Generative AI in Healthcare Market

USA Generative AI in Healthcare Market

The U.S. market has become a major deployment environment for generative healthcare technology because of its mature electronic health record infrastructure, concentration of cloud-computing companies, strong clinical research ecosystem, and expanding base of specialized healthcare AI vendors. Health systems are integrating ambient intelligence directly into physician workflows to capture conversations, prepare clinical notes, generate patient summaries, and retrieve context from existing records. U.S. hospitals are also adopting multimodal models for imaging, pathology, oncology, precision medicine, and drug-development research. FDA oversight is evolving alongside these deployments, including efforts to improve transparency around medical devices that incorporate foundation models and large language models.

Clinical acceptance is rising because generative AI addresses tasks that consume physician time without eliminating medical oversight. In 2024, 66% of surveyed U.S. physicians reported using healthcare AI for functions that included documentation, discharge instructions, translation, clinical assistance, and chart-related activities. This adoption pattern is supporting competition among EHR companies, hyperscale cloud providers, ambient-intelligence developers, medical-model specialists, and healthcare systems developing proprietary models from institutional data.

Europe Generative AI in Healthcare Market

Europe's Generative AI in Healthcare Market is developing within a regulatory environment that emphasizes privacy, safety, transparency, data governance, and controlled access to health information. Hospitals and research networks are evaluating generative systems for clinical documentation, medical imaging interpretation, population-health analytics, pharmaceutical research, and multilingual patient engagement. European adoption is supported by strong public health systems, digital-health programs, university research networks, diagnostic technology companies, and established medical-device manufacturers.

The European Health Data Space entered into force in 2025 and creates a framework for health-data access, interoperability, research, innovation, and secondary data use across the European Union. This framework can strengthen healthcare model development by enabling controlled access to diverse datasets while maintaining governance requirements. European vendors are consequently focusing on explainability, secure deployment, model monitoring, data localization, and integration with established hospital information systems as prerequisites for wider generative AI deployment.

Generative AI in Healthcare Market Latest Trends

The Generative AI in Healthcare Market is shifting toward ambient intelligence, multimodal medical foundation models, agentic workflows, and deeply integrated clinical assistants. Early healthcare chatbots operated primarily through text prompts, while newer platforms can interpret conversations, documents, medical images, laboratory information, and historical records within a unified clinical context. This capability is encouraging providers to move generative AI from isolated pilots into specialty workflows where physicians can validate outputs before they become part of the medical record.

Ambient clinical documentation is one of the strongest deployment trends. Systems can capture patient-clinician conversations, create structured notes, draft follow-up instructions, retrieve historical information, and prepare workflow actions without requiring clinicians to type continuously during visits. Major platforms are increasingly integrated with electronic health records so generated information remains within established clinical environments instead of creating disconnected applications.

Another trend is the development of open and adaptable medical models. Developers can fine-tune specialized foundation models for radiology, pathology, laboratory interpretation, clinical question answering, medical education, and research applications. Multimodal capability is particularly important because healthcare information rarely exists in one format.

Enterprise-scale generative AI is also expanding beyond physicians into nursing, administration, research, scheduling, patient communication, and operational support. NHS England announced a deployment of Microsoft Copilot access for 505,000 clinicians and support staff, demonstrating how generative platforms can become institution-wide productivity infrastructure rather than limited clinical experiments.

Generative AI in Healthcare Market Dynamics

Generative AI market dynamics are shaped by the simultaneous pressure to increase healthcare productivity and maintain clinical safety. Providers are seeking tools that can reduce repetitive documentation, synthesize fragmented health information, automate administrative communication, and help clinicians navigate complex medical datasets. Pharmaceutical and biotechnology organizations are applying foundation models to molecular design, research synthesis, biomarker exploration, trial operations, and scientific knowledge management.

Adoption, however, depends on confidence in generated outputs. Healthcare organizations require validation processes, human review, auditability, cybersecurity controls, consent policies, and clear governance before models are scaled across clinical settings. Physician attitudes demonstrate this tension, with 88% of surveyed doctors identifying robust safety and efficacy validation as critical to broader AI adoption.

Drivers of Market Growth

Reducing Clinical Documentation and Administrative Workloads.

