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Agentic AI In Healthcare Market Size, Share Report, 2033GVR Report cover
Agentic AI In Healthcare Market (2026 - 2033)
Size, Share & Trends Analysis Report By Agent System (Single Agent Systems, Multi Agent Systems), By Technology (Machine Learning, NLP), By Product, By Application, By End-use, By Region, And Segment Forecasts
Market Size, 2025
$759.6MMarket Estimate, 2026
$1,082.8MMarket Forecast, 2033
$17,292.3MCAGR, 2026–2033
48.6%Agentic AI In Healthcare Market Summary
The global agentic AI in healthcare market size was valued at USD 759.6 million in 2025 and is projected to grow from USD 1,082.8 million in 2026 to USD 17,292.3 million by 2033, at a CAGR of 48.6% from 2026 to 2033. North America dominated the market, accounting for a revenue share of 53.9% in 2025. The rising automation of repetitive tasks, growing focus on cost and resource optimization, and increasing adoption of enhanced patient care solutions are factors contributing to market growth.
Source: Grand View Research, IR Documents, Primary Interviews, Paid DatabasesTo learn more about this report,
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- By agent system: Single agent systems segment held the largest market share of 59.2% in 2025
- By product: Ready-to-deploy agents segment held the largest market share of 63.2% in 2025
- By application: Medical imaging segment held the largest market share of 19.1% in 2025.
- By technology: Machine learning segment held the largest market share of 35.6% in 2025.
- By end use: Pharmaceutical & biotechnology companies segment held the largest market share of 31.5% in 2025.
Regional Highlights
- Largest regional market: North America (53.9% revenue share, 2025)
- Fastest-growing regional market: Asia Pacific (highest CAGR, 2026-2033)
- By country: The U.S. held the largest market share in 2025.
Market Size & Forecast
- Market size in 2025: USD 759.6 Million
- Estimated market size in 2026: USD 1,082.8 Million
- Projected market size by 2033: USD 17,292.3 Million
- CAGR (2026-2033): 48.6%
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- Timeline2026–2033 annual, 2025 base
- Coverage20+ countries, 5 regions
- Companies10+ key players profiled
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In addition, AI agents help accelerate research and drug development processes in healthcare. The adoption of Agentic AI in healthcare is accelerating, driven by its ability to automate repetitive tasks and enhance operational efficiency. Administrative processes such as data entry, claims processing, and patient scheduling often demand significant time and are prone to human errors. AI-driven agents are streamlining these functions, reducing manual effort and enabling healthcare staff to focus on higher-value tasks. For instance, in February 2025, VoiceCare AI launched a pilot program with the Mayo Clinic to automate back-office operations using agentic AI technology. This initiative aims to optimize administrative workflows, minimize errors, and improve efficiency, allowing healthcare providers to dedicate more time to patient care.Automation also enhances accuracy, lowers operational costs, and speeds up processes. A key area benefiting from AI-driven efficiency is insurance claims processing, where AI can automatically assess and approve claims, minimizing delays and financial losses. In November 2024, Thoughtful AI introduced a specialized team of AI agents for revenue cycle management (RCM), streamlining workflows for tasks like eligibility verification and claims processing while improving accuracy and cost-effectiveness.
To learn more about this report, Download Free Sample Report Furthermore, agentic AI is transforming patient care by delivering personalized, data-driven insights that support clinical decision-making. AI-powered tools analyze vast datasets from electronic health records (EHRs), diagnostic devices, and wearables to predict health conditions and recommend tailored treatments. In fields like oncology and cardiology, AI-driven decision support systems assist in early disease detection, improving patient outcomes. AI agents are also expected to monitor patients in real-time, identifying potential health risks and enabling timely interventions.
For instance, in September 2024, Revitalist Lifestyle and Wellness Ltd. partnered with Sama Therapeutics to advance mental health care through a proprietary Digital Human AI Agent, enhancing mental health assessments worldwide.
In addition, AI is revolutionizing research and drug development by analyzing clinical trial data, genetic information, and real-world evidence. AI-driven insights help pharmaceutical companies identify promising drug candidates, predict their efficacy, and streamline clinical trial processes. AI models also simulate biological interactions to accelerate drug discovery. In January 2025, NVIDIA partnered with IQVIA, Illumina, Mayo Clinic, and Arc Institute to enhance drug discovery, advance genomic research, and drive innovation in healthcare through agentic and generative AI.
Agentic AI is rapidly reshaping the healthcare landscape, optimizing operations, enhancing patient care, and accelerating medical breakthroughs. Its integration into various healthcare processes is setting new standards for efficiency, precision, and innovation.
To learn more about this report, Download Free Sample Report 
To learn more about this report, Download Free Sample Report The integration of agentic AI in healthcare is revolutionizing medical services, enhancing diagnostic precision, optimizing operational efficiency, and enabling mass customization of treatments. As healthcare systems evolve, Agentic AI is expected to play a crucial role in autonomous clinical decision-making, real-time patient monitoring, and AI-driven drug discovery, leading to improved patient outcomes and reduced physician workload.
Despite challenges related to data privacy, ethical concerns, and system interoperability, the adoption of responsible AI governance and regulatory frameworks is expected to ensure its seamless and ethical integration. In the coming years, Agentic AI is expected to redefine healthcare workflows, making them more intelligent, predictive, and personalized, ultimately transforming the industry into a more efficient, proactive, and patient-centric ecosystem.
