The global dialysis equipment market size was USD 1,4 billion in 2023, and is calculated at

USD 1.8 Billion in 2024.

The market is projected to reach

USD 34.61 Billion by 2034

, and register a revenue

34.4%

over the forecast period (2025-2034).

Premium Insights:

Growth of the Generative Artificial Intelligence (AI) in healthcare market is inclining rapidly since the COVID-19 pandemic and need for more capable and advanced solutions in healthcare. A key factor driving adoption and integration of generative artificial intelligence, also known as generative AI and GenAI, in healthcare is rapidly increasing and growing volume of unstructured healthcare data and need for quick filtering and analysis of these datasets. Generative AI-powered solutions are being used in drug discovery, disease diagnosis and screening, predicting side effects, and repurposing drugs, medical chatbots, virtual health assistants, tailored treatment plan medical simulations, and customer services among others.

Integration of Natural Language Processing (NLP), deep learning, predictive analytics, generative adversarial networks, and transformer neural networks are serving to equip generative AI with enhanced capability to analyze, extract, and provide meaningful insights from large datasets, and with unparalleled speed and accuracy.

Generative AI continues to gain rapid traction and is being increasingly integrated into operations across hospitals and clinics, pharmaceutical companies, medical device manufacturers, and research institutions. It has been proving highly efficient in diagnostic imaging, drug discovery, precision medicine, personalized treatment planning, virtual health assistants, chatbots, and remote patient monitoring, and is also comparatively more cost-effective compared to human workforce in healthcare delivery. It also enables accuracy and speed with automation of repetitive tasks, optimizing workflows, and minimizing overall operational costs. Primary applications of generative AI in healthcare are automatically segmenting organs or anomalies in medical images, which is not only accurate, but also saves time for healthcare professionals. In pathology, generative AI analyzes patterns in medical images to predict or identify pathological conditions, supporting early detection and intervention. Generative AI can also design new drug candidates, which can serve to accelerate development timelines by replacing the conventional manual design process.

Increasing appeal and preference towards personalized medicine and preventive healthcare are also major factors driving increased integration and use of generative AI in healthcare. Use of GenAI for analysis and identification of specific patterns in data and diseases in persons is aiding in development of individualized treatment strategies and approaches. Growth of the personalized medicine and preventive healthcare trends are also supported to a major extent by rising prevalence of chronic diseases and inclining focus across the healthcare sector to encourage early detection and integrate advanced technologies to assist in disease diagnosis and patient care, monitoring, and management. In addition, steady telehealth and remote patient monitoring trends are serving to drive adoption of AI-powered solutions and technologies in the healthcare sector. Acceptance of AI-based virtual health assistants, chatbots, and predictive analytics tools, as well as round-the-clock availability of these online tools are factors expected to continue to drive growth of the generative AI in healthcare market.

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Top Generative Artificial Intelligence (Gen AI) in Healthcare Market Drivers and Trends:

• 
  

  Development of Cutting-Edge Treatments and Solutions:

  
  

  Generative AI has been revolutionizing diagnostics and treatment, as well as aiding healthcare personnel and facilities in enhancing patient care, management, and monitoring, all of which have been improving patient outcomes and enabling value-addition to services being offered. Advancements in technologies and AI-powered algorithms and models has been driving innovation and development of cutting-edge technologies and solutions. Capability to analyze medical images and extract and provide data-driven insights to enhance patient care and streamline operational processes and use in behavioral health monitoring has been transforming medical diagnostics, and enhancing care and treatment. Generative AI is also aiding in development of personalized treatment plans, assisting in predictive analytics, predictive modeling, disease monitoring and assessment, supporting advancements in preventive healthcare, and overall reducing overall healthcare costs.

• 
  

  Accuracy and Optimized Treatment Strategies:

  
  

  Integration of generative AI in healthcare has been increasing steadily in a variety of crucial applications, driven by rapidly increasing patient data volumes and medical data, and rising prevalence of diseases and need for efficient patient diagnosis, treatment, care, and positive outcomes. Capability and accuracy of AI-powered models to analyze large datasets, patient-specific data, disease patterns, symptoms, assist in early disease detection, provide insights in personalized medicine and treatments, and deliver precise and quick insights and solutions are resulting in increasing adoption and integration. The advantages offered are enabling healthcare personnel to achieve improved diagnostic accuracy, create optimized treatment strategies, and also to efficiently allocate and utilize healthcare resources.

