITHMS ACCELERATED BY OPENVINO TOOLKIT 143
TABLE 51 MACHINE LEARNING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 144
TABLE 52 MACHINE LEARNING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 144
7.2.1 DEEP LEARNING 144
7.2.1.1 Rising applications in voice recognition, fraud detection, and recommendation engines to drive market 144
TABLE 53 WINNING HEALTH TECHNOLOGY INTRODUCED AI MEDICAL IMAGING SOLUTION BASED ON AMAX DEEP LEARNING ALL-IN-ONE TO REDUCE OVERALL MODEL INFERENCE TIME FROM OVER 0.5 HOURS TO LESS THAN 2 MINUTES FOR AI-AIDED DIAGNOSTIC IMAGING OF PULMONARY NODULES 146
7.2.2 SUPERVISED LEARNING 146
7.2.2.1 Contribution to clinical decision-making and enhancing personalized medications to boost demand 146
7.2.3 REINFORCEMENT LEARNING 147
7.2.3.1 Enhanced diagnostic accuracy in medical imaging analysis to fuel market growth 147
7.2.4 UNSUPERVISED LEARNING 147
7.2.4.1 Ability to uncover hidden patterns and handle unlabeled data challenges to boost demand 147
7.2.5 OTHERS 147
7.3 NATURAL LANGUAGE PROCESSING 148
TABLE 54 CASE STUDY: MARUTI TECHLABS ASSISTED UKHEALTH WITH ML MODEL FOR AUTOMATIC DATA EXTRACTION AND CLASSIFICATION 148
TABLE 55 NATURAL LANGUAGE PROCESSING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 149
TABLE 56 NATURAL LANGUAGE PROCESSING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 149
7.3.1 IVR 150
7.3.1.1 Enhanced operational efficiency and optimized clinical support to drive market 150
7.3.2 OCR 150
7.3.2.1 Reduced errors in data entry and streamlined administrative processes to spur demand 150
7.3.3 PATTERN AND IMAGE RECOGNITION 151
7.3.3.1 Optimized therapeutic outcomes and development of personal medication to foster segmental growth 151
7.3.4 AUTO CODING 152
7.3.4.1 Contribution to cost-saving and optimization of coding processes to drive market 152
7.3.5 CLASSIFICATION AND CATEGORIZATION 152
7.3.5.1 Accurate prediction of disease outcomes to boost demand 152
7.3.6 TEXT ANALYTICS 152
7.3.6.1 Significant contribution to drug discovery by examining extensive datasets of scientific literature to boost demand 152
7.3.7 SPEECH ANALYTICS 153
7.3.7.1 Contribution to sentiment analysis by assessing tone of patient conversations to boost demand 153
7.4 CONTEXT-AWARE COMPUTING 153
TABLE 57 CONTEXT-AWARE COMPUTING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2020–2023 (USD MILLION) 154
TABLE 58 CONTEXT-AWARE COMPUTING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY TYPE, 2024–2029 (USD MILLION) 154
7.4.1 DEVICE CONTEXT 154
7.4.1.1 Ability to offer comprehensive view of patient data to boost demand 154
7.4.2 USER CONTEXT 155
7.4.2.1 Better predictive analysis for disease prevention to foster segmental growth 155
7.4.3 PHYSICAL CONTEXT 155
7.4.3.1 Ability to address individualized needs based on surrounding environment to boost market 155
7.5 COMPUTER VISION 155
7.5.1 ENHANCED PRECISION WITH 3D VISUALIZATIONS AND PERSONALIZED PROCEDURES TO FOSTER SEGMENTAL GROWTH 155
TABLE 59 CASE STUDY: PUNKTUM COLLABORATED WITH MAYO CLINIC TO DEVELOP CUTTING-EDGE DEEP LEARNING-BASED MODEL FOCUSED ON COMPUTER VISION FOR ACCURATE CLASSIFICATION OF ISCHEMIC STROKE ORIGINS 157
8 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION 158
8.1 INTRODUCTION 159
FIGURE 43 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION 159
FIGURE 44 MEDICAL IMAGING & DIAGNOSTICS SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE IN 2029 159
TABLE 60 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 160
TABLE 61 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 160
8.2 PATIENT DATA & RISK ANALYSIS 161
8.2.1 CONVERGENCE OF ML AND NLP TO OFFER LUCRATIVE GROWTH OPPORTUNITIES FOR PLAYERS 161
TABLE 62 CASE STUDY: MAYO CLINIC PARTNERED WITH GOOGLE TO IMPLEMENT AI MODELS AND ENHANCE PATIENT CARE 162
TABLE 63 PATIENT DATA & RISK ANALYSIS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 162
TABLE 64 PATIENT DATA & RISK ANALYSIS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 162
TABLE 65 PATIENT DATA & RISK ANALYSIS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 163
TABLE 66 PATIENT DATA & RISK ANALYSIS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 163
8.3 IN-PATIENT CARE & HOSPITAL MANAGEMENT 163
8.3.1 EASE OF PATIENT SCHEDULING WITH CHATBOTS AND VIRTUAL ASSISTANTS TO DRIVE MARKET 163
TABLE 67 CASE STUDY: PROMINENT MULTISPECIALTY HOSPITAL EMPLOYED ADOBE XD TO PREVENT RESOURCE WASTAGE AND ENHANCE EFFICIENCY 164
TABLE 68 IN-PATIENT CARE & HOSPITAL MANAGEMENT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 165
TABLE 69 IN-PATIENT CARE & HOSPITAL MANAGEMENT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 165