Market Dynamics
The rising automation of repetitive tasks, growing focus on cost and resource optimization, and increasing adoption of enhanced patient care solutions are factors contributing to market growth.
Agentic AI is transforming healthcare by enabling autonomous clinical decision-making, intelligent workflow automation, and personalized patient care. Unlike conventional AI systems that primarily assist clinicians with predefined tasks, Agentic AI can independently analyze complex clinical data, prioritize actions, coordinate workflows, and execute healthcare processes with minimal human intervention. These capabilities are improving diagnostic precision, optimizing resource utilization, and reducing administrative burden across healthcare organizations. Healthcare providers are increasingly investing in Agentic AI solutions to streamline care delivery, accelerate clinical decision-making, and improve operational efficiency.
The growing need to address physician shortages and rising healthcare expenditures is further accelerating the adoption of Agentic AI. Healthcare organizations are leveraging autonomous AI agents to automate repetitive administrative functions, coordinate patient care pathways, optimize scheduling, and support real-time clinical decision-making. These intelligent systems reduce clinician workload while improving care coordination and operational productivity. For instance, in February 2025, VoiceCare AI launched a pilot program with the Mayo Clinic to automate back-office operations using agentic AI technology. This initiative aims to optimize administrative workflows, minimize errors, and improve efficiency, allowing healthcare providers to dedicate more time to patient care. Agentic AI also enables continuous learning across diverse clinical datasets, enabling healthcare providers to deliver more efficient, data-driven care. For instance, in July 2026, Tempus AI (TEM) launched its next-generation Lens platform, an agentic AI system for oncology drug development, and upgraded its physician portal Hub with a generative AI co-pilot to streamline clinical workflows. As hospitals increasingly prioritize workflow optimization and value-based healthcare delivery, Agentic AI is becoming an essential component of next-generation healthcare infrastructure.
Furthermore, agentic AI is also accelerating innovation in drug discovery, precision medicine, and remote patient monitoring by enabling continuous analysis of complex healthcare data. In January 2025, NVIDIA partnered with IQVIA, Illumina, Mayo Clinic, and Arc Institute to enhance drug discovery, advance genomic research, and drive innovation in healthcare through agentic and generative AI. As responsible AI governance, regulatory frameworks, and interoperability standards continue to evolve, the integration of Agentic AI is expected to redefine healthcare workflows, making them more intelligent, predictive, efficient, and patient-centric while improving overall clinical outcomes.
The adoption of Agentic AI in healthcare is constrained by growing concerns related to data privacy, ethical decision-making, and evolving regulatory requirements. Agentic AI systems process large volumes of sensitive patient information from electronic health records, medical imaging, laboratory systems, and wearable devices, and must comply with regulations such as HIPAA, GDPR, and emerging AI-specific regulations. Healthcare organizations must also address informed consent, algorithm transparency, bias mitigation, and accountability for autonomous clinical decisions. These regulatory and ethical challenges increase implementation complexity and delay enterprise-scale adoption.
The absence of globally harmonized regulatory frameworks for autonomous AI further limits widespread deployment. Regulatory agencies continue to develop standards governing AI validation, safety monitoring, clinical performance, and post-market surveillance. Vendors must demonstrate that Agentic AI systems provide reliable, explainable, and clinically validated outputs before they can be integrated into routine healthcare workflows. Meeting these evolving regulatory expectations requires continuous model validation, documentation, and risk management, increasing development costs and commercialization timelines. These factors remain significant barriers to the large-scale adoption of Agentic AI across healthcare systems.Market Concentration & Characteristics
The chart below illustrates the relationship between industry concentration, industry characteristics, and industry participants. The x-axis represents the level of industry concentration, ranging from low to high. The y-axis represents various industry characteristics, including industry competition, level of partnerships & collaboration activities, degree of innovation, impact of regulations, and regional expansion. The agentic AI in healthcare industry is fragmented, with the presence of several emerging solution providers dominating the market. The degree of innovation is high, and the level of partnerships & collaboration activities is moderate. The impact of regulations on industry and the regional expansion of the industry is high.
The agentic AI in healthcare industry experiences a high degree of innovation driven by technological advancements. The increasing adoption of healthcare IT solutions in revenue cycle management, drug discovery, personalized treatments, and hospital administration supports new innovations in the market. For instance, in February 2025, Innovaccer launched Agents of Care-a suite of pre-trained AI Agents. This suite is expected to alleviate the strain of administrative overload in healthcare.
To learn more about this report, Download Free Sample Report “Healthcare needs more than fragmented, point solutions. It needs a unified, intelligent orchestration of AI capabilities,”
-Abhinav Shashank, Co-founder and CEO of Innovaccer.
“With Agents of Care, we’re redefining what AI can do for healthcare - delivering human-like efficiency while allowing care teams to focus on what truly matters: their patients.”
The industry is experiencing a moderate level of merger and acquisition activities undertaken by several key players. This is due to the desire to gain a competitive advantage in the industry, enhance technological capabilities, and consolidate in a rapidly growing market.
