AI in Healthcare - Quick Overview Statistics

Artificial Intelligence in healthcare refers to the use of advanced computer algorithms and machine learning techniques to analyze data in the healthcare sector to provide better healthcare services.

AI helps healthcare providers make more accurate and real-time diagnoses, personalize treatment plans, and improve patient safety by identifying health risks earlier.

Types of AI Applications in Healthcare Statistics

• Medical imaging analysis
• Natural language processing (NLP)
• Disease prediction and risk assessment
• Virtual Assistants and Chabot’s
• Drug discovery and development
• Robot-assisted surgery
• Patient engagement
• Diagnosis and treatment
• Machine learning

AI in Healthcare Statistics: AI in healthcare has been a hot topic for the past few years, and the report says that the industry is expected to reach $187.95 billion by the end of 2030. The fact of this platform in 2023 suggests a huge boom in the market size worldwide, with a compound annual increase rate (CAGR) of 40.1% from 2023 to 2030. The worldwide Artificial intelligence in the healthcare marketplace length changed into worth $20.65 billion in 2023 which has increased from last year. These AI in Healthcare Statistics include insights from various aspects and sources that will provide effective light on the importance of AI in the healthcare industry around the world in recent times. In 2023, the Market share records the gradual adoption of AI which is advancing the sector, and has been observed that 85% of organizations have already implemented AI. Additionally, 1/2 of the executives claimed that AI is indicating a tremendous shift inside and outside the industry. Aid of AI-based healthcare companies used solutions like telemedicine and remote tools and sensors backed by means of large information that can reduce healthcare charges improve access, and promote better outcomes, and performance. Key Takeaways According to AI in Healthcare Statistics, the platform when implemented Artificial Intelligence has experienced a huge increase, with a CAGR of 40.1% from 2023 to 2030 and a global market size expected to attain $187.95 billion by 2030. Around the world, approximately 40% of healthcare industries are regularly using AI and Machine Language in the sector. In 2023, Healthcare executives are increasingly adopting AI in their techniques, and nearly 1/2 of the executives surveyed are already using it. This is being adopted globally, with answers like telemedicine and faraway tools and sensors backed through huge information that could lessen healthcare charges and equitably improve admission to, results, and performance.

This statistic shows the global market size for artificial intelligence in healthcare in 2016, 2017 and a forecast for 2025. It is estimated that over this period the market will increase from roughly one billion to more than 28 billion U.S. dollars.

According to a survey of business leaders in the healthcare industry in the United States in 2021, ** percent of respondents reported having concerns that AI in healthcare could lead to threats to security and privacy. A further ** percent were worried that AI could have safety issues, while ** percent had concerns surrounding machine bias.

The Medical Knowledge Graph market is experiencing robust growth, driven by the increasing need for efficient data management and analysis within the healthcare sector. The rising volume of patient data, coupled with advancements in artificial intelligence (AI) and machine learning (ML), is fueling the adoption of knowledge graphs to improve clinical decision-making, research, and drug discovery. We estimate the market size in 2025 to be approximately $250 million, with a Compound Annual Growth Rate (CAGR) of 15% projected from 2025 to 2033. This growth is attributed to several key drivers, including the rising prevalence of chronic diseases demanding personalized medicine approaches, the increasing emphasis on interoperability between healthcare systems, and the growing demand for improved patient outcomes through data-driven insights. The market is segmented by application (e.g., clinical decision support, drug discovery, public health surveillance) and type (e.g., cloud-based, on-premise). Major players are actively investing in research and development to enhance the capabilities of their Medical Knowledge Graph solutions, further driving market expansion. The restraints to market growth include concerns around data privacy and security, the complexity of integrating knowledge graphs with existing healthcare IT infrastructure, and the need for skilled professionals to manage and interpret the vast amounts of data generated. However, ongoing advancements in data security technologies and the development of user-friendly interfaces are mitigating these challenges. Geographically, North America currently holds a significant market share, driven by early adoption and robust technological advancements. However, the Asia-Pacific region is poised for significant growth in the coming years due to increasing investments in healthcare infrastructure and rising healthcare expenditure. The forecast period of 2025-2033 presents significant opportunities for market players to capitalize on the growing demand for efficient and intelligent data management solutions in healthcare. The market's future is bright, with continuous innovation and expanding applications set to propel its trajectory.