Administrative burden is a central driver for Generative AI in Healthcare Market adoption because documentation requirements consume substantial physician and nursing capacity. Generative systems can listen to clinical encounters, convert conversations into structured notes, prepare referral letters, summarize records, and draft patient-facing instructions. This enables providers to spend a greater proportion of appointments interacting directly with patients instead of manually entering information.

Clinical assistants are also evolving from transcription tools into contextual workflow systems. They can extract symptoms, medications, treatment discussions, family history, diagnostic evidence, and follow-up tasks while allowing clinicians to review the generated record before approval. Microsoft has reported average time savings of 5 minutes per encounter from its clinical voice technologies, illustrating why ambient documentation has become a significant enterprise adoption case.

The driver extends beyond individual productivity. Standardized documentation can improve continuity between clinical teams, reduce information gaps, support coding workflows, and create structured data for operational analysis. As providers face workforce constraints and rising information volumes, generative AI offers a method for increasing administrative capacity without transferring every repetitive task to additional staff.

Market Restraints

Patient Privacy, Data Security, and Clinical Reliability Requirements.

Generative healthcare systems process highly sensitive information, including diagnoses, medications, medical histories, test results, clinician conversations, and personally identifiable health records. Organizations therefore face stricter privacy expectations than developers operating general-purpose consumer AI services. Data leakage, unauthorized model training, insecure third-party integrations, or unclear data-retention policies can prevent health systems from moving experimental models into production.

Clinical reliability creates an additional restraint. Language models can generate fluent responses that contain omissions, incorrect interpretations, unsupported statements, or context errors. Such limitations require healthcare organizations to design verification workflows rather than assuming generated content is clinically accurate by default.

Physician expectations demonstrate the importance of trustworthy infrastructure. In a recent professional survey, 86% of doctors identified data privacy as critical for broader AI adoption. Vendors must therefore combine model capability with access controls, encryption, audit trails, clinical validation, secure cloud environments, and transparent governance. These requirements lengthen procurement and implementation cycles compared with less regulated software categories.

Market Opportunities

Development of Specialized Multimodal Medical Foundation Models.

A major opportunity lies in models designed specifically for medical text, diagnostic imaging, pathology, laboratory data, genomic information, and longitudinal health records. General-purpose language models can support summarization and communication, but specialized systems can be adapted to clinical terminology, structured health information, disease-specific workflows, and medical imaging tasks. This creates opportunities for technology providers serving radiology, pathology, oncology, cardiology, dermatology, pharmaceutical research, and personalized medicine.

Open medical-model ecosystems can further accelerate innovation by allowing hospitals, universities, startups, and researchers to build purpose-specific tools without training foundation models from the beginning. Google reported participation from over 850 teams in its MedGemma Impact Challenge, with healthcare prototypes spanning critical care, pathology, mental health, and pneumonia management.

The opportunity also extends to countries where physician shortages and specialist access remain significant constraints. Locally adapted generative models can assist with record structuring, multilingual communication, medical education, remote-care workflows, and preliminary information synthesis. Solutions designed around human supervision can expand clinical capacity while preserving professional oversight.

Market Challenges

Integrating Generative AI Safely Into Existing Clinical Workflows.

Healthcare technology adoption depends heavily on workflow compatibility. A technically capable model can still fail commercially if physicians must switch between applications, manually copy data, repeat authentication, or verify poorly formatted outputs. Successful deployments therefore require integration with electronic health records, identity systems, scheduling software, imaging platforms, laboratory systems, and enterprise governance infrastructure.

Human factors are equally important. Physicians must understand when generative tools are appropriate, where model limitations exist, and how responsibility is assigned when AI-generated information contributes to care decisions. Hospitals also need processes for monitoring model performance after deployment because clinical terminology, guidelines, software configurations, and user behavior evolve over time.

Professional participation in technology selection is becoming a key implementation requirement. A physician survey found that 85% of doctors wanted to be consulted or directly involved in decisions concerning healthcare AI adoption. Vendors that neglect clinician involvement risk low utilization even when procurement and technical deployment are successfully completed.

Generative AI in Healthcare SWOT Analysis

Strengths

• Generative AI can transform unstructured clinical conversations into organized documentation while preserving physician review.

• Multimodal models can combine medical text, images, laboratory information, and historical records within a single analytical environment.

• Natural-language interfaces reduce the technical knowledge required for clinicians to interact with complex healthcare datasets.