Regulations, such as the HIPAA in the U.S. and the GDPR in Europe, establish standards for safeguarding patient data privacy and security. Compliance with these regulations is crucial for AI applications in healthcare to ensure the safe and secure handling of patient information, reducing the risk of data breaches and unauthorized access.
Companies within the agentic AI sector seek geographic expansion strategies to maintain their foothold in emerging markets and attract customers from these regions.Moreover, the industry is witnessing moderate geographical expansion, driven by an increasing customer base for agentic AI software. With the growing adoption of digital healthcare solutions, the market is expected to grow significantly in the coming years, especially in developing countries.
Analyst Perspective
The agentic AI in healthcare market is emerging as the next phase of healthcare automation, moving beyond traditional AI-assisted decision support toward autonomous systems capable of executing complex clinical, administrative, and operational tasks with minimal human intervention. Growing workforce shortages, increasing administrative burden, rising healthcare costs, and the need for operational efficiency are accelerating demand for AI agents that can coordinate workflows, automate patient interactions, streamline revenue cycle processes, and support clinical decision-making. Healthcare organizations are increasingly evaluating agentic AI as a strategic technology to improve productivity while maintaining care quality and regulatory compliance. The competitive landscape is rapidly evolving around vendors capable of combining generative AI, large language models, workflow orchestration, and secure cloud infrastructure into scalable healthcare-specific AI agent platforms. Success will increasingly depend on delivering clinically reliable, explainable, and interoperable autonomous systems that integrate seamlessly with EHRs, hospital information systems, and digital health platforms. Vendors offering strong AI governance, enterprise-grade security, healthcare domain expertise, and configurable multi-agent architectures are expected to establish long-term competitive leadership. Healthcare organizations are accelerating adoption of autonomous AI-driven operations to balance productivity gains with care quality and regulatory compliance.
Agent System Insights
By agent system, the single agent systems segment held the largest market share of 59.2% in 2025. However, the multi agent systems segment is expected to witness the fastest growth with a CAGR over the forecast period, as these systems are simpler to design and implement compared to multi-agent systems. They are a single autonomous AI entity that operates independently and is designed to handle responsibilities without the need for collaboration with other agents. They are programmed to execute predefined actions based on input data, making decisions and taking actions autonomously within their designated scope.
Furthermore, the multi-agent system segment is expected to grow at the fastest CAGR from 2025 to 2030. Multi-agent systems (MAS) are computational frameworks composed of several independent agents collaborating to address complex challenges. Each MAS agent makes decisions, communicates with other agents, and performs tasks independently or collaboratively. This architecture allows for the distribution of tasks among various specialized agents, enhancing the overall efficiency and effectiveness of problem-solving processes. For instance, in January 2025, Cognizant introduced the Neuro AI Multi-Agent Accelerator and Multi-Agent Service Suite, allowing healthcare organizations to optimize their business processes, improve operational efficiency, and provide personalized customer experiences.
Product Insights
By product, the ready-to-deploy agents segment held the largest market share of 63.2% in 2025. However, the build-your-own agents segment is expected to witness the fastest growth with a CAGR over the forecast period. This growth is attributed to their rapid implementation ability, cost efficiency, enhanced scalability, and decision-making ability. Equipped with advanced algorithms and machine learning capabilities, ready-to-deploy AI agents enhance decision-making processes within healthcare settings. They analyze vast amounts of data quickly and accurately, providing insights that help clinicians make informed choices regarding patient care, resource allocation, and treatment protocols. In addition, these agents allow interoperability for smoother data exchange between different platforms, such as electronic health records (EHRs) and billing systems, thereby reducing data silos and enhancing overall workflow efficiency.
The build-your-own agents segment is expected to register the fastest CAGR over the forecast period. These agents enable healthcare providers to customize AI functionalities based on their unique operational workflows, patient demographics, and clinical practices. This flexibility allows organizations to develop solutions precisely aligned with their goals, improving patient engagement or streamlining administrative processes.
Technology Insights
By technology, the machine learning segment held the largest market share of 35.6% in 2025. However, the context-aware computing segment is expected to witness the fastest growth with a CAGR over the forecast period. Healthcare systems generate vast amounts of data from various sources, such as patient records, clinical trials, wearable devices, and medical imaging. ML algorithms are able to process and analyze this data to discover hidden patterns, detect diseases early, and assist in personalized treatment plans. In addition, ML can predict patient outcomes, detect the risk of diseases (like cancer, diabetes, or heart disease), and anticipate complications before they occur, allowing healthcare providers to intervene early, improving patient outcomes, and reducing treatment costs.
The context-aware computing segment is anticipated to grow at the fastest CAGR from 2025 to 2030, driven by its ability to adapt to the dynamic needs of patients and healthcare professionals in real time. By leveraging real-time data from various sources like wearable devices, sensors, and environmental inputs, context-aware computing in healthcare is anticipated to deliver more personalized, efficient, and proactive care.
Application Insights
By application, the medical imaging segment held the largest market share of 19.1% in 2025. However, the personalized treatment & drug discovery segment is expected to witness the fastest growth with a CAGR over the forecast period. This growth is attributed to the ability of AI agents in enhanced image analysis, increased diagnostic accuracy, and reduced interpretation times, thereby supporting radiologist workflows. AI agents are increasingly being used to automate the interpretation of medical images like X-rays, CT scans, MRIs, and mammograms. AI has the capability to recognize patterns and anomalies that might be challenging for human radiologists to observe. It has shown potential in accurately identifying conditions such as tumors and fractures. This capability is anticipated to aid radiologists in making more accurate diagnoses and serve as a second opinion, increasing diagnostic confidence.
