Summary

The global artificial intelligence (AI) in healthcare market is projected to reach USD 194.79 billion by 2031 from USD 36.67 billion in 2026, at a CAGR of 39.7% during the forecast period. The AI in healthcare market is growing rapidly as hospitals and health systems increasingly deploy AI solutions to improve clinical efficiency, reduce physician burnout, and enhance patient outcomes. For example, in 2025, the Cleveland Clinic rolled out Ambience Healthcare’s AI documentation platform across its US outpatient network after evaluating the technology across more than 80 specialties. The solution was adopted by over 4,000 clinicians within 15 weeks of deployment, enabling documentation for more than one million patient encounters. Additionally, in January 2026, Aultman Health System deployed Nabla’s ambient AI solution across hundreds of clinicians through integration with Oracle Cerner, while AtlantiCare reported a 50% reduction in documentation time after implementing Oracle Health’s Clinical AI Agent. As real-world adoption continues to expand across hospitals, clinics, and health systems, AI is expected to play an increasingly critical role in shaping the future of healthcare delivery worldwide.

By deployment, the cloud-based models segment is expected to register the highest growth during the forecast period.
The AI in healthcare market is categorized into three deployment models: on-premises, cloud-based, and hybrid. The cloud-based models segment holds the largest share due to their scalability, cost-effectiveness, and accessibility. Cloud-based models facilitate real-time data processing and collaboration. They enable seamless integration, secure data storage, and rapid deployment, making them particularly well-suited for healthcare providers and payers. They provide faster, more reliable care while maintaining high-quality standards. Cloud-based AI solutions are becoming increasingly popular due to their cost-effectiveness, scalability, and support for remote access. These solutions enable seamless integration and real-time analytics. The growing adoption of telehealth and advancements in healthcare IT infrastructure further drive the demand for cloud-based models.

By end user, the hospitals & clinics segment dominated the artificial intelligence (AI) in healthcare market for healthcare providers in 2025.
By end user, the artificial intelligence (AI) in healthcare market for healthcare providers is segmented into hospitals & clinics, ambulatory surgical centers, home healthcare agencies & assisted living facilities, diagnostic & imaging centers, pharmacies, and other healthcare providers. The hospitals & clinics segment accounted for the largest share of the artificial intelligence (AI) in healthcare market for healthcare providers. This is attributed to the increasing demand for personalized medicines, precise diagnostics & surgical planning, growth in minimally invasive procedures, and the requirement for interoperability with existing systems. AI-based healthcare solutions enhance diagnostic accuracy, streamline operations, and personalize care in hospitals and clinics. They automate administrative tasks, predict patient outcomes, and enable faster decision-making with real-time data analysis. AI also supports remote monitoring, optimizes resources, and reduces costs by minimizing unnecessary treatments.

The Asia Pacific is expected to register the highest growth during the forecast period.
The artificial intelligence (AI) in healthcare market is divided into North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa. The Asia Pacific region is the fastest growing in the AI in healthcare market, driven by rapid healthcare digitalization, increasing investments in artificial intelligence, expanding adoption of electronic health records (EHRs), and strong government support for digital health initiatives. Countries such as China, India, Singapore, Japan, and South Korea are actively integrating AI into diagnostics, medical imaging, clinical decision support, and hospital workflow management. For instance, in February 2026, India launched its Strategy for AI in Healthcare (SAHI), establishing a national framework for the responsible and large-scale adoption of AI across the healthcare ecosystem. Healthcare providers across the region are also expanding AI adoption. In Singapore, the Ministry of Health is scaling generative AI for clinical documentation and AI-powered imaging solutions across the public healthcare system, while hospitals such as Ng Teng Fong General Hospital are using AI-enabled tools to improve patient management and care delivery.

The breakdown of primary participants is as mentioned below:
・By Company Type: Tier 1 (32%), Tier 2 (44%), and Tier 3 (24%)
・By Designation: Directors (30%), Managers (34%), and Others (36%)
・By Region: North America (40%), Europe (28%), Asia Pacific (20%), Latin America (7%), and the Middle East & Africa (5%)
Key Players
The prominent players operating in the artificial intelligence (AI) in healthcare market include Koninklijke Philips N.V. (Netherlands), Microsoft Corporation (US), Siemens Healthineers AG (Germany), NVIDIA Corporation (US), Epic Systems Corporation (US), GE Healthcare (US), Medtronic (US), Oracle (US), Veradigm LLC (US), Merative (IBM) (US), Google (US), Cognizant (US), Johnson & Johnson (US), Amazon Web Services, Inc. (US), SOPHiA GENETICS (US), Riverian Technologies (US), Terarecon (ConcertAI) (US), Solventum Corporation (US), Tempus (US), Viz.ai (US). These companies adopted strategies such as product launches, product updates, expansions, partnerships, collaborations, mergers, and acquisitions to strengthen their market presence in the artificial intelligence (AI) in healthcare market.
Research Coverage
The report analyzes the artificial intelligence (AI) in healthcare market and estimates the market size and future growth potential of various market segments by offering, function, application, deployment model, tool, end user, and region. The report also analyses factors (such as drivers, restrains, opportunities, and challenges) affecting market growth. It evaluates the opportunities and challenges for market stakeholders. The report also examines micromarkets in terms of their growth trends, prospects, and contributions to the total artificial intelligence (AI) in healthcare market. The report forecasts revenue for the market segments across five major regions. The report also provides a competitive analysis of the key players in this market, along with their company profiles, product offerings, recent developments, and key market strategies.
Reasons to Buy the Report
This report will help established firms, as well as new entrants/smaller firms, gauge the market pulse, which, in turn, will help them garner a greater share of the market. Firms purchasing the report could use one or a combination of the following strategies to strengthen their market positions.
This report provides insights on:
・Analysis of key drivers (rapid proliferation of AI in healthcare sector and growing need for improved healthcare services), restraints (shortage of skilled AI professionals handling AI-powered solutions and lack of standardized frameworks for AI and ML technologies), opportunities (strategic partnerships and collaborations among healthcare companies and AI technology providers and increase in focus on developing human-aware AI systems), challenges (concerns regarding data privacy and lack of interoperability) are factors contributing the growth of the artificial intelligence (AI) in healthcare market.
・Product Development/Innovation: Detailed insights on upcoming trends, research & development activities, and software launches in the artificial intelligence (AI) in healthcare market.
・Market Development: Comprehensive information on the lucrative emerging markets, offering, function, application, deployment, tool, end user, and region
・Market Diversification: Exhaustive information about software portfolios, growing geographies, recent developments, and investments in the artificial intelligence (AI) in healthcare market.
・Competitive Assessment: In-depth assessment of market shares, growth strategies, product offerings, company evaluation quadrant, and capabilities of leading players in the global artificial intelligence (AI) in healthcare market, such as Koninklijke Philips N.V. (Netherlands), Microsoft Corporation (US), Siemens Healthineers AG (Germany), NVIDIA Corporation (US), and Epic Systems Corporation (US).