In 2024, the size of artificial intelligence (AI) in the healthcare market in India reached *** million U.S. dollars. It was estimated that in 2025 the value would increase to substantially around *** billion U.S. dollars. The integration of AI is a turning point for medical research, diagnosis, and treatment in India.

Sixty percent of respondents from the pharma and healthcare industry state that deployment of artificial intelligence helps improve quality control. According to 42 percent of respondents, monitoring and diagnosis is another important use case for AI. AI technology helps diagnosing diseases and its algorithms can select treatments accordingly. In addition, it may soon be possible to use AI in this industry to offer patients personalized preventive risk screenings. This opens possibilities to find the most suitable options for patients based on AI-enabled technology.

Market Analysis: Clinical Knowledge Graph Technology The global clinical knowledge graph (CKG) technology market is projected to reach $X million by 2033, exhibiting a CAGR of XX% during the forecast period 2025-2033. Key drivers fueling this growth include the increasing adoption of artificial intelligence (AI) and machine learning (ML) in healthcare, the rising demand for personalized medicine, and the need to improve the efficiency of medical research and development. Other factors contributing to market expansion are the growing awareness of the benefits of CKGs and the increasing availability of healthcare data. The market for CKG technology is segmented based on type (structured and unstructured), application (medical diagnosis and treatment, drug discovery, others), and region (North America, South America, Europe, Middle East & Africa, and Asia Pacific). North America is expected to dominate the market throughout the forecast period due to the high adoption of AI and ML in healthcare and the presence of well-established healthcare infrastructure. The Asia Pacific region is projected to experience the fastest growth during the forecast period due to the increasing healthcare expenditure and the growing awareness of the benefits of CKGs. Key players in the market include Raapid, Datavid, Wisecube AI, Cambridge Semantics, Ontotext, and Elsevier.

IntroductionThe electronic health record (EHR) has greatly expanded healthcare communication between patients and health workers. However, the volume and complexity of EHR messages have increased health workers' cognitive load, impeding effective care delivery and contributing to burnout.MethodsTo understand these potential detriments resulting from EHR communication, we analyzed EHR messages sent between patients and health workers at Emory Healthcare, a large academic healthcare system in Atlanta, Georgia. We quantified the burden of messages interacted with by each health worker type and visualized the communication patterns using graph theory. Our analysis included 76,694 conversations comprising 144,369 messages sent between 47,460 patients and 3,749 health workers across 85 healthcare specialties.ResultsOn average, nurses/certified nursing assistants/medical assistants (nurses/CNA/MA) interacted with the most messages (350), followed by non-physician practitioners (NPP) (241), physicians (166), and support staff (155), with the average conversation involving 10.51 interactions before resolution. Network analysis of the communication flow revealed that each health worker was connected to approximately two other health workers (average degree = 2.10). In message sending, support staff led in closeness centrality (0.44), followed by nurses/CNA/MA (0.41), highlighting their key role in fast information spread. For message reception, nurses/CNA/MA (0.51) and support staff (0.41) also had the highest values, underscoring their vital role in the communication network on the receiving end as well.DiscussionOur analysis demonstrates the feasibility of applying graph theory to understand communication dynamics between patients and health workers and highlights the burden of EHR-based messaging.