Moreover, personalized treatment & drug discovery segment is expected to grow at the fastest CAGR over the forecast period. AI agents are increasingly used to analyze large-scale genomic data, identifying specific genetic mutations that could influence a patient’s susceptibility to diseases or their response to certain medications. AI’s ability to analyze vast amounts of data, predict outcomes, and tailor treatments to individual patients improves healthcare outcomes and accelerates the development of new therapies.
End Use Insights
By end use, the pharmaceutical & biotechnology companies segment held the largest market share of 31.5% in 2025. However, the academic and research institutes’ segment is expected to witness the fastest growth with a CAGR over the forecast period. Healthcare companies are increasingly adopting agentic AI tools and platforms to enhance operational efficiency, improve patient care, and streamline administrative processes. Agentic AI tools help analyze large datasets, including genetic data, protein structures, and chemical properties, to identify potential drug candidates. These tools help predict which compounds might be effective in treating certain diseases, significantly reducing the time and costs of early-stage drug discovery.
To learn more about this report, Download Free Sample Report The healthcare payers segment is anticipated to grow at the fastest CAGR from 2025 to 2030. Healthcare payers are increasingly adopting agentic AI tools to optimize operations, reduce costs, improve customer experience, and enhance decision-making processes. AI agents are anticipated to detect patterns in claims data that indicate potential fraud and autonomously flag suspicious claims for further review, reducing financial losses from fraud. In addition, they have the ability to analyze historical claims, health records, and lifestyle factors to categorize members based on their health risks. This allows insurers to adjust premiums or offer more tailored plans to individuals based on their specific needs.
Regional Insights
North America dominated the agentic AI in healthcare market with the largest revenue share of 53.9% in 2025. This is attributed to advancements in healthcare IT infrastructure, growing care expenditures, widespread adoption of AI/ML technologies, favorable government initiatives, lucrative funding options, and the presence of several key players. Factors such as the growing geriatric population, changing lifestyles, increasing prevalence of chronic disorders, growing demand for value-based care, and rising awareness levels about the implementation of AI-based technologies are further propelling market growth in North America.
To learn more about this report, Download Free Sample Report U.S. Agentic AI In Healthcare Market Trends
The agentic AI in healthcare market in the U.S. held the largest share in the North America region in 2025 due to the advancements in medical technology, drug discovery, and increasing demand for efficient and personalized healthcare solutions, coupled with regulatory initiatives and supportive government policies. Moreover, the presence of robust healthcare infrastructure and an increasing use of AI in healthcare fraud analytics propel market growth further.
Europe Agentic AI In Healthcare Market Trends
Europe Agentic AI in healthcare industry is anticipated to witness significant growth during the forecast period. This is attributed to the widespread adoption of AI technologies and increasing investments by government and private organizations. For instance, in February 2025, the European Union announced an investment of approximately USD 200 billion in artificial intelligence at the AI Action Summit in Paris.
“AI will improve our healthcare, spur our research and innovation, and boost our competitiveness.”
-EU Commission President Ursula von der Leyen
Agentic AI in healthcare industry in the UK is expected to grow over the forecast period, owing to the robust healthcare infrastructure and increasing investments in AI technologies to enhance patient care, optimize operations, and tackle various healthcare challenges. For instance, according to gov.uk, private firms invested around USD 250 million investments in AI from July 2024 to January 2025. Similarly, in January 2025, the government introduced the AI Opportunities Action Plan, which includes the creation of AI Growth Zones and an investment of USD 9 billion in the coming five years.
Agentic AI in healthcare industry in Germany held the largest market share in 2024 in the European market, attributed to the increasing investments in healthcare technology and robust R&D infrastructure. In addition, favorable government initiatives propel market growth further.
Asia Pacific Agentic AI In Healthcare Market Trends
Asia Pacific agentic AI in the healthcare industry is projected to experience significant growth in the coming years. Increasing investments from private investors, venture capitalists, and non-profit organizations aimed at enhancing clinical outcomes, improving data analysis and security, and reducing costs are driving adoption rates in the region. In addition, favorable government initiatives that support and promote the adoption of AI-based technologies by healthcare organizations and providers are anticipated to boost market growth. Moreover, rising healthcare expenditure and technological advancements in healthcare IT contribute to market growth.
Japan agentic AI in healthcare industry is expected to grow significantly over the forecast period. Increasing demand for personalized healthcare solutions and the growing adoption of artificial intelligence in healthcare contribute to market growth. In addition, technological advancements and growing government support and initiatives further fuel market growth.
Agentic AI in healthcare industry in China is expected to grow rapidly, owing to the increasing healthcare expenditure and rapid adoption of healthcare IT solutions. Moreover, increasing public and private funding and initiatives propel market growth further. For instance, in December 2024, Fangzhou Inc. hosted an AI summit in Chengdu, strengthening partnerships with Tencent and Baidu. They unveiled the "AI Agent Solution," enhancing healthcare access for consumers and professionals. This collaboration aims to revolutionize Internet healthcare, combining Fangzhou's expertise with Tencent and Baidu's strengths in digital marketing and AI.