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Table of Contents

1 INTRODUCTION 66
1.1 STUDY OBJECTIVES 66
1.2 MARKET DEFINITION 66
1.3 MARKET SCOPE 67
1.3.1 MARKET SEGMENTATION AND REGIONAL SCOPE 67
1.3.2 INCLUSIONS AND EXCLUSIONS 68
1.3.3 YEARS CONSIDERED 71
1.4 CURRENCY CONSIDERED 72
1.5 STAKEHOLDERS 72
2 EXECUTIVE SUMMARY 73
2.1 MARKET HIGHLIGHTS AND KEY INSIGHTS 73
2.2 KEY MARKET PARTICIPANTS: MAPPING OF STRATEGIC DEVELOPMENTS 75
2.3 DISRUPTIVE TRENDS IN AI IN HEALTHCARE MARKET 76
2.4 HIGH-GROWTH SEGMENTS 77
2.5 REGIONAL SNAPSHOT: MARKET SIZE, GROWTH RATE, AND FORECAST 78
3 PREMIUM INSIGHTS 79
3.1 AI IN HEALTHCARE MARKET OVERVIEW 79
3.2 AI IN HEALTHCARE MARKET, BY APPLICATION AND REGION 80
3.3 AI IN HEALTHCARE MARKET: GEOGRAPHIC SNAPSHOT 80
4 MARKET OVERVIEW 81
4.1 INTRODUCTION 81
4.2 MARKET DYNAMICS 81
4.2.1 DRIVERS 82
4.2.1.1 Increasing need for early detection and diagnosis of diseases 82
4.2.1.2 Exponential growth in data volume and complexity due to surging adoption of digital technologies 83
4.2.1.3 Significant cost pressure on healthcare service providers with increasing prevalence of chronic diseases 84
4.2.1.4 Rapid proliferation of AI in healthcare sector 84
4.2.1.5 Growing inclination toward precision medicine and personalized care 85
4.2.2 RESTRAINTS 86
4.2.2.1 Reluctance among medical practitioners to adopt AI-based technologies 86
4.2.2.2 Shortage of skilled AI professionals handling AI-powered solutions 86
4.2.2.3 Lack of standardized frameworks for AI and ML technologies 87
 
4.2.3 OPPORTUNITIES 87
4.2.3.1 Rising demand for AI-driven clinical decision support systems 87
4.2.3.2 Increasing focus on developing human-aware AI systems 88
4.2.3.3 Strategic partnerships and collaborations among healthcare companies and AI technology providers 89
4.2.3.4 Rising adoption of AI-enabled medical imaging and diagnostics 90
4.2.4 CHALLENGES 91
4.2.4.1 Inaccurate predictions due to scarcity of high-quality healthcare data 91
4.2.4.2 Concerns regarding data privacy 91
4.2.4.3 Lack of interoperability between AI solutions offered by different vendors 92
4.3 UNMET NEEDS AND WHITE SPACES 92
4.4 INTERCONNECTED MARKETS AND CROSS-SECTOR OPPORTUNITIES 93
4.5 STRATEGIC MOVES BY TIER 1/2/3 PLAYERS 93
5 INDUSTRY TRENDS 94
5.1 PORTER’S FIVE FORCES ANALYSIS 94
5.1.1 THREAT OF NEW ENTRANTS 95
5.1.2 THREAT OF SUBSTITUTES 96
5.1.3 BARGAINING POWER OF SUPPLIERS 96
5.1.4 BARGAINING POWER OF BUYERS 96
5.1.5 INTENSITY OF COMPETITIVE RIVALRY 96
5.2 MACROECONOMIC OUTLOOK 97
5.2.1 INTRODUCTION 97
5.2.2 GDP TRENDS AND FORECAST 97
5.2.3 TRENDS IN GLOBAL HEALTHCARE IT INDUSTRY 97
5.3 VALUE CHAIN ANALYSIS 97
5.4 ECOSYSTEM ANALYSIS 99
5.5 PRICING ANALYSIS 100
5.5.1 INDICATIVE PRICING FOR AI IN HEALTHCARE, BY APPLICATION (2025) 100
5.5.2 INDICATIVE PRICING FOR AI IN HEALTHCARE MARKET, BY REGION (2025) 101
5.6 KEY CONFERENCES AND EVENTS, 2026–2027 102
5.7 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS 102
5.8 INVESTMENT AND FUNDING SCENARIO 103
5.9 CASE STUDY ANALYSIS 104
5.9.1 ACCELERATING LUNG CANCER DIAGNOSIS THROUGH AI ACROSS NHS HOSPITALS 104
5.9.2 STRENGTHENING PATIENT TRUST THROUGH DIGITAL PATIENT ENGAGEMENT SOLUTIONS 104
5.9.3 OPTIMIZING PATIENT ENGAGEMENT AND COMMUNICATION WITH TRUBRIDGE PATIENT CONNECT 105
 