• Generative tools can be embedded directly into electronic health record workflows rather than forcing clinicians to use standalone applications.

• Solutions represented 71.2% of the component market in the latest published segmentation, reflecting strong demand for deployable software platforms.

Weaknesses

• Generated medical text can contain unsupported statements, omissions, or incorrect contextual interpretations.

• Model performance can vary across specialties, patient populations, languages, institutions, and documentation styles.

• Healthcare deployments depend on extensive integration, validation, governance, and cybersecurity controls.

• Proprietary models may provide limited visibility into training data, decision pathways, or model-update effects.

• Physician concern about AI-related professional skill loss reached 88%, emphasizing the importance of balanced human-machine workflows.

Opportunities

• Specialized models can support precision medicine, digital pathology, diagnostic imaging, clinical research, and molecular discovery.

• Hospitals can deploy generative assistants across physicians, nurses, administrative staff, researchers, and patient-service teams.

• Localized medical models can expand multilingual communication and structured-record capabilities in underserved healthcare environments.

• Agentic AI can connect documentation with scheduling, orders, referrals, patient communication, and workflow coordination.

• Clinical applications held 62.1% of the application market in the latest published segmentation, indicating substantial room for specialized clinical products.

Threats

• Incorrect generated clinical information can create patient-safety risks when users rely on outputs without adequate verification.

• Cyberattacks targeting healthcare AI infrastructure could expose medical information or manipulate model behavior.

• Regulatory requirements may differ substantially across countries, increasing compliance complexity for global deployments.

• Poorly validated AI systems can damage clinician confidence and slow adoption of otherwise useful applications.

• Medical centers represented 44% of end-use demand in published market segmentation, creating concentration risk when hospital procurement cycles slow.

Generative AI in Healthcare Segmentation Analysis

The Generative AI in Healthcare Market can be segmented according to component, function, application, and end use. These dimensions reflect how healthcare organizations purchase AI capabilities, what tasks are automated, where models interact with clinical systems, and which organizations operate the technology. The structure also shows that generative AI is expanding beyond text generation into imaging, clinical research, patient interaction, and administrative workflows.

By Component

Solutions include generative platforms, healthcare-specific models, clinical assistants, conversational systems, documentation software, imaging applications, decision-support tools, and EHR-integrated applications. Services include implementation, model customization, systems integration, workflow redesign, training, validation, support, and managed AI operations. Solutions currently account for 71.2% of the component market, reflecting demand for scalable platforms that can be deployed across multiple clinical workflows.

Healthcare organizations typically begin with a defined software use case and then require service support for integration and governance. Service demand increases when hospitals connect AI systems to proprietary medical records, imaging environments, security architecture, and institution-specific documentation requirements. Consequently, solutions and services operate as complementary layers, with software establishing functional capability and professional services enabling safe enterprise deployment.

By Function

Administrative Process Optimization, Virtual Nursing Assistants, Robot-Assisted AI Surgery, and Medical Imaging Analysis represent distinct functional areas. Administrative process optimization includes documentation generation, record summarization, scheduling support, patient correspondence, workflow automation, and information retrieval. Virtual nursing assistants focus on patient interaction, education, routine guidance, follow-up communication, and nursing workflow support.

Administrative Process Optimization holds 32.9% of the function market, demonstrating the strong commercial importance of reducing non-clinical workload. Medical Imaging Analysis combines generative and analytical models with radiology, pathology, and other visual diagnostic data. Robot-Assisted AI Surgery integrates AI-generated guidance and contextual intelligence with advanced surgical technology. These functions differ in regulatory intensity, with administrative applications generally requiring less clinical validation than systems directly influencing diagnostic or procedural decisions.

By Application

Clinical applications include cardiovascular care, dermatology, infectious disease, and oncology, where generative systems can help organize patient information, support documentation, interpret complex datasets, and assist clinicians in evaluating treatment context. System applications include disease diagnosis, telemedicine, electronic health records, and drug-interaction workflows. These applications connect generative models with broader healthcare infrastructure and operational processes.

The clinical segment holds 62.1% of the application market, supported by demand for AI tools that directly assist patient-care workflows. Oncology and cardiovascular applications benefit from large volumes of imaging, laboratory, pathology, and longitudinal patient data, while dermatology can integrate visual information with clinical histories. System applications provide another adoption route because generative AI can transform unstructured records into summaries and structured information that clinicians can access within existing digital systems.