TABLE 70 IN-PATIENT CARE & HOSPITAL MANAGEMENT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 165
TABLE 71 IN-PATIENT CARE & HOSPITAL MANAGEMENT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 165
8.4 MEDICAL IMAGING & DIAGNOSTICS 166
8.4.1 ACCESSIBILITY IN MEDICAL IMAGING AND WORKFLOW OPTIMIZATION TO FOSTER SEGMENTAL GROWTH 166
TABLE 72 CASE STUDY: PHILIPS TRANSFORMED HEALTHCARE WITH AWS-POWERED AI SOLUTIONS 167
TABLE 73 MEDICAL IMAGING & DIAGNOSTICS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 167
TABLE 74 MEDICAL IMAGING & DIAGNOSTICS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 168
TABLE 75 MEDICAL IMAGING & DIAGNOSTICS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 168
TABLE 76 MEDICAL IMAGING & DIAGNOSTICS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 168
8.5 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING 169
8.5.1 ENHANCED PATIENT COMPLIANCE THROUGH BEHAVIORAL ANALYSIS TO BOOST DEMAND 169
TABLE 77 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 170
TABLE 78 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 171
TABLE 79 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 171
TABLE 80 LIFESTYLE MANAGEMENT & REMOTE PATIENT MONITORING: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 171
8.6 VIRTUAL ASSISTANTS 171
8.6.1 ABILITY TO OFFER SIMPLIFIED COMPLEX MEDICAL INFORMATION TO DRIVE MARKET 171
TABLE 81 CASE STUDY: OSF COLLABORATED WITH GYANT TO IMPLEMENT CLARE, AI VIRTUAL CARE NAVIGATION ASSISTANT, BOOSTING DIGITAL HEALTH TRANSFORMATION 172
TABLE 82 VIRTUAL ASSISTANT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 173
TABLE 83 VIRTUAL ASSISTANT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 173
TABLE 84 VIRTUAL ASSISTANT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 173
TABLE 85 VIRTUAL ASSISTANT: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 174
8.7 DRUG DISCOVERY 174
8.7.1 ACCELERATED IDENTIFICATION OF POTENTIAL DRUG CANDIDATES TO BOOST DEMAND 174
TABLE 86 CASE STUDY: AZOTHBIO UTILIZED RESCALE’S PLATFORM TO ENHANCE R&D AGILITY 175
TABLE 87 DRUG DISCOVERY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 175
TABLE 88 DRUG DISCOVERY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 175
TABLE 89 DRUG DISCOVERY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 176
TABLE 90 DRUG DISCOVERY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 176
8.8 RESEARCH 176
8.8.1 GROWING IMPORTANCE IN ANALYSIS OF SEQUENCE AND FUNCTIONAL PATTERNS FROM SEQUENCE DATABASES TO ACCELERATE DEMAND 176
TABLE 91 RESEARCH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 177
TABLE 92 RESEARCH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 177
TABLE 93 RESEARCH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 177
TABLE 94 RESEARCH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 178
8.9 HEALTHCARE ASSISTANCE ROBOTS 178
8.9.1 USE TO REVOLUTIONIZE PATIENT CARE BY STREAMLINING TASKS AND ENABLING REAL-TIME DATA ANALYSIS AND ENHANCE HEALTHCARE EXPERIENCES TO DRIVE MARKET 178
TABLE 95 HEALTHCARE ASSISTANCE ROBOTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 179
TABLE 96 HEALTHCARE ASSISTANCE ROBOTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 180
TABLE 97 HEALTHCARE ASSISTANCE ROBOTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 180
TABLE 98 HEALTHCARE ASSISTANCE ROBOTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 180
8.10 PRECISION MEDICINES 180
8.10.1 PERSONALIZED HEALTHCARE BY STREAMLINING CLINICAL TRIALS TO ACCELERATE DEMAND 180
TABLE 99 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 181
TABLE 100 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 181
TABLE 101 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 182
TABLE 102 PRECISION MEDICINE: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 182
8.11 EMERGENCY ROOMS & SURGERIES 182
8.11.1 QUICK IDENTIFICATION OF LIFE-THREATENING PATHOLOGIES TO FOSTER SEGMENTAL GROWTH 182
TABLE 103 EMERGENCY ROOMS & SURGERIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 183
TABLE 104 EMERGENCY ROOMS & SURGERIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 183
TABLE 105 EMERGENCY ROOMS & SURGERIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 183
TABLE 106 EMERGENCY ROOMS & SURGERIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 183
8.12 WEARABLES 184
8.12.1 PERSONALIZED TREATMENT STRATEGIES AND REAL-TIME INSIGHTS TO BOOST DEMAND 184
TABLE 107 CASE STUDY: KENSCI COLLABORATED WITH MICROSOFT TO ASSIST US NATIONAL GOVERNMENT IN IDENTIFYING PATIENTS WITH COPD 184
TABLE 108 WEARABLES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 185