5.10 IMPACT OF US TARIFF – OVERVIEW 105
5.10.1 INTRODUCTION 105
5.10.2 KEY TARIFF RATES 106
5.10.3 PRICE IMPACT ANALYSIS 107
5.10.4 IMPACT ON COUNTRIES/REGIONS 107
5.10.4.1 US 107
5.10.4.2 Europe 108
5.10.4.3 Asia Pacific 109
5.10.5 IMPACT ON END USERS 109
6 TECHNOLOGICAL ADVANCEMENTS, AI-DRIVEN IMPACT, PATENTS, INNOVATIONS, AND FUTURE APPLICATIONS 111
6.1 KEY EMERGING TECHNOLOGIES 111
6.1.1 MACHINE LEARNING AND DEEP LEARNING 111
6.1.2 NATURAL LANGUAGE PROCESSING 111
6.1.3 COMPUTER VISION 111
6.2 COMPLEMENTARY TECHNOLOGIES 112
6.2.1 CLOUD COMPUTING 112
6.2.2 DIGITAL TWINS 112
6.2.3 ROBOTIC PROCESS AUTOMATION 112
6.3 ADJACENT TECHNOLOGIES 112
6.3.1 BLOCKCHAIN 112
6.3.2 AUGMENTED REALITY AND VIRTUAL REALITY 113
6.3.3 INTERNET OF THINGS 113
6.4 PATENT ANALYSIS 113
6.4.1 PATENT PUBLICATION TRENDS FOR AI IN HEALTHCARE LANDSCAPE 113
6.4.2 INSIGHTS: JURISDICTION AND TOP APPLICANT ANALYSIS 114
6.5 FUTURE APPLICATIONS 116
6.5.1 AI-DRIVEN PRECISION MEDICINE 116
6.5.2 REAL-TIME CLINICAL DECISION SUPPORT WITH GENERATIVE AI 117
6.5.3 DISEASE PREDICTION AND EARLY INTERVENTION THROUGH PREDICTIVE ANALYTICS 117
6.5.4 AI-POWERED VIRTUAL HEALTH ASSISTANTS AND DIGITAL CARE NAVIGATION 117
6.5.5 ADVANCING AUTONOMOUS AI AGENTS FOR HEALTHCARE WORKFLOW AUTOMATION 117
7 REGULATORY LANDSCAPE 118
7.1 REGIONAL REGULATIONS AND COMPLIANCE 118
7.1.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 118
7.1.2 REGULATORY FRAMEWORK 120
 
8 CUSTOMER LANDSCAPE AND BUYER BEHAVIOR 124
8.1 INTRODUCTION 124
8.2 DECISION-MAKING PROCESS 124
8.3 KEY STAKEHOLDERS INVOLVED IN BUYING PROCESS AND THEIR EVALUATION CRITERIA 124
8.3.1 KEY STAKEHOLDERS IN BUYING PROCESS 125
8.3.2 BUYING CRITERIA 125
8.4 ADOPTION BARRIERS AND INTERNAL CHALLENGES 126
8.5 UNMET NEEDS OF VARIOUS END USERS 127
8.5.1 UNMET NEEDS 127
8.5.2 END-USER EXPECTATIONS 128
8.6 MARKET PROFITABILITY 129
8.7 IMPACT ON COUNTRIES/REGIONS 130
8.7.1 US 130
8.7.2 EUROPE 131
8.7.3 ASIA PACIFIC 131
8.8 IMPACT ON END USERS 132
9 AI IN HEALTHCARE MARKET, BY FUNCTION 133
9.1 INTRODUCTION 134
9.2 DIAGNOSIS & EARLY DETECTION 134
9.2.1 PRE-SCREENING 135
9.2.1.1 Early detection, better outcomes, and cost-effective care associated with pre-screening to boost market 135
9.2.2 IVD 136
9.2.2.1 IVD market, by technology 137
9.2.2.1.1 Immunoassays 137
9.2.2.1.1.1 Potential to enhance biomarker detection and laboratory automation to accelerate demand 137
9.2.2.1.2 Clinical chemistry 138
9.2.2.1.2.1 Ability to improve laboratory efficiency and diagnostic accuracy to spur adoption 138
9.2.2.1.3 Molecular diagnostics 139
9.2.2.1.3.1 Pressing need to identify viral and bacterial pathogens in infectious diseases to facilitate adoption 139
9.2.2.2 IVD market, by application 140
9.2.2.2.1 Image analysis & interpretation 141
9.2.2.2.1.1 Rising focus of pathologists on analyzing tissue samples for detecting abnormalities at faster rate to fuel segmental growth 141
9.2.2.2.2 Biomarker discovery & analysis 142
9.2.2.2.2.1 Elevating adoption of precision medicine to create growth opportunities 142
9.2.2.2.3 Other IVD applications 142
 