Latin America Agentic AI in Healthcare Market Trends
Latin America agentic AI in healthcare industry is anticipated to grow at a significant CAGR over the forecast period, This is attributed to the growing awareness about AI technologies, increasing government spending, and collaboration activities.
Middle East & Africa Agentic AI In Healthcare Market Trends
Middle East and Africa agentic AI in healthcare industry is expected to grow at a significant CAGR over the forecast period. The market is characterized by a dynamic landscape driven by the growing adoption of healthcare IT solutions, increasing healthcare expenditures, and supportive government policies.
Key Agentic AI In Healthcare Company Insights
Key players operating in the agentic AI in healthcare industry are undertaking various initiatives to strengthen their market presence and increase the reach of their products and services. Strategies such as new product launches and partnerships are playing a key role in propelling market growth. Moreover, key players in the healthcare industry, such as GE HealthCare, are developing multi-agent systems in collaboration with Amazon Bedrock to enhance patient care and personalized experiences.
Key Agentic AI In Healthcare Companies:
The following key companies have been profiled for this study on the agentic AI in healthcare market.
- nVIDIA
- Oracle
- Microsoft
- Thoughtful Automation Inc.
- Hippocratic AI Inc.
- Cognigy
- Amelia US LLC
- Beam AI.
- Momentum
- Notable
- Springs
Competitive Benchmarking
Category
Operating Strategies
Competitive Edge
Weakness
Established Players (NVIDIA, Oracle, Microsoft)
- Expand agentic AI capabilities through strategic partnerships, cloud ecosystem expansion, and collaborations with healthcare providers, pharmaceutical companies, and digital health vendors. Invest in generative AI, autonomous AI agents, healthcare foundation models, AI infrastructure, interoperability, clinical workflow automation, and secure cloud platforms to support enterprise-wide healthcare transformation and intelligent decision-making.
- Strong global market presence, extensive AI and cloud infrastructure, advanced computing capabilities, and established relationships with healthcare enterprises. Robust R&D investments, scalable cloud ecosystems, comprehensive AI development platforms, and strong cybersecurity and regulatory compliance capabilities enable deployment of enterprise-grade agentic AI solutions across diverse healthcare settings.
- High implementation costs, complex enterprise integration, and dependence on sophisticated IT infrastructure may limit adoption among smaller healthcare organizations. Broad technology portfolios may require significant customization to address specialty-specific clinical workflows and evolving healthcare use cases.
Emerging & Specialized Players (Thoughtful Automation Inc., Hippocratic AI Inc., Cognigy, Amelia US LLC, Beam AI, Momentum, Notable, Springs)
- Focus on autonomous AI agents for clinical workflow automation, patient engagement, administrative operations, care coordination, contact center automation, and revenue cycle optimization. Leverage large language models (LLMs), generative AI, conversational AI, low-code platforms, and API-driven integration to deliver scalable, healthcare-specific automation solutions with rapid deployment and continuous learning capabilities.
- Greater agility in developing healthcare-focused autonomous AI agents tailored to administrative, clinical, and patient-facing workflows. Ability to rapidly integrate generative AI, conversational AI, and workflow orchestration technologies while offering flexible deployment models, faster implementation, and highly personalized automation solutions.
- Limited financial resources, smaller enterprise customer base, and evolving clinical validation compared with established technology providers. Dependence on partnerships with EHR vendors, cloud providers, and healthcare organizations, along with evolving AI governance and regulatory requirements, may limit large-scale adoption and international expansion.
Recent Developments
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In March 2025 Microsoft has the launched of Microsoft Dragon Copilot, innovative-edge AI-powered assistant designed to streamline clinical workflows and enhance patient care. Built on technology from Nuance Communications, a speech recognition and AI leader Microsoft acquired for USD 19.7 billion in 2022, this new solution marks a significant step in healthcare AI innovation.
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In January 2025, Hippocratic AI launched a Healthcare AI Agent App Store, allowing clinicians to create AI agents for patient care and operational efficiency.
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In October 2024, Microsoft launched new AI features in its Microsoft Cloud for Healthcare, designed to improve care experiences. These offerings consist of healthcare AI models for data integration and analysis and a public preview of a healthcare agent service that assists with appointment scheduling and patient triage.
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In March 2024, Hippocratic AI partnered with NVIDIA to create empathetic AI healthcare agents that enable super-low-latency conversational interactions. This collaboration focuses on developing "Empathy Inference," which enhances emotional connections between patients and AI. The agents will assist healthcare providers in non-diagnostic tasks, improving access and patient outcomes through advanced technology.