The Clinical Knowledge Graph (CKG) technology market is experiencing robust growth, driven by the increasing need for improved healthcare data interoperability, enhanced clinical decision-making, and the rising adoption of precision medicine. The market's expansion is fueled by the ability of CKGs to integrate disparate data sources – electronic health records (EHRs), medical literature, genomic data, and clinical trial results – into a unified, semantically rich knowledge base. This allows healthcare providers to access comprehensive patient information, identify patterns and insights previously obscured by data silos, and ultimately deliver more effective and personalized care. Factors like the growing volume of healthcare data, advancements in artificial intelligence (AI) and machine learning (ML) for knowledge graph construction and querying, and increasing regulatory mandates for data interoperability are all contributing to the market's upward trajectory. While the initial investment in CKG implementation can be significant, the long-term benefits in terms of improved patient outcomes, reduced medical errors, and enhanced operational efficiency are driving widespread adoption across hospitals, research institutions, and pharmaceutical companies. Despite the positive market outlook, challenges remain. Data integration complexities, the need for skilled professionals to manage and maintain CKGs, and concerns about data privacy and security are potential restraints. However, ongoing technological advancements, coupled with the increasing awareness of CKGs' value proposition, are mitigating these challenges. Market segmentation reveals a strong demand across various applications, including clinical decision support systems, drug discovery and development, and public health surveillance. Different CKG types, ranging from those focused on specific diseases to broader, multi-domain knowledge graphs, cater to diverse healthcare needs. The North American market currently holds a significant share, followed by Europe and the Asia-Pacific region, reflecting higher healthcare expenditure and technological advancements in these regions. The market is expected to witness significant expansion across all segments and regions over the forecast period, fueled by continuous innovation and a growing recognition of the transformative potential of CKG technology.

AI in Medical Writing Market size projected to reach USD 2367.1 Billion by 2034, with a 13.3% CAGR during the forecast period. Key players are Cactus Communication, CureMetrix Inc., Elsevier and, others.

The Knowledge Graph market is experiencing robust growth, projected to reach $1503 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 13.7% from 2025 to 2033. This expansion is driven by several key factors. The increasing need for efficient data management and enhanced decision-making across various sectors, including finance, government, and healthcare, fuels the demand for sophisticated knowledge graph solutions. These solutions offer improved data interoperability, enabling organizations to connect disparate data sources and extract valuable insights for strategic planning and operational efficiency. Furthermore, the rise of artificial intelligence (AI) and machine learning (ML) technologies, which heavily rely on structured and interconnected data, is a significant catalyst for market growth. The adoption of cloud-based knowledge graph platforms, offering scalability and cost-effectiveness, further strengthens this trend. The market segmentation reveals a strong demand for both Enterprise Knowledge Graph Platforms and Knowledge Graph-as-a-Service (KaaS) models, catering to diverse organizational needs and technological capabilities. The competitive landscape is dynamic, featuring both established players and emerging innovators. While companies like Iflytek and Tongdun represent significant market participants, the emergence of specialized knowledge graph providers suggests a healthy level of competition and innovation. Geographic distribution indicates a strong presence in North America and Asia Pacific, particularly in the United States and China, which are expected to remain key markets due to their advanced technological infrastructure and substantial investments in data-driven initiatives. However, growth potential exists in other regions as awareness and adoption of knowledge graph technologies increase. The restraints on market growth might include the complexity of implementation, the need for skilled professionals to manage and maintain these systems, and the potential high initial investment costs. However, the long-term benefits of improved data accessibility and business intelligence are expected to outweigh these challenges, ensuring continued market expansion.

Introduction

The Global AI In Life Science Analytics Market is projected to grow from USD 1.7 billion in 2023 to USD 5.6 billion by 2033, at a CAGR of 12.7% during the forecast period. The increasing adoption of AI-driven solutions in research, drug discovery, and healthcare analytics is fueling market expansion. AI technologies enhance data processing capabilities, enabling faster insights and decision-making.