9.2.3 DIAGNOSTIC IMAGING 143
9.2.3.1 Diagnostic imaging market, by application 144
9.2.3.1.1 Disease interpretation & report analysis 144
9.2.3.1.1.1 Proficiency in identifying disease-specific patterns and correlating findings across multiple data sources to fuel adoption 144
9.2.3.1.2 Image captioning & annotation 145
9.2.3.1.2.1 Increasing use of machine learning for automated image captioning to foster market growth 145
9.2.3.1.3 Image reconstruction 146
9.2.3.1.3.1 Greater emphasis of healthcare facilities on optimizing imaging workflows and increasing scanner throughput to augment adoption 146
9.2.3.1.4 Other diagnostic imaging applications 147
9.2.3.2 Diagnostic imaging market, by modality 148
9.2.3.2.1 Magnetic resonance imaging 148
9.2.3.2.1.1 Escalating adoption of AI across MRI workflows to enhance diagnostic confidence and improve scanner utilization to augment market growth 148
9.2.3.2.2 Computed tomography 149
9.2.3.2.2.1 Urgent need for efficient diagnosis to provide the right treatments to drive market 149
9.2.3.2.3 X-ray imaging 150
9.2.3.2.3.1 Development of innovative AI-based X-ray imaging solutions to drive market 150
9.2.3.2.4 Ultrasound 151
9.2.3.2.4.1 Increasing investments in developing AI-assisted ultrasound imaging auto-assessment tools to expedite market growth 151
9.2.3.2.5 Nuclear imaging 152
9.2.3.2.5.1 Integration of AI algorithms for quick detection and monitoring of abnormalities to facilitate market growth 152
9.2.3.2.6 Other diagnostic imaging modalities 153
9.2.4 RISK ASSESSMENT & PATIENT STRATIFICATION 154
9.2.4.1 Critical need to identify high-risk patients and predict their disease progression risks to boost demand 154
9.2.5 DRUG ALLERGY ALERTING 155
9.2.5.1 Significant focus on improving accuracy and speed of identifying allergic reactions to contribute to market growth 155
9.2.6 OTHER DIAGNOSIS & EARLY DETECTION FUNCTIONS 155
9.3 TREATMENT PLANNING & PERSONALIZATION 156
9.3.1 PERSONALIZED TREATMENT PLANNING 157
9.3.1.1 Precision medicine & genomic analysis 158
9.3.1.1.1 Adoption of AI-based software for personalized treatment decisions and improved outcomes to propel market 158
9.3.1.2 Predictive models for treatment response 159
9.3.1.2.1 Ability to personalize therapies, improve outcomes, and minimize adverse effects to fuel growth 159
9.3.1.3 Treatment recommendation systems 160
9.3.1.3.1 Competency in offering personalized, evidence-based treatment options to spike demand 160
9.3.2 PHARMACOLOGICAL THERAPY 161
9.3.2.1 Drug response prediction 162
9.3.2.1.1 Increasing use of AI-powered drug response prediction in oncology and chronic disease management to drive market 162
9.3.2.2 Dosing & administration 163
9.3.2.2.1 Capability of AI-based dosing models to adjust doses in real-time based on individual patient responses to spur demand 163
9.3.2.3 Other pharmacological therapy functions 164
9.3.3 SURGICAL THERAPY 164
9.3.3.1 Preoperative imaging & 3D modeling 165
9.3.3.1.1 Widening use of AI-driven 3D modeling in orthopedic, cardiovascular, neurological surgeries to fuel market growth 165
9.3.3.2 Intraoperative guidance & robotics 166
9.3.3.2.1 Inclination toward minimally invasive surgeries and faster patient recovery to boost implementation 166
9.3.3.3 Postoperative analysis & recovery 167
9.3.3.3.1 Ability to predict recovery patterns, identify risks, and provide personalized rehabilitation plans to boost demand 167
9.3.4 RADIATION THERAPY 168
9.3.4.1 Motion synchronization & auto contouring 169
9.3.4.1.1 Need for precise delivery of radiation and minimum exposure to surrounding healthy tissues to reinforce segmental growth 169
9.3.4.2 Real-time adaptive treatment delivery 170
9.3.4.2.1 Growing focus on consistent treatment accuracy and clinical outcome optimization to strengthen demand 170
9.3.4.3 Response assessment & quality assurance 171
9.3.4.3.1 Ability of AI solutions to analyze medical images, treatment plans, and dosimetric data to accelerate demand 171
9.3.4.4 Other radiation therapy functions 172
9.3.5 BEHAVIORAL THERAPY & PSYCHOTHERAPY 173
9.3.5.1 Virtual counseling & chatbots 174
9.3.5.1.1 Potential to improve patient communication, engagement, and service efficiency to expand implementation 174
9.3.5.2 Progress monitoring & feedback 175
9.3.5.2.1 Increasing use of smart wearables and mobile health apps to drive market 175
9.3.5.3 Follow-up & long-term support 175
9.3.5.3.1 Widespread adoption of telemedicine platforms to drive market expansion 175
 
9.3.6 IMMUNOTHERAPY 176
9.3.6.1 Real-time patient data monitoring 177
9.3.6.1.1 High demand for proactive healthcare management to foster segmental growth 177
9.3.6.2 Response & side-effect prediction 178
9.3.6.2.1 Need for earlier detection of treatment-related risks across drugs and vaccines to support segmental growth 178
9.3.6.3 Relapse prediction & long-term management 179
9.3.6.3.1 Necessity for continuous patient monitoring and risk stratification to fuel demand 179
9.3.7 OTHER TREATMENT PLANNING & PERSONALIZATION FUNCTIONS 180
9.4 PATIENT ENGAGEMENT & REMOTE MONITORING 181
9.4.1 SYMPTOM MANAGEMENT & VIRTUAL ASSISTANCE 182
9.4.1.1 Constant requirement for symptom management in chronic diseases to accelerate adoption of AI-powered apps 182
9.4.2 TELEHEALTH & REMOTE PATIENT MONITORING 183
9.4.2.1 Increasing adoption of mobile health apps and wearable devices to augment segmental growth 183
9.4.3 HEALTHCARE ASSISTANCE ROBOTS 184
9.4.3.1 Competency in improving quality of care and reducing burden on healthcare workers to promote adoption 184
9.4.4 MEDICATION REMINDERS 185
9.4.4.1 Capability to deliver medication guidance, personalized health coaching, and care coordination to foster demand 185
9.4.5 PATIENT EDUCATION & EMPOWERMENT 186
9.4.5.1 Need to improve treatment adherence and enhance self-management to drive market 186
9.4.6 OTHER PATIENT ENGAGEMENT & REMOTE MONITORING FUNCTIONS 187
9.5 POST-TREATMENT SURVEILLANCE & SURVIVORSHIP CARE 188
9.5.1 RECURRENCE MONITORING 189
9.5.1.1 Advancing post-treatment care through intelligent recurrence monitoring to support market growth 189
9.5.2 LONG-TERM OUTCOME PREDICTION 189
9.5.2.1 Greater emphasis on predicting disease recurrence and survival chances to improve uptake of AI-powered medical devices 189
9.5.3 MENTAL HEALTH & SUPPORT SYSTEMS 190
9.5.3.1 Rising focus on emotional well-being of patients with critical illness to support segmental growth 190
9.6 PHARMACY MANAGEMENT 191
9.6.1 EPRESCRIBING 192
9.6.1.1 Urgent need to replace paper-based prescription to electronic platform to expedite adoption of ePrescribing solutions 192
9.6.2 MEDICATION MANAGEMENT 193
9.6.2.1 Innovative medication management programs between healthcare professionals and pharmacists to encourage segmental growth 193
 