By End Use

Clinical Research, Medical Centers, Diagnostic Centers, and Others form the primary end-use structure. Medical centers deploy generative AI across clinical documentation, specialist workflows, patient engagement, operational automation, medical education, research, and information retrieval. Clinical research organizations use generative systems for protocol analysis, scientific literature synthesis, trial documentation, participant identification, and structured research workflows.

Medical centers account for 44% of the end-use market, indicating that hospitals and integrated clinical organizations remain the largest institutional deployment environment. Diagnostic centers are particularly relevant for multimodal applications because medical imaging and laboratory information can be combined with patient histories and generated interpretations. Other users include academic institutions, specialized care providers, pharmaceutical organizations, digital-health companies, and healthcare technology developers building generative applications for specific clinical needs.

Regional Analysis

Regional performance differs according to electronic health record penetration, AI infrastructure, healthcare digitization, regulatory policy, cloud adoption, medical research capacity, and access to high-quality clinical datasets. North America has established early leadership through hospital deployments and technology-company concentration, while Europe emphasizes regulated and interoperable health-data infrastructure. Asia-Pacific is expanding through public digital-health systems and large healthcare populations, while Middle East & Africa adoption is concentrated in digitally advanced health systems and targeted modernization programs.

North America

• North America remains the largest regional Generative AI in Healthcare Market, with a 41% share in the latest published regional assessment. The U.S. leads regional adoption through major cloud companies, EHR vendors, academic medical systems, biotechnology firms, medical-device manufacturers, and specialized AI developers. Canada is also expanding clinical AI deployments within hospital networks and research institutions.

The region's strongest commercial use cases include ambient documentation, clinical information retrieval, medical imaging, pathology, patient communication, research assistance, and pharmaceutical development. Health systems increasingly prefer tools integrated with established EHR environments because integration reduces workflow disruption and makes governance easier.

Regulatory attention remains significant. U.S. authorities are refining methods for identifying medical devices that incorporate foundation models and multimodal AI while healthcare organizations are establishing internal governance committees. This combination of high adoption and increasing oversight is encouraging vendors to invest in traceability, validation, privacy, and specialty-specific model performance rather than relying solely on general-purpose model capability.

Europe

• Europe holds 21.2% of the Generative AI in Healthcare Market according to published regional segmentation. Adoption is concentrated in digitally mature systems such as Germany, the United Kingdom, France, the Netherlands, and Nordic healthcare environments. European hospitals are evaluating generative tools for documentation, imaging, clinical research, patient communication, and administrative automation.

Data governance is a major differentiator in Europe. Healthcare providers must evaluate AI systems within established privacy frameworks while preparing for requirements affecting high-risk AI and regulated medical applications. This encourages demand for private-cloud deployment, secure model hosting, transparent auditability, and controlled health-data access.

Europe also benefits from strong medical-device, pharmaceutical, research, and hospital ecosystems. Cross-border health-data initiatives can support model development by improving interoperability while maintaining structured access rules. Vendors capable of combining multilingual functionality with clinical validation and regional compliance are positioned to address fragmented language and regulatory requirements across European healthcare systems.

Asia-Pacific

• Asia-Pacific held 25% of the regional market in a published healthcare AI assessment, reflecting significant adoption momentum across major digital-health economies. China, Japan, India, South Korea, Singapore, and Australia represent important adoption centers, although healthcare infrastructure and regulatory conditions differ substantially between countries.

Large patient populations create demand for AI-enabled documentation, telemedicine, medical-record processing, diagnostic support, multilingual communication, and clinical research automation. Governments are also investing in digital public health infrastructure that can create structured foundations for future generative applications.

Local-language capability is particularly important in this region because healthcare communication spans diverse scripts, dialects, and clinical practices. Developers are therefore focusing on models that can process regional languages while maintaining medical terminology and patient context. Asia-Pacific also has strong electronics, cloud infrastructure, pharmaceutical manufacturing, medical research, and software-development capacity, supporting domestic healthcare AI ecosystems.

Middle East & Africa

• Middle East & Africa accounted for 3% of the Generative AI in Healthcare Market in a published regional assessment. Adoption is concentrated in Gulf healthcare systems, private hospital networks, digitally advanced urban centers, telehealth services, and specialized medical institutions. Saudi Arabia and the United Arab Emirates are notable environments for hospital digitization and healthcare technology investment.