One of the key drivers of this growth is the rising complexity of biomedical data. AI tools, such as machine learning algorithms and knowledge graphs, help researchers analyze vast datasets efficiently. For instance, AI-powered databases have significantly reduced the time required to identify disease-associated genes. This innovation accelerates drug discovery and enhances precision medicine approaches, improving patient outcomes.

Government initiatives and investments are also shaping market dynamics. In the U.S., summits like the AI-Bioscience Collaborative Summit promote international cooperation and private-sector data sharing to advance biotechnology. Similarly, in India, AI-driven healthcare is expected to contribute $25-30 billion to the GDP by 2025. Policies such as the IndiaAI Mission and the Digital Personal Data Protection Act, 2023, support responsible AI integration and data security, ensuring sustainable growth.

International health organizations recognize AI's potential to address global healthcare challenges. The World Health Organization (WHO) emphasizes the need for robust governance structures to ensure safety and equity in AI-driven healthcare. Collaborative efforts and regulatory frameworks are being established to standardize AI applications, promoting ethical adoption in life sciences.

As AI adoption accelerates, the market is witnessing innovations across various applications, including research and development, sales and marketing support, and supply chain analytics. The pharmaceutical, biotechnology, and medical device sectors are leveraging AI to optimize operations and improve efficiency. With continued advancements and regulatory support, AI in life science analytics is poised to transform healthcare and biotechnology in the coming years.

As per Cognitive Market Research's latest published report, the Global AI in Mental Health market size was USD 910.6 Million in 2022 and it is forecasted to reach USD 11,371.0 Million by 2030. AI in Mental Health Industry's Compound Annual Growth Rate will be 37.2% from 2023 to 2030. Market Dynamics of AI in Mental Health Market

Growing adoption of AI to detect mental illness and symptoms:

AI plays a cog role in assessing and diagnosing mental illness symptoms. It is observed that detecting the sign of mental illness was challenging for clinicians and researchers. Mental disorders diagnosis often depends on self-reporting or direct observation of abnormal behaviors and actions. Direct observation is a costly procedure and time-consuming. Nonetheless, AI plays an important role to analyse and diagnose several mental health issues. The emergence of deep learning helps to monitor the probability of improving mental health conditions. With the help of deep learning, health practitioners can study and identify behaviors patterns, and potential warning signs which help physicians to make quick decisions. Stressful or traumatic events and generic mental disorders history are some of the major factors that may lead to mental illness. The data assembly and recognition allow clinical institutions and physicians to analyze the prediction for mental health issues in the patient. More specifically, AI helps to diagnose mental illness symptoms more accurately and quickly so that physicians can provide the right treatment with the right diagnosis. Thus, the emergence of AI and its several benefits in the healthcare industry has augmented market growth.

Restraining Factors of AI in Mental Health Market:

Data privacy and regulatory issues: The advent of new technologies such as Artificial Intelligence and IoT is reshaping the healthcare industry. Technology helps to keep records, monitor and track patient health. It also keeps a record of patient’s personal information and their treatment plans. However, the increasing prevalence of data theft and cyber-attacks is expected to hinder market growth to a certain extent. For instance, in 2020, around 58% of data theft increased in the healthcare industry as compared to the previous year. The governments of several countries have imposed different rules and regulations for adopting new technologies. For instance, in the U.S. companies must comply with HIPAA, GDPR, and other guidelines to launch their products and services. These factors can obstruct market growth.

Current Trends on AI in mental healthcare:

Several advantages associated with AI technology has imposing health specialist to adopt high-tech solutions to treat their patent more efficiently. On the other hand, to emphasize the usage of AI in the healthcare industry, giant players operating in this industry are focusing on product development and innovation followed by technological innovation. Conventional treatment for mental illness comprises medications and patient counseling. However, with the help of AI, health practitioners can monitor patients’ treatment and their medications. In addition. In the depression phase, patients loss their interest and mood in day-to-day activities where AI plays an important role in the treatment process. Thus, the rising need for AI solutions to treat mental illness is projected to propel the market growth over the forecast period, from 2023 to 2030. Introduction of AI in Mental Health

A mental disorder is a medical condition that disturbs a person’s thinking, fexeling, interest, mood, and ability for performing daily activities. Several factors such as trauma or a history of abuse, injury, genetic disorder (biological factors), physical illness, use of alcohol or drugs, generic disorder, and chemical imbalances in the brain are some of the major factors contributing to the development of mental illness. In addition, common signs of mental illness are changes in eating habits, mood swings, excessive worrying or fear, avoiding friends and social activities, and problems concentrating.