9.6.3 PHARMACY AUDIT & ANALYSIS 194
9.6.3.1 Demand for operational excellence and improved patient care outcomes across pharmacies to create growth opportunities 194
9.6.4 OTHER PHARMACY MANAGEMENT FUNCTIONS 195
9.7 DATA MANAGEMENT & ANALYTICS 196
9.7.1 RISING FOCUS OF HEALTHCARE ORGANIZATIONS ON DEVELOPING PERSONALIZED TREATMENT PLANS TO SUPPORT SEGMENTAL GROWTH 196
9.8 AI SCRIBE 196
9.8.1 GROWING NEED TO IMPROVE CLINICIAN PRODUCTIVITY AND REDUCE DOCUMENTATION TIME TO STRENGTHEN AI SCRIBE DEPLOYMENT 196
9.9 CLINICAL DECISION SUPPORT SYSTEMS 197
9.9.1 INCREASING NEED FOR EVIDENCE-BASED CLINICAL DECISION-MAKING TO SPIKE ADOPTION 197
9.10 ADMINISTRATIVE 198
9.10.1 PATIENT REGISTRATION & SCHEDULING 200
9.10.1.1 Rising adoption of self-service patient registration solutions to contribute to segmental growth 200
9.10.2 PATIENT ELIGIBILITY & AUTHORIZATION 201
9.10.2.1 Growing demand for faster prior authorization workflows to facilitate market growth 201
9.10.3 REVENUE CYCLE MANAGEMENT 201
9.10.3.1 Significant need to reduce claim denials and reimbursement delays to reinforce demand 201
9.10.4 WORKFORCE MANAGEMENT 202
9.10.4.1 Growing need to optimize healthcare workforce utilization to create lucrative opportunities 202
9.10.5 SUPPLY CHAIN & INVENTORY MANAGEMENT 203
9.10.5.1 Strong focus on reducing inventory costs and waste to bolster market growth 203
9.10.6 COMPLIANCE & DOCUMENTATION 204
9.10.6.1 Increasing regulatory compliance requirements in healthcare to escalate demand for AI-based compliance & documentation 204
9.10.7 HEALTHCARE WORKFLOW MANAGEMENT 205
9.10.7.1 Surging need for end-to-end workflow automation to foster segmental growth 205
9.10.8 ASSET MANAGEMENT 206
9.10.8.1 Pressing need to improve equipment availability and reduce downtime to heighten demand for AI-based asset management 206
9.10.9 CUSTOMER RELATIONSHIP MANAGEMENT 207
9.10.9.1 Growing focus on personalized patient engagement to boost segmental growth 207
9.10.10 FRAUD DETECTION & RISK MANAGEMENT 208
9.10.10.1 Rising financial losses to drive investment in AI-based proactive risk management solutions 208
 
9.10.11 CYBERSECURITY 209
9.10.11.1 Critical requirement to protect sensitive patient data to propel demand for AI-powered threat detection solutions 209
9.10.12 OTHER ADMINISTRATIVE FUNCTIONS 210
10 AI IN HEALTHCARE MARKET, BY OFFERING 211
10.1 INTRODUCTION 212
10.2 INTEGRATED SOLUTIONS 212
10.2.1 ESCALATING DEPLOYMENT OF ENTERPRISE-WIDE AI PLATFORMS TO CONTRIBUTE TO SEGMENTAL GROWTH 212
10.3 NICHE/POINT SOLUTIONS 213
10.3.1 GROWING ADOPTION OF SPECIALIZED AI APPLICATIONS FOR TARGETED CLINICAL AND ADMINISTRATIVE TASKS TO DRIVE MARKET 213
10.4 AI TECHNOLOGIES 214
10.4.1 HARNESSING AI FOR IMPROVED PATIENT OUTCOMES AND OPERATIONAL EFFICIENCY TO SUPPORT MARKET GROWTH 214
10.5 SERVICES 215
10.5.1 INCREASING FOCUS ON SEAMLESS AI DEPLOYMENT AND ONGOING SUPPORT TO FACILITATE SEGMENTAL GROWTH 215
11 AI IN HEALTHCARE MARKET, BY APPLICATION 217
11.1 INTRODUCTION 218
11.2 CLINICAL APPLICATIONS 218
11.2.1 NECESSITY TO ENHANCE PATIENT MONITORING AND CLINICAL DECISION-MAKING ABILITY TO SPUR DEMAND 218
11.3 NON-CLINICAL APPLICATIONS 220
11.3.1 REQUIREMENT TO ENHANCE OPERATIONAL EFFICIENCY, REDUCE ADMINISTRATIVE BURDEN, AND ENSURE BETTER RESOURCE ALLOCATION TO FOSTER MARKET GROWTH 220
12 AI IN HEALTHCARE MARKET, BY DEPLOYMENT MODEL 222
12.1 INTRODUCTION 223
12.2 ON-PREMISES MODEL 223
12.2.1 CRITICALITY OF DATA PRIVACY AND SECURITY TO SUPPORT DEPLOYMENT OF ON-PREMISES HEALTHCARE INFRASTRUCTURE 223
12.3 CLOUD-BASED MODEL 224
12.3.1 RAPID SCALABILITY AND SECURE DATA HANDLING AND INTEGRATION ATTRIBUTES TO ELEVATE DEMAND FOR CLOUD-BASED AI SOLUTIONS IN HEALTHCARE 224
12.4 HYBRID MODEL 225
12.4.1 FOCUS OF HEALTHCARE ORGANIZATIONS ON PROTECTING DATA WITHOUT SACRIFICING SCALABILITY TO DRIVE SEGMENTAL GROWTH 225
 