Generative AI opportunities in the region include multilingual patient interaction, clinical documentation, medical education, remote consultations, workforce support, and health-record summarization. These applications are relevant where specialist availability is uneven or providers serve highly multilingual populations.

Infrastructure remains less consistent across African markets, limiting large-scale implementation in areas without reliable cloud connectivity, interoperable records, or extensive digitization. However, mobile healthcare, telemedicine, and cloud-based clinical platforms provide potential deployment pathways that do not require every provider to maintain advanced local computing infrastructure.

Competitive Landscape

Competition in the Generative AI in Healthcare Market spans hyperscale technology vendors, medical-software providers, EHR companies, AI startups, diagnostic specialists, pharmaceutical companies, and healthcare institutions developing internal models. Large technology vendors compete through cloud infrastructure, foundation models, developer platforms, healthcare data services, and enterprise integration. Specialized companies differentiate through clinical workflows such as ambient documentation, pathology, imaging, medical research, or specialty-specific decision support.

NVIDIA holds an estimated 18.6% share in one current competitive market assessment, illustrating the strategic importance of accelerated computing, AI infrastructure, model development frameworks, and healthcare partnerships. Microsoft competes through clinical voice technology, Dragon Copilot, Azure infrastructure, healthcare data platforms, and integration with major EHR environments. Google focuses on medical foundation models, multimodal AI, research platforms, cloud services, and open medical-model development.

Competition is increasingly determined by workflow integration rather than raw model performance alone. Healthcare buyers evaluate clinical accuracy, specialty coverage, cybersecurity, implementation support, governance, EHR compatibility, data handling, and post-deployment monitoring. Specialized vendors can therefore compete effectively with larger companies when they solve a specific healthcare workflow with stronger clinical integration.

Partnerships are becoming central to competitive strategy. Technology vendors increasingly work with hospitals, academic centers, pharmaceutical companies, imaging providers, and EHR vendors to validate products in real-world care environments. This collaboration model helps developers access clinical expertise while enabling healthcare institutions to shape products before enterprise deployment.

List of Top Generative AI in Healthcare Companies

• NVIDIA Corporation (U.S.)

• Google LLC (U.S.)

• Microsoft Corporation (U.S.)

• IBM (U.S.)

• Oracle Corporation (U.S.)

• OpenAI (U.S.)

• Amazon Web Services (U.S.)

• Johnson & Johnson (U.S.)

• Tencent Holdings Ltd. (China)

• Siemens AG (Germany)

• Epic Systems Corporation (U.S.)

• Tempus AI (U.S.)

• PathAI (U.S.)

• Neuralink Corporation (U.S.)

• Abridge AI (U.S.)

Leading Companies by Market Share

• NVIDIA Corporation: NVIDIA holds an estimated 18.6% share within a current competitive assessment of the Generative AI in Healthcare Market, supported by accelerated computing infrastructure, medical AI frameworks, digital pathology partnerships, drug-discovery platforms, and healthcare-focused foundation-model development.

• Microsoft Corporation: Microsoft holds an estimated 15.8% share within the same competitive assessment, supported by Azure, Nuance clinical technologies, Dragon Copilot, healthcare data services, and deep integration with enterprise clinical workflows.

Investment Trends & Opportunities

Investment in the Generative AI in Healthcare Market is increasingly directed toward companies that can demonstrate measurable workflow adoption rather than general AI experimentation. Ambient clinical intelligence has become a major investment category because documentation burden is widespread, implementation outcomes can be measured, and products can integrate directly with existing electronic medical records. Capital is also moving toward medical foundation models, clinical agents, pathology AI, imaging systems, computational biology, synthetic medical data, personalized medicine, and pharmaceutical research platforms.

Abridge raised a $250 million funding round alongside expansion of its clinical conversation platform across major health systems, illustrating investor interest in enterprise-scale clinical AI adoption. Investors are increasingly evaluating access to clinical distribution, EHR integration, healthcare data governance, retention among clinicians, measurable administrative savings, and evidence of successful deployment across multiple specialties.