Agentic AI In Healthcare Market Report Scope
Report Attribute
Details
Market size in 2025
USD 759.6 million
Estimated market size in 2026
USD 1,082.8 million
Projected market size by 2033
USD 17,292.3 million
Growth rate
CAGR of 40.6% from 2026 to 2033
Historical data
2021 - 2025
Forecast period
2026 - 2033
Quantitative units
Revenue in USD million/billion and CAGR from 2026 to 2033
Report coverage
Revenue forecast, company ranking, competitive landscape, growth factors, and trends
Segments covered
Agent system, product, application, technology, end use, region
Regional scope
North America; Europe; Asia Pacific; Latin America; MEA
Country scope
U.S.; Canada; Mexico; Germany; UK; France; Italy; Spain; Norway; Denmark; Sweden; China; Japan; India; South Korea; Australia; Thailand; Brazil; Argentina; Saudi Arabia; South Africa; UAE; Kuwait
Key companies profiled
nVIDIA; Oracle; GE Healthcare; Microsoft; Thoughtful Automation Inc.; Hippocratic AI Inc.; Cognigy; Amelia US LLC; Beam AI.; Momentum.; Notable.; Springs
IQVIA Inc
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Global Agentic AI In Healthcare Market Report Segmentation
This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2021 to 2033. For this study, Grand View Research has segmented the global agentic AI in healthcare market report based on agent system, product, technology, application, end use, and region:
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Agent System Outlook (Revenue, USD Million, 2021 - 2033)
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Single Agent Systems
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Multi Agent Systems
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Product Outlook (Revenue, USD Million, 2021 - 2033)
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Ready-to-Deploy Agents
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Build-Your-Own Agents
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Technology Outlook (Revenue, USD Million, 2021 - 2033)
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Machine Learning
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Deep Learning
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Supervised
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Unsupervised
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Others (Reinforcement Learning, Semi-supervised)
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Natural Language Processing
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Smart Assistance
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OCR (Optical Character Recognition)
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Auto Coding
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Text Analytics
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Speech Analytics
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Classification & Categorization
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Context-aware Computing
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Computer Vision
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Application Outlook (Revenue, USD Million, 2021 - 2033)
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Medical Imaging
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Personalized Treatment & Drug Discovery
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Electronic Health Records (EHRs)
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Remote Patient Care
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Clinical Decision-Making
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Risk Prediction & Pandemic Preparedness
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Genomic Data Analysis
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Chronic Disease Management
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Hospital Resource Optimization
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Medical Research & Data analysis
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Others
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End Use Outlook (Revenue, USD Million, 2021 - 2033)
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Healthcare Providers
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Healthcare Companies
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Academic & Research Institutes
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Healthcare Payers
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Others
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Regional Outlook (Revenue, USD Million, 2021 - 2033)
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North America
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U.S.
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Canada
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Mexico
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Europe
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Germany
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UK
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France
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Italy
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Spain
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Denmark
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Sweden
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Norway
-
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Asia Pacific
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China
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Japan
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India
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South Korea
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Australia
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Thailand
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Latin America
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Brazil
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Argentina
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MEA
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South Africa
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Saudi Arabia
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UAE
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Kuwait
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Research Methodology
The Agentic AI in healthcare market figures in this report are based on a proven research process that combines executive interviews with secondary research from proprietary databases, company filings, and recognized regulatory and institutional sources. Market size is built through value-chain sizing - reconciling supply-side and demand-side estimates - and triangulated with bottom-up and top-down approaches. Every estimate passes multiple levels of expert validation before publication, with each Agentic AI in healthcare segment quantified using the revenue-capture definitions in the table below.
Segment Definition
Segment - Agent System
Revenue capture definition
Single Agent Systems
AI systems in which a single autonomous agent independently performs specific healthcare tasks such as clinical documentation, patient triage, diagnostic assistance, scheduling, or decision support. These agents perceive inputs, reason, and execute actions within a defined workflow without coordinating with multiple autonomous agents.
Multi Agent Systems
AI ecosystems comprising multiple autonomous agents that collaborate, communicate, and coordinate to accomplish complex healthcare workflows. Each agent specializes in functions such as diagnosis, scheduling, medical coding, patient monitoring, or resource management, collectively improving efficiency, scalability, and clinical decision-making.
Segment - Product
Revenue capture definition
Ready-to-Deploy Agents
Pre-configured agentic AI solutions designed for immediate implementation with minimal customization. These agents provide standardized capabilities such as ambient clinical documentation, virtual assistants, patient engagement, administrative automation, and decision support across healthcare organizations.
Build-Your-Own Agents
Customizable agentic AI platforms that enable healthcare organizations to develop, configure, train, and deploy autonomous AI agents tailored to specific clinical, operational, research, or administrative workflows while integrating with existing healthcare IT infrastructure.
Segment - Technology
Revenue capture definition
Machine Learning
AI techniques that enable systems to learn from structured and unstructured healthcare data to generate predictions, automate decisions, recognize patterns, and continuously improve performance without explicit programming.
Deep Learning
Advanced machine learning models utilizing multi-layer neural networks to analyze complex healthcare data such as medical images, genomic information, speech, and electronic health records for highly accurate predictions and decision support.
Supervised
Machine learning algorithms trained using labeled datasets, enabling AI systems to classify diseases, predict clinical outcomes, identify abnormalities, and support diagnostic decision-making based on known examples.
Unsupervised
Machine learning methods that analyze unlabeled healthcare datasets to identify hidden patterns, patient clusters, disease subtypes, and emerging clinical insights without predefined outcomes.
Others (Reinforcement Learning, Semi-supervised)
Includes advanced AI learning approaches such as reinforcement learning, where agents optimize decisions through feedback from their environment, and semi-supervised learning, which combines labeled and unlabeled data to improve predictive performance.
Natural Language Processing (NLP)
AI technology that enables computers to understand, interpret, generate, and summarize human language from clinical notes, physician-patient conversations, medical literature, and electronic health records.