13 AI IN HEALTHCARE MARKET, BY TOOL 227
13.1 INTRODUCTION 228
13.2 MACHINE LEARNING 228
13.2.1 DEEP LEARNING 230
13.2.1.1 Convolutional neural networks 231
13.2.1.1.1 Ability to analyze X-rays, CT scans, and MRIs with high precision to elevate adoption 231
13.2.1.2 Recurrent neural networks 232
13.2.1.2.1 Competency in analyzing sequential data to predict disease progression and treatment efficacy to boost deployment 232
13.2.1.3 Generative adversarial networks 233
13.2.1.3.1 Capability to generate synthetic data to train AI models and address data scarcity and privacy issues to propel demand 233
13.2.1.4 Graph neural networks 234
13.2.1.4.1 Ability to recommend personalized treatment to stimulate adoption 234
13.2.1.5 Other deep learning tools 235
13.2.2 SUPERVISED LEARNING 236
13.2.2.1 Growing importance of evidence-based decision-making in predictive and diagnostic tasks to spur demand 236
13.2.3 REINFORCEMENT LEARNING 237
13.2.3.1 Significance of precision medicine, value-based care, and intelligent clinical decision support to accelerate demand 237
13.2.4 UNSUPERVISED LEARNING 238
13.2.4.1 Increasing need to uncover hidden insights from unstructured healthcare data to boost adoption 238
13.2.5 OTHER MACHINE LEARNING TOOLS 238
13.3 NATURAL LANGUAGE PROCESSING 239
13.3.1 SENTIMENT ANALYSIS 241
13.3.1.1 Increasing adoption of AI for patient feedback analysis to contribute to market growth 241
13.3.2 PATTERN & IMAGE RECOGNITION 241
13.3.2.1 Growing utilization of AI for early disease detection and personalized treatment planning to facilitate market expansion 241
13.3.3 AUTO CODING 242
13.3.3.1 Elevating use of AI tools to improve claim reimbursement process to fuel market growth 242
13.3.4 CLASSIFICATION & CATEGORIZATION 243
13.3.4.1 Business need to organize and classify healthcare data efficiently to expand AI implementation 243
13.3.5 TEXT ANALYTICS 244
13.3.5.1 Necessity to derive insights from unstructured clinical text to enhance use of text analytics 244
 
13.3.6 SPEECH RECOGNITION 244
13.3.6.1 Rising focus on reducing physician administrative burden to accelerate demand 244
13.4 CONTEXT-AWARE COMPUTING 245
13.4.1 DEVICE CONTEXT 246
13.4.1.1 Integration of connected medical devices to boost demand for intelligent device-aware healthcare applications 246
13.4.2 USER CONTEXT 247
13.4.2.1 Rising focus on individualized clinical decision support to spike demand 247
13.4.3 PHYSICAL CONTEXT 248
13.4.3.1 Increasing adoption of AI for real-time environmental and patient context monitoring to generate demand 248
13.5 GENERATIVE AI 249
13.5.1 INCREASING USE OF GENERATIVE MODELS TO OFFER TAILORED INTERVENTIONS TO DRIVE MARKET 249
13.6 COMPUTER VISION 249
13.6.1 ABILITY TO DETECT ABNORMALITIES SUCH AS TUMORS AND FRACTURES TO FUEL ADOPTION 249
13.7 IMAGE ANALYSIS 251
13.7.1 GROWING ADOPTION OF AI FOR EARLY DISEASE DETECTION AND DIAGNOSIS TO CONTRIBUTE TO MARKT GROWTH 251
14 AI IN HEALTHCARE MARKET, BY END USER 252
14.1 INTRODUCTION 253
14.2 HEALTHCARE PROVIDERS 253
14.2.1 HOSPITALS & CLINICS 255
14.2.1.1 Increasing focus on enhancing patient outcomes through AI to expedite market expansion 255
14.2.2 AMBULATORY CARE CENTERS 256
14.2.2.1 Rising adoption of AI to reduce care delivery costs to reinforce market growth 256
14.2.3 HOME HEALTHCARE AGENCIES & ASSISTED-LIVING FACILITIES 257
14.2.3.1 Escalating demand for personalized and continuous patient care to accelerate market growth 257
14.2.4 DIAGNOSTIC & IMAGING CENTERS 258
14.2.4.1 Increasing partnerships between hospitals and imaging centers to develop regulatory-cleared AI tools to support market growth 258
14.2.5 PHARMACIES 259
14.2.5.1 Heightened need for AI-driven inventory optimization to unlock opportunities 259
14.2.6 OTHER HEALTHCARE PROVIDERS 260
 
14.3 HEALTHCARE PAYERS 260
14.3.1 PUBLIC PAYERS 262
14.3.1.1 Increasing focus on cost optimization and fraud prevention to promote adoption of AI by public payers 262
14.3.2 PRIVATE PAYERS 263
14.3.2.1 Leveraging AI to optimize claims management, member engagement, and value-based care programs to drive market 263
14.4 PATIENTS 264
14.4.1 INCREASING ADOPTION OF AI FOR PATIENT ENGAGEMENT AND SELF-CARE TO FACILITATE MARKET GROWTH 264
14.5 OTHER END USERS 265
15 AI IN HEALTHCARE MARKET, BY REGION 266
15.1 INTRODUCTION 267
15.2 NORTH AMERICA 267
15.2.1 MACROECONOMIC OUTLOOK FOR NORTH AMERICA 269
15.2.2 US 284
15.2.2.1 Increasing focus on personalized medicine and precision healthcare to drive market 284
15.2.3 CANADA 298
15.2.3.1 Rising use of AI to improve disease care and patient survival and clinical workflows to drive market 298
15.3 EUROPE 312
15.3.1 MACROECONOMIC OUTLOOK FOR EUROPE 312
15.3.2 GERMANY 327
15.3.2.1 Collaborative health-related research and innovation programs and initiatives to contribute to market growth 327
15.3.3 UK 340
15.3.3.1 Significant focus of government on early diagnosis, personalized treatments, and rapid drug development to boost AI demand 340
15.3.4 FRANCE 354
15.3.4.1 Expanding AI-enabled digital healthcare and patient services to foster market growth 354
15.3.5 ITALY 368
15.3.5.1 Growing deployment of AI infrastructure to ensure transparency, data protection, and patient safety to facilitate market growth 368
15.3.6 SPAIN 382
15.3.6.1 Partnerships between scientists, medical professionals, and AI experts to develop innovative diagnostic tools to drive market 382
15.3.7 REST OF EUROPE 395
 