• 
  

  Reduced Human Error and Spending:

  
  

  Generative AI is serving to automate repetitive tasks, manage administrative tasks, remove need for manual entry of patient data, enhance scheduling and documentation to health record search, and streamline workflows, all of which reduced overall operational costs to a sizable extent. Automating repetitive tasks can free resources for other crucial tasks and operational needs, reduces spending, speeds up processes and can reduce potential of human error. Also, availability of predictive analytics platforms, virtual health assistants, and chatbots adds value to services, enables patient access to 24/7 services, and supports reliable support framework in the healthcare facility. Besides, integration of NLP-powered conversational EHR solutions enable collection of relevant patient information by asking specific questions in natural language.

• 
  

  Interoperability Across Organizations:

  
  

  Integration of GenAI and advanced medical analytics and Electronic Health Records (EHRs), and adoption of cloud computing can offer enhanced performance and accessibility to data and medical insights for healthcare organizations across a wider network. Generative AI is enhancing capabilities of EHRs and enabling interoperability of patient records, clinical decision support, and co-pilot experiences for clinicians. Also, EHR Interoperability enables better workflows and reduced ambiguity, and allows proper data transfer among EHR systems and healthcare organizations, thereby improving delivery of healthcare.

Generative Artificial Intelligence (Gen AI) in Healthcare Market Restraining Factor Insights

• 
  

  Data Privacy and High Costs for Interoperability:

  
  

  Sharing of sensitive patient data and potential risks of breach or unauthorized access and use are major concerns having an impact on adoption of generative AI in the healthcare sector. Lack of safe and secure interoperability between healthcare systems and organizations is also a key concern. Integration of these solutions into existing systems is also a challenge, and can add up to substantial costs for healthcare organizations.

• 
  

  Resistance to Change and Regulatory Uncertainty:

  
  

  Some healthcare organizations may be hesitant to adopt newer technologies and shift away from traditional healthcare practices and approaches. Also, regulatory uncertainties due to continuously evolving technologies and applications, as well as changes in healthcare policies can have a negative impact on adoption of solutions such as generative AI in healthcare.

• 
  

  Ethical Implications of AI in Healthcare:

  
  

  Concerns related with decision making by artificial intelligence or a machine give rise to ethical concerns and considerations. Accountability, transparency, and fairness related to treatment decisions and recommendations are also major factors raising concern associated with negative or positive outcomes of patients. Thes factors can have impact on adoption to a major extent.

Generative Artificial Intelligence (Gen AI) in Healthcare Market Opportunities

• 
  

  Innovation in Imaging Diagnostics:

  
  

  Need for more accuracy in diagnostic imaging and analytics can present opportunities for leading companies in this segment to integrate and enhance generative AI capabilities to address this need. Enhancing accuracy of medical imaging and analysis and early disease detection and diagnostics can enable companies to expand offerings of advanced imaging technologies and solutions and drive revenue streams.

• 
  

  Leveraging Telehealth Trends:

  
  

  Capitalizing on the rising appeal of telehealth services and traction of the digital transformation in healthcare through development of more advanced and responsive and natural sounding virtual health assistants and chatbots can serve to address rising needs in remote patient monitoring and telemedicine platforms.

• 
  

  Drug Discovery and R&D Collaborations:

  
  

  Companies can focus on more streamlined applicability of generative AI in drug discovery and also focus on addressing specific needs among pharmaceutical companies through collaborations and partnerships to speed up research and development processes and accelerate introduction of novel treatments in the market.

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Generative Artificial Intelligence (Gen AI) in Healthcare Market Segmentation:

By Technology:

• Natural Language Processing (NLP)
• Deep Learning
• Predictive Analytics
• Generative Adversarial Networks
• Transformer Neural Networks

By Component:

• Solutions
• Services

By Modality:

• Text
• Image
• Video

By Application:

• Diagnostic Imaging
• Drug Discovery
• Precision Medicine
• Personalized Treatment Planning
• Virtual Health Assistants
• Chatbots
• Remote Patient Monitoring

By End-Use:

• Hospitals and Clinics
• Pharmaceutical Companies
• Medical Device Manufacturers
• Research Institutions

Segment Insights:

Among the technology segments, the deep learning segment is expected to account for largest revenue share over the forecast period. Deep learning has emerged as a key technology in generative AI due to ability to process vast amounts of unstructured data, identify patterns, and generate highly sophisticated outputs. Deep learning is proving highly capable in a number of AI innovations, including enhancing generative capabilities in natural language processing and image creation. Versatility across industries, ranging from healthcare to entertainment, and capability to generate text, create images, and digital content, and increasing use in diagnostics and customer services are some of the transformative possibilities deep learning offers among others. Advancements in deep learning in AI models is also supported by advancements in hardware such as GPUs and TPU, and software frameworks, making the deployment of AI solutions more scalable and efficient, and also opens up further avenues for deployment and broader adoption across sectors.