Another opportunity is infrastructure for healthcare model development. Hospitals and research organizations require secure computing, model orchestration, evaluation frameworks, data pipelines, and monitoring systems before they can train or adapt clinical models. This creates investment potential across the entire AI stack rather than only patient-facing applications.

Localized healthcare AI represents another opportunity. Models designed for regional languages, medical systems, clinical protocols, and data standards can address markets where U.S.-centric products provide limited coverage. Investment is therefore expanding toward platforms capable of secure customization without requiring institutions to build generative AI systems from the ground up.

Product Innovation & Development

Product development is moving from generic conversational assistants toward specialized systems that combine clinical context, medical data, multimodal reasoning, and workflow automation. Healthcare AI products increasingly support ambient documentation, automated note preparation, clinical evidence summarization, referral drafting, patient instructions, order preparation, and information retrieval inside established medical software. This reduces the need for clinicians to move between separate applications.

Medical foundation models represent another major innovation area. Google's Cell2Sentence-Scale model uses a 27-billion-parameter architecture for single-cell analysis and was developed to support biological research into disease mechanisms and therapeutic pathways. Other multimodal healthcare models can interpret radiology images, pathology slides, laboratory reports, clinical text, and longitudinal records.

Product innovation is also shifting toward healthcare agents capable of completing connected tasks rather than producing isolated text outputs. Future systems can combine conversation capture with record review, documentation, orders, scheduling, patient follow-up, and operational coordination while maintaining human approval points.

Developers are placing greater emphasis on grounding, citations, model evaluation, specialty-specific tuning, and healthcare safeguards. These features are essential because clinical users must understand the basis of generated information before using it in care. Product differentiation will therefore depend increasingly on trust, integration, and medical specialization rather than model size alone.

Recent Developments (2023–2026)

• In June 2026, Google supported India's National Health Authority in applying Gemma technology and its Medical Data Toolkit to structure health records within the redesigned Aarogya Setu platform.

• In March 2026, Microsoft introduced expanded Dragon Copilot capabilities designed to connect clinical intelligence, organizational work context, nursing workflows, radiology functions, and partner-built healthcare agents.

• In October 2025, Google DeepMind introduced a Gemma-based single-cell foundation model designed to help researchers investigate biological responses and potential cancer-treatment pathways.

• In June 2025, Abridge expanded its clinical conversation technology into inpatient workflows and introduced capabilities for preparing outpatient orders within integrated Epic environments.

• In March 2025, Microsoft launched Dragon Copilot by combining clinical voice dictation, ambient intelligence, generative AI, and healthcare-specific safeguards within a unified clinician workflow assistant.

• In January 2025, NVIDIA announced healthcare and life-science collaborations focused on generative AI, genomics, digital pathology, clinical research automation, biological modeling, and AI-enabled medical workflows.

Scope of the Generative AI in Healthcare Market Report

The Generative AI in Healthcare Market report examines the development, adoption, commercialization, and competitive structure of generative technologies used across healthcare and life sciences. Coverage includes foundation models, generative platforms, clinical assistants, multimodal medical AI, ambient intelligence, medical-image analysis, workflow automation, conversational healthcare systems, and implementation services. The report evaluates how these technologies interact with hospitals, diagnostic organizations, researchers, pharmaceutical companies, clinicians, and healthcare information systems.

Component analysis covers solutions and services. Functional analysis evaluates Virtual Nursing Assistants, Robot-Assisted AI Surgery, Administrative Process Optimization, and Medical Imaging Analysis. Application coverage includes clinical use cases across cardiovascular care, dermatology, infectious disease, and oncology, together with system applications involving diagnosis, telemedicine, electronic health records, and drug-interaction workflows. End-use analysis includes Clinical Research, Medical Centers, Diagnostic Centers, and other healthcare organizations.

The report also assesses regulatory requirements, data privacy, cybersecurity, clinical validation, interoperability, model governance, healthcare workforce acceptance, infrastructure requirements, and integration barriers. Competitive analysis examines established technology groups and specialized healthcare AI developers according to model capability, distribution, partnerships, clinical integration, computing infrastructure, and product specialization.

Regional analysis evaluates adoption across North America, Europe, Asia-Pacific, and Middle East & Africa. The report additionally reviews investment patterns, product development priorities, multimodal healthcare models, enterprise adoption, clinical documentation, agentic systems, and medical AI partnerships shaping the Generative AI in Healthcare Market.