15.4 ASIA PACIFIC 410
15.4.1 MACROECONOMIC OUTLOOK FOR ASIA PACIFIC 411
15.4.2 CHINA 424
15.4.2.1 Growing focus on healthcare innovation through national AI initiatives to accelerate market growth 424
15.4.3 JAPAN 438
15.4.3.1 Strong healthcare infrastructure to drive adoption of advanced AI 438
15.4.4 INDIA 450
15.4.4.1 Strong government support for digital health initiatives to create lucrative opportunities 450
15.4.5 AUSTRALIA 464
15.4.5.1 Strong research ecosystem and national digital health initiatives to drive market 464
15.4.6 SOUTH KOREA 477
15.4.6.1 National Strategy for Artificial Intelligence and Digital Healthcare Innovation Plan initiatives to support market growth 477
15.4.7 REST OF ASIA PACIFIC 490
15.5 LATIN AMERICA 504
15.5.1 MACROECONOMIC OUTLOOK FOR LATIN AMERICA 504
15.5.2 BRAZIL 517
15.5.2.1 Expanding digital health infrastructure and growing adoption of AI-enabled healthcare technologies to foster market growth 517
15.5.3 MEXICO 530
15.5.3.1 Growing adoption of AI-driven chronic disease management platforms to stimulate market growth 530
15.5.4 REST OF LATIN AMERICA 543
15.6 MIDDLE EAST & AFRICA 556
15.6.1 MACROECONOMIC OUTLOOK FOR MIDDLE EAST & AFRICA 557
15.6.2 GCC 570
15.6.2.1 Saudi Arabia 583
15.6.2.1.1 Accelerating AI-driven healthcare transformation under Saudi Vision 2030 to contribute to market growth 583
15.6.2.2 UAE 596
15.6.2.2.1 Rising focus on improving healthcare quality and delivering personalized patient care to reinforce market growth 596
15.6.2.3 Rest of GCC 609
15.6.3 SOUTH AFRICA 622
15.6.3.1 Expanding use of AI to enhance medical imaging, clinical decision support, and disease surveillance to drive market 622
15.6.4 REST OF MIDDLE EAST & AFRICA 635
 
16 COMPETITIVE LANDSCAPE 649
16.1 INTRODUCTION 649
16.2 KEY PLAYER COMPETITIVE STRATEGY/RIGHT TO WIN, JANUARY 2023 TO JUNE 2026 649
16.3 REVENUE ANALYSIS, 2021–2025 652
16.4 MARKET SHARE ANALYSIS, 2025 653
16.5 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2025 655
16.5.1 STARS 655
16.5.2 EMERGING LEADERS 655
16.5.3 PERVASIVE PLAYERS 655
16.5.4 PARTICIPANTS 655
16.5.5 COMPANY FOOTPRINT: KEY PLAYERS, 2025 657
16.5.5.1 Company footprint 657
16.5.5.2 Region footprint 658
16.5.5.3 Application footprint 659
16.5.5.4 Tool footprint 660
16.5.5.5 Function footprint 661
16.5.5.6 Offering footprint 662
16.5.5.7 Deployment footprint 663
16.5.5.8 End user footprint 664
16.6 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2025 665
16.6.1 PROGRESSIVE COMPANIES 665
16.6.2 RESPONSIVE COMPANIES 665
16.6.3 DYNAMIC COMPANIES 665
16.6.4 STARTING BLOCKS 665
16.6.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2025 667
16.6.5.1 Detailed list of key startups/SMEs 667
16.6.5.2 Company footprint (startups/SMEs) 667
16.7 COMPANY VALUATION AND FINANCIAL METRICS 668
16.7.1 FINANCIAL METRICS 668
16.7.2 COMPANY VALUATION 668
16.8 BRAND/PRODUCT COMPARISON 669
16.9 COMPETITIVE SCENARIO 669
16.9.1 PRODUCT LAUNCHES/ENHANCEMENTS/APPROVALS 669
16.9.2 DEALS 671
16.9.3 OTHER DEVELOPMENTS 672
 
17 COMPANY PROFILES 673
17.1 KEY PLAYERS 673
17.1.1 KONINKLIJKE PHILIPS N.V. 673
17.1.1.1 Business overview 673
17.1.1.2 Products & services offered 674
17.1.1.3 Recent developments 676
17.1.1.3.1 Product launches/enhancements/approvals 676
17.1.1.3.2 Deals 678
17.1.1.3.3 Expansions 680
17.1.1.3.4 Other developments 681
17.1.1.4 MnM view 681
17.1.1.4.1 Right to win 681
17.1.1.4.2 Strategic choices 681
17.1.1.4.3 Weaknesses & competitive threats 681
17.1.2 MICROSOFT CORPORATION 682
17.1.2.1 Business overview 682
17.1.2.2 Products & services offered 683
17.1.2.3 Recent developments 685
17.1.2.3.1 Product launches/enhancements/approvals 685
17.1.2.3.2 Deals 686
17.1.2.4 MnM view 688
17.1.2.4.1 Right to win 688
17.1.2.4.2 Strategic choices 688
17.1.2.4.3 Weaknesses & competitive threats 688
17.1.3 NVIDIA CORPORATION 689
17.1.3.1 Business overview 689
17.1.3.2 Products & services offered 690
17.1.3.3 Recent developments 691
17.1.3.3.1 Product launches/enhancements/approvals 691
17.1.3.3.2 Deals 692
17.1.3.4 MnM view 694
17.1.3.4.1 Right to win 694
17.1.3.4.2 Strategic choices 694
17.1.3.4.3 Weaknesses & competitive threats 694
17.1.4 SIEMENS HEALTHINEERS AG 695
17.1.4.1 Business overview 695
17.1.4.2 Products & services offered 696
17.1.4.3 Recent developments 698
17.1.4.3.1 Product launches/enhancements/approvals 698
17.1.4.3.2 Deals 699
 