By Component:

Among the component segments, the solutions segment are expected to dominate in terms of revenue share over the forecast period. Increasing adoption of GenAI solutions across a variety of businesses and sectors for specific needs, such as enhancing automation, improving decision-making, and generating content, is expected to continue to support growth of this segment. These solutions are often packaged as standalone products or integrated systems that address key business challenges, such as generative AI-powered chatbots, image generation tools, and automated data analytics platforms. AI tools are also gaining traction to streamline workflows and improve productivity across industries, including healthcare, manufacturing, and marketing.

By Modality:

Among the modality segments, the text generation segment is expected to account for largest revenue share over the forecast period. Adoption of text-based applications such as language translation, chatbot development, and automated content creation has been registering significant incline. AI-powered platforms for communication and content generation have exhibited significant success with large language models, such as GPT, proving capability to produce human-like text, which has a wide range of applications in customer service, marketing, education, and content generation. Growth of the text generation segment is also expected to continue to incline rapidly as more businesses adopt these tools to scale content creation and enhance user experiences through conversational AI.

By Application:

Among the application segments, the chatbots segment is expected to account for largest revenue share over the forecast period. Chatbots powered by generative AI are being rapidly integrated across sectors and industries, particularly in customer service, e-commerce, and healthcare. AI-driven chatbots are proving highly effective in handling customer queries, reducing response time, and providing personalized interactions. Also, ability to function 24/7, offer multilingual support, and handle increasingly complex conversations makes these solutions indispensable for businesses aiming to improve customer experience while reducing operational costs. In addition, rapid advancements in NLP and AI-driven dialogue systems is expected to further enhance capabilities of chatbots, enabling more natural and engaging user interactions.

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By End-Use:

Among the end-use segments over the forecast period, the hospitals and clinics segment is expected to account for largest revenue share. This can be attributed to significant digital transformation across the healthcare industry, with generative AI playing a crucial role in enhancing diagnostic accuracy, personalizing patient care, and improving operational efficiency. Reliance and integration of generative AI chatbots and virtual assistants, as well as ML, deep learning, and data analytics for image analysis, predictive diagnostics, and personalized treatment plans in the healthcare sector has been increasing steadily. Ability to analyze large datasets quickly and accurately is especially valuable in radiology, pathology, and genomics, where rapid and precise diagnostics are crucial. In addition, increasing use of AI in remote patient monitoring and telehealth solutions, which gained significant importance and use particularly during the COVID-19 pandemic, has served to accelerate adoption of generative AI in healthcare facilities and hospitals and clinics.

Regions and Countries

North America

• United States
• Canada
• Mexico
  

Europe

• Germany
• United Kingdom
• France
• Italy
• Spain
• Rest of Europe

Asia Pacific

• China
• Japan
• India

Rest of Asia Pacific

• Latin America
• Brazil
• Argentina

Rest of Latin America

• Middle East & Africa
• Saudi Arabia
• South Africa
• United Arab Emirates
• Israel
• Rest of MEA

Generative Artificial Intelligence (Gen AI) in Healthcare Market Regional Landscape:

Among the regional markets, North America is expected to continue to account for largest revenue share over the forecast period. Technological advancements and innovation, high rate of adoption of generative AI-powered solutions, presence of modern and sophisticated healthcare infrastructure, high investment in healthcare and medical sector, favorable regulatory environment, and large patient pool in the US are key factors supporting growth of the market in the region.

The Europe generative AI in healthcare market is also registering a steady revenue growth rate, with adoption and integration of these solutions being significantly high in Germany and the UK along with presence of modern healthcare facilities and rapidly aging population and need for advanced healthcare solutions to address rising medical needs.

The Asia Pacific generative AI in healthcare market is expected to register a steady and rapid growth rate over the forecast period. Currently, China and Japan lead among the countries in the region with regard to adoption of generative AI solutions in the healthcare sector. However, factors such as steady digitization trend in the healthcare sector, increasing focus on precision medicine, personalized healthcare trends gaining traction, and improving healthcare infrastructure and spending are expected to drive growth of the Asia Pacific market over the forecast period.