In 2021, ** percent of respondents to survey conducted in the United Kingdom reported they would feel uncomfortable using app powered by AI to determine a diagnosis. Although, ** percent of respondents would be comfortable if their doctor used AI to aid a diagnosis or treatment.

In 2023, the large model medical market in China valued at *** million yuan. However, according to the forecast, the industry size was projected to exceed ***** billion yuan by 2030. The maturation of technology and the availability of data are enabling the large model of medical AI to overcome some research and development hurdles in medical scenarios (such as drug discovery, individualized medicine, medical imaging, and data enhancement).

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Cloud Artificial Intelligence (AI) Market Size 2024-2028

The cloud artificial intelligence (ai) market size is forecast to increase by USD 12.61 billion, at a CAGR of 24.1% between 2023 and 2028.

The market is experiencing significant growth, driven by the emergence of technologically advanced devices and the increasing adoption of 5G and mobile penetration. These advancements enable faster and more efficient data processing, leading to increased demand for cloud-based AI solutions. However, the market also faces challenges from open-source platforms, which offer free alternatives to proprietary AI offerings. Companies must navigate this competitive landscape by focusing on providing value-added services and maintaining a strong competitive edge through innovation and differentiation. To capitalize on market opportunities, organizations should explore applications in sectors such as healthcare, finance, and manufacturing, where AI can drive operational efficiency, enhance customer experiences, and generate new revenue streams. Effective strategic planning and a strong focus on data security will be crucial for businesses seeking to succeed in this dynamic and evolving market.

What will be the Size of the Cloud Artificial Intelligence (AI) Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
Request Free SampleThe market continues to evolve, driven by advancements in machine learning (ML), computer vision, and natural language processing. Bias mitigation and responsible AI are increasingly prioritized, with knowledge graphs and explainable AI (XAI) playing crucial roles in ensuring transparency and trust. Agile development and AI ethics are integral to creating ethical and unbiased AI systems. ML models are being applied across various sectors, from fraud detection and sales forecasting to speech recognition and image recognition. Data security and privacy remain paramount, with cloud computing and edge computing solutions offering secure alternatives. Deep learning (DL) and reinforcement learning are advancing rapidly, enabling more sophisticated AI applications. Semantic reasoning and predictive analytics are transforming decision making, while AI-powered chatbots and virtual assistants enhance customer service. Data labeling and model training are essential components of AI development, with API integration streamlining deployment and model training. Risk management and predictive analytics are critical for businesses seeking to mitigate potential threats and optimize operations. The ongoing unfolding of market activities reveals a dynamic landscape, with AI regulations and governance emerging as key considerations. Sentiment analysis and text analytics offer valuable insights into customer behavior and preferences. In the ever-evolving AI ecosystem, continuous innovation and adaptation are essential. The integration of various AI technologies and applications will shape the future of business and society.

How is this Cloud Artificial Intelligence (AI) Industry segmented?