Smart Assistance
AI-powered virtual assistants and intelligent healthcare agents that automate routine administrative and clinical tasks, provide real-time information, support patient engagement, and assist clinicians during care delivery.
OCR (Optical Character Recognition)
AI technology that converts scanned documents, handwritten notes, prescriptions, laboratory reports, and medical forms into structured, searchable digital data for integration into healthcare information systems.
Auto Coding
AI-driven systems that automatically assign standardized medical codes, such as ICD, CPT, and HCPCS codes, from clinical documentation to streamline billing, reimbursement, and regulatory compliance.
Text Analytics
AI techniques that extract meaningful clinical, operational, and research insights from unstructured textual data, including medical records, scientific publications, pathology reports, and patient communications.
Speech Analytics
AI technology that analyzes spoken interactions between patients and healthcare professionals to generate clinical documentation, identify health indicators, evaluate communication quality, and support decision-making.
Classification & Categorization
AI algorithms that automatically organize and classify healthcare data, medical images, laboratory results, patient records, and clinical documents into predefined categories to improve information retrieval and workflow efficiency.
Context-aware Computing
AI systems capable of interpreting contextual information such as patient history, location, physiological status, clinical workflows, and environmental factors to deliver personalized recommendations and adaptive decision support.
Computer Vision
AI technology that interprets and analyzes medical images, surgical videos, pathology slides, and visual clinical data to support diagnosis, image-guided interventions, disease detection, and workflow automation.
Segment - Application
Revenue capture definition
Medical Imaging
Agentic AI solutions that automate image acquisition, interpretation, anomaly detection, reporting, workflow prioritization, and clinical decision support across radiology, pathology, and other imaging specialties.
Personalized Treatment & Drug Discovery
AI agents that analyze clinical, molecular, and genomic data to identify therapeutic targets, recommend personalized treatment strategies, optimize drug discovery, and accelerate precision medicine research.
Electronic Health Records (EHRs)
Agentic AI deployed to automate documentation, summarize patient records, extract clinical information, improve interoperability, and support data-driven clinical decision-making within EHR systems.
Remote Patient Care
AI agents that continuously monitor patients using connected devices, analyze real-time health data, provide proactive interventions, support telehealth services, and facilitate home-based care management.
Clinical Decision-Making
AI systems that assist clinicians by analyzing patient information, clinical guidelines, laboratory results, and medical evidence to generate diagnostic recommendations, treatment options, and risk assessments.
Risk Prediction & Pandemic Preparedness
AI agents that predict disease progression, identify high-risk populations, forecast outbreaks, optimize resource allocation, and support public health surveillance and emergency response planning.
Genomic Data Analysis
AI platforms that analyze genomic, transcriptomic, and molecular datasets to identify disease-associated variants, biomarkers, therapeutic targets, and patient-specific treatment opportunities.
Chronic Disease Management
AI-driven systems that monitor patients with chronic conditions such as diabetes, cardiovascular disease, COPD, and neurological disorders, enabling personalized interventions and long-term disease management.
Hospital Resource Optimization
AI agents that optimize hospital operations through intelligent scheduling, bed management, staffing allocation, operating room utilization, inventory optimization, and workflow automation.
Medical Research & Data Analysis
AI solutions that accelerate biomedical research by analyzing large clinical datasets, scientific literature, clinical trial information, and real-world evidence to generate research insights and support evidence-based medicine.
Others
Includes applications such as robotic process automation, population health management, medical coding, pharmacovigilance, mental health support, healthcare cybersecurity, revenue cycle management, and patient engagement.
Segment - End Use
Revenue capture definition
Healthcare Providers
Hospitals, physician practices, ambulatory care centers, diagnostic laboratories, and other healthcare organizations that deploy agentic AI to improve clinical workflows, patient care, operational efficiency, and decision-making.
Healthcare Companies
Pharmaceutical, biotechnology, medical device, digital health, and life sciences companies that utilize agentic AI for drug discovery, clinical development, manufacturing optimization, commercialization, regulatory compliance, and research.
Academic & Research Institutes
Universities, academic medical centers, and research organizations employing agentic AI for biomedical research, clinical studies, genomic analysis, healthcare innovation, and medical education.
Healthcare Payers
Public and private health insurance organizations that use agentic AI for claims automation, fraud detection, utilization management, risk assessment, member engagement, reimbursement optimization, and value-based care initiatives.
Others
Includes government health agencies, public health organizations, contract research organizations (CROs), contract development and manufacturing organizations (CDMOs), long-term care facilities, home healthcare providers, and health technology companies adopting agentic AI solutions.
Estimation Model
To learn more about this report, Download Free Sample Report Market-specific Research Models
Consensus-Based Estimates & Forecasting
The agentic AI in healthcare market presents emerging to dominant opportunities, featuring global key players such as nVIDIA; Oracle; GE Healthcare; Microsoft; Thoughtful Automation Inc.; Hippocratic AI Inc.; Cognigy; Amelia US LLC; Beam AI.; Momentum.; Notable.; Springs. To ensure a holistic approach, the GVR research team adopted a comprehensive methodology, considering multiple companies worldwide and utilizing various variables for data accuracy.