TABLE 109 WEARABLES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 185
TABLE 110 WEARABLES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 185
TABLE 111 WEARABLES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 186
8.13 MENTAL HEALTH 186
8.13.1 PRESSING NEED TO DETECT DEPRESSION AND IDENTIFY SUICIDE RISKS THROUGH TEXT ANALYSIS TO DRIVE MARKET 186
TABLE 112 MENTAL HEALTH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 187
TABLE 113 MENTAL HEALTH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 187
TABLE 114 MENTAL HEALTH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 187
TABLE 115 MENTAL HEALTH: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 188
8.14 CYBERSECURITY 188
8.14.1 PREVENTION OF INFILTRATION ATTEMPTS AND ENHANCED SPEED OF THREAT DETECTION TO BOOST DEMAND 188
TABLE 116 CASE STUDY: SNORKEL FLOW CREATED HIGH-ACCURACY ML MODELS TO OVERCOME HAND-LABELING CHALLENGES 189
TABLE 117 CYBERSECURITY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 189
TABLE 118 CYBERSECURITY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 189
TABLE 119 CYBERSECURITY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 190
TABLE 120 CYBERSECURITY: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 190
9 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER 191
9.1 INTRODUCTION 192
FIGURE 45 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER 193
FIGURE 46 HOSPITALS & HEALTHCARE PROVIDERS TO HOLD LARGEST MARKET SHARE IN 2029 193
TABLE 121 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2020–2023 (USD MILLION) 194
TABLE 122 ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY END USER, 2024–2029 (USD MILLION) 194
9.2 HOSPITALS & HEALTHCARE PROVIDERS 194
9.2.1 INCREASING USE IN MINING MEDICAL DATA AND STUDYING GENOMICS-BASED DATA FOR PERSONALIZED MEDICINE TO BOOST MARKET GROWTH 194
TABLE 123 CASE STUDY: UNIVERSITY COLLEGE LONDON, KING’S COLLEGE LONDON, AND NATIONAL HEALTH SERVICE COLLABORATION RESULTED IN DEVELOPMENT OF COGSTACK, THAT REVOLUTIONIZED HEALTHCARE DATA UTILIZATION 196
TABLE 124 HOSPITALS & HEALTHCARE PROVIDERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 196
TABLE 125 HOSPITALS & HEALTHCARE PROVIDERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 197
TABLE 126 HOSPITALS & HEALTHCARE PROVIDERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 197
TABLE 127 HOSPITALS & HEALTHCARE PROVIDERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 197
9.3 PATIENTS 198
9.3.1 RISE IN USE OF AI IN MENTAL HEALTH SUPPORT APPLICATIONS THROUGH CHATBOTS AND VIRTUAL THERAPISTS TO BOOST MARKET GROWTH 198
TABLE 128 CASE STUDY: COGNIZANT PARTNERED WITH ONE OF CLIENTS TO ENHANCE CALLER SELF-SERVICE AND IMPROVE MEMBER EXPERIENCE METRICS 199
TABLE 129 PATIENTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 199
TABLE 130 PATIENTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 199
TABLE 131 PATIENTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 200
TABLE 132 PATIENTS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 200
9.4 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES 200
9.4.1 GROWING PARTNERSHIPS AMONG PLAYERS TO OFFER LUCRATIVE GROWTH OPPORTUNITIES TO PLAYERS 200
TABLE 133 CASE STUDY: AZURE MACHINE LEARNING-BASED INTELLIGENT SYSTEM ASSISTED LEADING PHARMA COMPANY TO AUTO-CLASSIFY PRODUCTS INTO MARKET-RELATED CATEGORIES THAT BOOSTED OPERATIONAL EFFICIENCY 202
TABLE 134 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 202
TABLE 135 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 203
TABLE 136 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 203
TABLE 137 PHARMACEUTICALS & BIOTECHNOLOGY COMPANIES: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 203
9.5 HEALTHCARE PAYERS 204
9.5.1 FAST AND ACCURATE CLAIM PROCESSING AND ENHANCED FRAUD DETECTION BENEFITS TO BOOST DEMAND 204
TABLE 138 HEALTHCARE PAYERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 205
TABLE 139 HEALTHCARE PAYERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 205
TABLE 140 HEALTHCARE PAYERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 205
TABLE 141 HEALTHCARE PAYERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 206
9.6 OTHERS 206
TABLE 142 OTHERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2020–2023 (USD MILLION) 207
TABLE 143 OTHERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY APPLICATION, 2024–2029 (USD MILLION) 207
TABLE 144 OTHERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2020–2023 (USD MILLION) 207
TABLE 145 OTHERS: ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKET, BY REGION, 2024–2029 (USD MILLION) 208

　

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