Generative Artificial Intelligence (Gen AI) in Healthcare Market

Competitive Landscape:

Company List:

• IBM Watson
• Google LLC (Alphabet Inc.)
• Microsoft Corporation
• NVIDIA Corporation
• Johnson & Johnson
• Siemens Healthineers AG
• General Electric Company (GE Healthcare)
• Philips Healthcare
• Medtronic PLC
• Epic Systems Corporation
• Tempus Labs Inc.
• PathAI
• Aidoc Medical Ltd.
• Arterys Inc.
• CureMetrix

Competitive Landscape:

The competitive landscape in the global Generative-Artificial Intelligence (GenAI) in healthcare market has become intense with existing companies and new entrants engaged in a continuous technological race to develop and introduce solutions and tools with more advanced features and capabilities. Generative AI is being increasingly trained and explored for more enhanced and accurate diagnostic capability, and for drug discovery, imaging, disease diagnosis, drug development, and synthetic data generation, among other.

Leading companies are also engaging in innovation in generative AI, ad besides developing more efficient and advanced solutions for healthcare applications, are also focusing on capabilities for optimizing workflows and enhancing patient care and outcomes. Other strategies include collaborations and partnerships with research institutions and centers and hospitals, and mergers and acquisitions with intent to integrate more advanced technologies into existing portfolios, expand market reach, and drive revenues.

Recent Developments

• October 11, 2024: Microsoft announced it is unveiling a number of artificial intelligence enhancements in Microsoft Cloud for healthcare innovations. This includes new healthcare AI models in Azure AI Studio, new healthcare data capabilities in Microsoft Fabric, and developer tools in Copilot Studio. According to the inputs from the company, innovations include AI-driven capabilities to combine data from EHRs to generate comprehensive insights and support use cases and clinical imaging, Medicare & Medicaid Services claims, social determinants of health and others. A key new feature is conversational data integration and generative AI voice-enabled tool, Nuance’s DAX Copilot, which has been available for a year, but has gained traction in recent months

• June 13, 2024: Cognizant, in partnership with Google Cloud, launched healthcare-specific generative AI solutions to target high-cost workflows, enhancing efficiency, accuracy, and overall healthcare delivery. As part of an expanded partnership announced in August 2023, Cognizant has now launched the first set of healthcare large language model (LLM) solutions on Google Clouds generative AI technology. This includes Vertex AI platform and Gemini models. According to the announcement, these new generative AI solutions and tools are capable of redesigning healthcare administrative processes and improving experiences. Integration of these highly tuned models in an ecosystem characterized by increasing complexity and demands on healthcare systems will streamline administrative processes and accelerate speed of operations while significantly improving quality of care and services delivered to members

Frequently Asked Questions:

Q: What is the global Generative Artificial Intelligence (AI) in healthcare market size in 2024 and what is the projection for 2034?

A: The global Generative Artificial Intelligence (AI) in healthcare market size was calculated at USD 1.8 billion in 2024 and expected to reach USD 34.61 billion 2034

Which regional market accounted for largest revenue share in 2023, and what is the expected trend over the forecast period?

A: North America accounted for largest revenue share in 2023, and is also expected to continue to maintain its lead over the forecast period.

Q: Which are the major companies are included in the global Generative Artificial Intelligence (AI) in healthcare market report?

A: Major companies in the market report are IBM Watson, Google LLC (Alphabet Inc.), Microsoft Corporation, NVIDIA Corporation, Johnson & Johnson, Siemens Healthineers AG, General Electric Company (GE Healthcare), Philips Healthcare, Medtronic PLC, Epic Systems Corporation, Tempus Labs Inc., PathAI, Aidoc Medical Ltd., Arterys Inc., CureMetrix

Q: What is the projected revenue CAGR of the global Generative Artificial Intelligence (AI) in healthcare market over the forecast period?

A: The global Generative Artificial Intelligence (AI) in healthcare market is expected to register a CAGR of 34.4% between 2025 and 2034.

Q: What are some key factors driving revenue growth of the Generative Artificial Intelligence (AI) in healthcare market ?

A: Some key factors driving market revenue growth include increasing disease prevalence, expanding volumes of healthcare data, need for data analytics, increasing initiatives in drug discovery, disease diagnosis, personalized medicine, demand for medical chatbots, virtual health assistants, tailored treatment plan medical simulations, enhanced and accurate disease diagnosis, and adoption of genAI in customer services.