Our methodology included:
Model 1: Commodity Flow Analysis - For public players, annual reports were referenced. For private players, premium paid data sources such as S&P Global, Crunchbase, ZoomInfo, RocketReach, etc., were used for the latest revenue insights.
Model 2: Parent Market Analysis
Model 3: Country-level Multivariate Analysis Assumptions
Model 4: Segment-level Multivariate Analysis Assumptions
Model 5: Agentic AI in Healthcare Market CAGR Calculations
Model 1: Commodity Flow Analysis
To learn more about this report, Download Free Sample Report Commodity flow analysis was employed to track the movement of agentic AI in healthcare through the value chain, from technology providers to end users. This analysis provided insights into demand generation points, intermediary roles, and ultimate consumption patterns.
Delivered Customizations
This report has been delivered with the following In-depth customizations
Client Request
Customization Delivered
Value Adds
Agentic AI Adoption & Autonomous Healthcare Workflow Analysis
Developed a tailored analysis of the global Agentic AI in Healthcare market focused on autonomous AI agents supporting clinical decision-making, administrative automation, patient engagement, care coordination, revenue cycle management, clinical documentation, and healthcare operations. The study assessed adoption trends across hospitals, health systems, physician practices, payers, life sciences organizations, and digital health providers while evaluating workforce shortages, increasing healthcare data complexity, AI maturity, and demand for intelligent workflow automation driving market growth.
Enables stakeholders to understand evolving autonomous AI adoption trends, identify high-growth healthcare applications, assess workflow automation opportunities, and evaluate the impact of agentic AI on operational efficiency, clinician productivity, patient outcomes, and long-term market expansion.
Autonomous AI Agents, Clinical Intelligence & Decision Support Assessment
Delivered a customized evaluation of Agentic AI utilization across virtual clinical assistants, autonomous patient scheduling, ambient documentation, prior authorization, care navigation, revenue cycle automation, clinical decision support, patient triage, and personalized patient communication. The analysis assessed advancements in large language models (LLMs), multimodal AI, natural language processing (NLP), AI orchestration frameworks, generative AI, and intelligent agent collaboration. Additionally, it evaluated technology innovations and implementation strategies from leading companies including NVIDIA, Microsoft, Oracle, Hippocratic AI, Cognigy, Amelia US LLC, Thoughtful Automation, Beam AI, Momentum, Notable, and Springs.
Provides actionable insights into commercially attractive autonomous AI segments, emerging intelligent agent technologies, evolving healthcare provider requirements, and growth opportunities associated with reduced administrative burden, enhanced clinical efficiency, improved patient engagement, accelerated decision-making, and scalable healthcare automation.
Healthcare AI Infrastructure, Governance & Competitive Landscape Assessment
Conducted a focused assessment of the Agentic AI in Healthcare ecosystem, including cloud infrastructure, AI development platforms, healthcare interoperability frameworks, EHR integration, cybersecurity, AI governance, regulatory compliance, and competitive positioning. The analysis evaluated implementation challenges, model validation requirements, data privacy considerations, responsible AI practices, technology adoption barriers, strategic partnerships, and competitive differentiation initiatives among leading market participants such as NVIDIA, Oracle, Microsoft, Thoughtful Automation, Hippocratic AI, Cognigy, Amelia US LLC, Beam AI, Momentum, Notable, and Springs.
Supports strategic planning for autonomous AI deployment, healthcare digital transformation, technology investments, provider partnerships, regulatory compliance, geographic expansion, and market entry strategies by identifying adoption drivers, operational challenges, governance requirements, revenue opportunities, infrastructure needs, and competitive advantages across the global Agentic AI in Healthcare value chain.
Frequently Asked Questions About This Report
Single agent systems segment held the largest market share of 59.2% in 2025
Ready-to-deploy agents segment held the largest market share of 63.2% in 2025
Medical imaging segment held the largest market share of 19.1% in 2025.
Machine learning segment held the largest market share of 35.6% in 2025.
Pharmaceutical & biotechnology companies segment held the largest market share of 31.5% in 2025.
The global agentic AI in healthcare market size was valued at USD 759.6 million in 2025 and is estimated at USD 1,082.8 million for 2026.
The global agentic AI in healthcare market is expected to grow at a CAGR of 48.6% from 2026 to 2033, reaching USD 17,292.3 million by 2033.
Some key players operating in the agentic AI in healthcare market include nVIDIA; Oracle; Thoughtful Automation Inc.; Hippocratic AI Inc.; Cognigy; Amelia US LLC; Beam AI.; Momentum.; Notable.; and Springs.
Key factors driving agentic AI in healthcare market growth include the rising demand for personalized healthcare solutions coupled with technological advancements in AI. The growing integration of AI in disease diagnostics and the increasing shift towards preventive care contribute to market growth. Moreover, the rising automation of repetitive tasks, growing focus on cost and resource optimization, and increasing adoption of enhanced patient care solutions are further fueling market growth.
North America dominated with a 53.9% revenue share in 2025.
About the Author(s)
Healthcare IT Research Team
Healthcare · Healthcare ITThis report was authored by the healthcare it research team at Grand View Research - comprising two research analysts, one senior research analyst, and one industry expert - with specialized expertise in the healthcare it segment of the healthcare industry. All findings are based on proprietary healthcare databases, executive interviews, and regulatory analysis, subject to internal peer review prior to publication.
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