17.1.4.4 MnM view 700
17.1.4.4.1 Right to win 700
17.1.4.4.2 Strategic choices 701
17.1.4.4.3 Weaknesses & competitive threats 701
17.1.5 GE HEALTHCARE 702
17.1.5.1 Business overview 702
17.1.5.2 Products & services offered 703
17.1.5.3 Recent developments 705
17.1.5.3.1 Product launches/enhancements/approvals 705
17.1.5.3.2 Deals 705
17.1.5.3.3 Other developments 707
17.1.5.4 MnM view 707
17.1.5.4.1 Right to win 707
17.1.5.4.2 Strategic choices 707
17.1.5.4.3 Weaknesses & competitive threats 707
17.1.6 EPIC SYSTEMS CORPORATION 708
17.1.6.1 Business overview 708
17.1.6.2 Products & services offered 708
17.1.6.3 Recent developments 709
17.1.6.3.1 Product launches/enhancements/approvals 709
17.1.6.3.2 Deals 709
17.1.7 ORACLE 710
17.1.7.1 Business overview 710
17.1.7.2 Products & services offered 711
17.1.7.3 Recent developments 712
17.1.7.3.1 Product launches/enhancements/approvals 712
17.1.7.3.2 Deals 713
17.1.7.3.3 Expansions 714
17.1.8 VERADIGM INC. 715
17.1.8.1 Business overview 715
17.1.8.2 Products & services offered 715
17.1.8.3 Recent developments 716
17.1.8.3.1 Product launches/enhancements/approvals 716
17.1.8.3.2 Deals 717
17.1.9 AMAZON WEB SERVICES, INC. 718
17.1.9.1 Business overview 718
17.1.9.2 Products & services offered 719
17.1.9.3 Recent developments 720
17.1.9.3.1 Product launches/enhancements/approvals 720
17.1.9.3.2 Deals 721
17.1.9.3.3 Expansions 722
 
17.1.10 MERATIVE 723
17.1.10.1 Business overview 723
17.1.10.2 Products & services offered 723
17.1.10.3 Recent developments 724
17.1.10.3.1 Product launches/enhancements/approvals 724
17.1.10.3.2 Deals 724
17.1.11 IBM 725
17.1.11.1 Business overview 725
17.1.11.2 Products & services offered 726
17.1.11.3 Recent developments 727
17.1.11.3.1 Deals 727
17.1.12 MEDTRONIC 728
17.1.12.1 Business overview 728
17.1.12.2 Products & services offered 729
17.1.12.3 Recent developments 730
17.1.12.3.1 Product launches/enhancements/approvals 730
17.1.12.3.2 Deals 731
17.1.13 GOOGLE 732
17.1.13.1 Business overview 732
17.1.13.2 Products & services offered 733
17.1.13.3 Recent developments 735
17.1.13.3.1 Product launches/enhancements/approvals 735
17.1.13.3.2 Deals 736
17.1.13.3.3 Other developments 738
17.1.14 SOPHIA GENETICS 739
17.1.14.1 Business overview 739
17.1.14.2 Products & services offered 740
17.1.14.3 Recent developments 740
17.1.14.3.1 Product launches/enhancements/approvals 740
17.1.14.3.2 Deals 741
17.1.14.3.3 Other developments 742
17.1.15 JOHNSON & JOHNSON SERVICES, INC. 743
17.1.15.1 Business overview 743
17.1.15.2 Products & services offered 744
17.1.15.3 Recent developments 745
17.1.15.3.1 Product launches/enhancements/approvals 745
17.1.15.3.2 Deals 745
17.1.16 TEMPUS AI, INC. 746
17.1.16.1 Business overview 746
17.1.16.2 Products & services offered 747
17.1.16.3 Recent developments 748
17.1.16.3.1 Product launches/enhancements/approvals 748
17.1.16.3.2 Deals 749
17.1.17 CONCERTAI 751
17.1.17.1 Business overview 751
17.1.17.2 Products & services offered 751
17.1.17.3 Recent developments 752
17.1.17.3.1 Product launches/enhancements/approvals 752
17.1.17.3.2 Deals 753
17.1.18 SOLVENTUM CORPORATION 755
17.1.18.1 Business overview 755
17.1.18.2 Products & services offered 756
17.1.18.3 Recent developments 757
17.1.18.3.1 Deals 757
17.1.18.3.2 Other developments 758
17.1.19 COGNIZANT 759
17.1.19.1 Business overview 759
17.1.19.2 Products & services offered 760
17.1.19.3 Recent developments 761
17.1.19.3.1 Product launches/enhancements/approvals 761
17.1.19.3.2 Deals 762
17.1.20 VIZ.AI, INC. 763
17.1.20.1 Business overview 763
17.1.20.2 Products & services offered 763
17.1.20.3 Recent developments 764
17.1.20.3.1 Product launches/enhancements/approvals 764
17.1.20.3.2 Deals 764
17.1.20.3.3 Other developments 766
17.1.21 RIVERAIN TECHNOLOGIES 767
17.1.21.1 Business overview 767
17.1.21.2 Products & services offered 767
17.1.21.3 Recent developments 768
17.1.21.3.1 Product launches/enhancements/approvals 768
17.1.21.3.2 Deals 768
17.2 OTHER PLAYERS 770
17.2.1 QVENTUS 770
17.2.2 QURE.AI 771
17.2.3 SUKI AI, INC. 772
17.2.4 ENLITIC 773
17.2.5 SEGMED 773
 
18 RESEARCH METHODOLOGY 774
18.1 RESEARCH DATA 774
18.1.1 SECONDARY DATA 775
18.1.1.1 Key data from secondary sources 776
18.1.2 PRIMARY DATA 776
18.1.2.1 Key data from primary sources 778
18.1.2.2 Key industry insights 778
18.2 MARKET SIZE ESTIMATION 779
18.3 DATA TRIANGULATION 784
18.4 MARKET SHARE ESTIMATION 785
18.5 RESEARCH ASSUMPTIONS 785
18.6 RESEARCH LIMITATIONS 785
18.6.1 METHODOLOGY-RELATED LIMITATIONS 785
18.6.2 SCOPE-RELATED LIMITATIONS 785
18.7 RISK ASSESSMENT 786
19 APPENDIX 787
19.1 DISCUSSION GUIDE 787
19.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 794
19.3 CUSTOMIZATION OPTIONS 796
19.4 RELATED REPORTS 796
19.5 AUTHOR DETAILS 797