The cloud artificial intelligence (ai) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. ComponentSoftwareServicesGeographyNorth AmericaUSEuropeGermanyUKAPACChinaJapanRest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.Artificial Intelligence (AI) software development is a significant area of innovation in the business world, with applications ranging from automating operations to personalizing service delivery and generating insights. AI technologies, such as machine learning (ML), deep learning (DL), computer vision, speech recognition, and natural language processing, are transforming industries. Responsible AI practices, including bias mitigation and explainable AI (XAI), are crucial for building trust and ensuring fairness in AI systems. Agile development methodologies facilitate the integration of AI capabilities into existing software. Data security and privacy are paramount in AI implementations. Cloud computing and edge computing provide flexible solutions for storing and processing sensitive data. AI regulations, such as those related to data privacy and security, are shaping the market. AI ethics are also a critical consideration, with transparency and accountability essential for building trust in AI systems. AI is revolutionizing various industries, from healthcare to finance and marketing. In healthcare, AI is used for predictive analytics, sales forecasting, and fraud detection, improving patient outcomes and operational efficiency. In finance, AI is used for risk management

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Generative Artificial Intelligence (AI) Market Size 2025-2029

The generative artificial intelligence (AI) market size is forecast to increase by USD 185.82 billion at a CAGR of 59.4% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for AI-generated content. This trend is being driven by the accelerated deployment of large language models (LLMs), which are capable of generating human-like text, music, and visual content. However, the market faces a notable challenge: the lack of quality data. Despite the promising advancements in AI technology, the availability and quality of data remain a significant obstacle. To effectively train and improve AI models, high-quality, diverse, and representative data are essential. The scarcity and biases in existing data sets can limit the performance and generalizability of AI systems, posing challenges for businesses seeking to capitalize on the market opportunities presented by generative AI.
Companies must prioritize investing in data collection, curation, and ethics to address this challenge and ensure their AI solutions deliver accurate, unbiased, and valuable results. By focusing on data quality, businesses can navigate this challenge and unlock the full potential of generative AI in various industries, including content creation, customer service, and research and development.

What will be the Size of the Generative Artificial Intelligence (AI) Market during the forecast period?

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The market continues to evolve, driven by advancements in foundation models and large language models. These models undergo constant refinement through prompt engineering and model safety measures, ensuring they deliver personalized experiences for various applications. Research and development in open-source models, language modeling, knowledge graph, product design, and audio generation propel innovation. Neural networks, machine learning, and deep learning techniques fuel data analysis, while model fine-tuning and predictive analytics optimize business intelligence. Ethical considerations, responsible AI, and model explainability are integral parts of the ongoing conversation.
Model bias, data privacy, and data security remain critical concerns. Transformer models and conversational AI are transforming customer service, while code generation, image generation, text generation, video generation, and topic modeling expand content creation possibilities. Ongoing research in natural language processing, sentiment analysis, and predictive analytics continues to shape the market landscape.

How is this Generative Artificial Intelligence (AI) Industry segmented?

The generative artificial intelligence (AI) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Software
  Services


Technology

  Transformers
  Generative adversarial networks (GANs)
  Variational autoencoder (VAE)
  Diffusion networks


Application

  Computer Vision
  NLP
  Robotics & Automation
  Content Generation
  Chatbots & Intelligent Virtual Assistants
  Predictive Analytics
  Others


End-Use

  Media & Entertainment
  BFSI
  IT & Telecommunication
  Healthcare
  Automotive & Transportation
  Gaming
  Others


Model

  Large Language Models
  Image & Video Generative Models
  Multi-modal Generative Models
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    Spain
    The Netherlands
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

Generative Artificial Intelligence (AI) is revolutionizing the tech landscape with its ability to create unique and personalized content. Foundation models, such as GPT-4, employ deep learning techniques to generate human-like text, while large language models fine-tune these models for specific applications. Prompt engineering and model safety are crucial in ensuring accurate and responsible AI usage. Businesses leverage these technologies for various purposes, including content creation, customer service, and product design. Research and development in generative AI is ongoing, with open-source models and transformer models leading the way. Neural networks and deep learning power these models, enabling advanced capabilities like audio generation, data analysis, and predictive analytics.

Natural language processing, sentiment analysis, and conversational AI are essential applications, enhancing business intelligence and customer experiences. Ethica