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.

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.

According to a study conducted globally in 2022, ** percent of people surveyed stated that they are willing to trust the application of AI in healthcare for diagnosis and treatment, while a further ** percent were ambivalent. This high level of willingness to trust AI in healthcare could reflect the significant immediate benefits that improved medical diagnosis and treatment precision provide for patients, along with the high levels of trust in doctors in most nations. Adoption and market growth While public trust in healthcare AI is increasing, adoption within medical organizations varies. A 2022 survey found that 16 percent of healthcare organizations had implemented AI models for less than 2 years, while ** percent were still evaluating use cases. Notably, ** percent were not actively considering AI solutions. Despite this mixed adoption, the AI healthcare market is poised for significant growth, with robot-assisted surgery alone forecast to reach ** billion U.S. dollars by 2026. Generational and regional differences Attitudes toward AI in healthcare differ across age groups and regions. Younger generations are more optimistic about AI's potential to improve their personal health in the near future compared to baby boomers. Geographically, trust in AI for medical use varies significantly, with Chinese clinicians showing the highest confidence at ** percent, compared to less than ** percent of the clinicians surveyed in the United States.

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Artificial Intelligence (AI) Market in Healthcare Size 2025-2029

The artificial intelligence (AI) market in healthcare size is forecast to increase by USD 30.23 billion, at a CAGR of 33.1% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing demand for digitization in healthcare services. AI-based tools are increasingly being adopted to improve efficiency, accuracy, and patient outcomes in various healthcare applications. One of the most promising areas for AI in healthcare is elderly care, where these technologies can help address the growing population of aging individuals and their unique healthcare needs. However, the market faces challenges, including skepticism from physicians and providers regarding the reliability and effectiveness of AI solutions.
This reluctance can hinder the widespread adoption of AI in healthcare, necessitating efforts to build trust and demonstrate the tangible benefits of these technologies. Navigating these challenges will be crucial for companies seeking to capitalize on the market's potential and make a lasting impact on the strategic healthcare landscape.

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

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with dynamic applications across various sectors. AI-powered diagnostics leverage machine learning algorithms and deep learning models for improved diagnostic accuracy, while ethics remain a critical consideration in their implementation. Robotic surgery and wearable sensors enhance patient care and enable remote monitoring, contributing to better outcomes and reduced medical errors. Personalized medicine and precision oncology benefit from data analytics platforms and big data management, facilitating early disease detection and drug discovery. Hospital information systems optimize workflows and ensure data integration, security, and privacy. Model validation and data validation are essential for maintaining model accuracy and reducing bias.

AI's role in mental health care and chronic disease management is increasingly significant, with computer vision systems and explainable AI facilitating image recognition and algorithm transparency. Telemedicine platforms and predictive analytics enable cost reduction and increased efficiency, while process optimization and risk stratification improve patient care. The ongoing unfolding of market activities includes the development of AI ethics frameworks, bias mitigation strategies, and data security measures. Natural language processing and data analytics platforms facilitate improved healthcare IT infrastructure, enabling more effective clinical decision support and patient privacy protection. Continuous advancements in AI technology and its integration into healthcare systems promise to revolutionize the industry, offering significant benefits for patients and healthcare providers alike.

How is this Artificial Intelligence (AI) in Healthcare Industry segmented?

The artificial intelligence (AI) in healthcare 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.

Application

  Medical imaging and diagnostics
  Drug discovery
  Virtual assistants
  Operations management
  Others


Component

  Software
  Hardware
  Services


End-user

  Hospitals and clinics
  Research institutes and academies


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Application Insights

The medical imaging and diagnostics segment is estimated to witness significant growth during the forecast period.

Medical imaging, a crucial aspect of healthcare, involves creating visual representations of the human body for clinical analysis and diagnosis. Radiology, the science behind this process, encompasses techniques such as X-rays, CAT scans, and MRIs. However, managing vast amounts of high-resolution medical imaging data for effective treatment and diagnosis is a significant challenge for even large healthcare institutions and experienced professionals. The increasing volume of data and the need for radiologist efficiency have led to the adoption of Artificial Intelligence (AI) in medical imaging. AI technologies like natural language processing, machine learning algorithms, deep learning models, and image recognition are employed to enhance diagnostic accuracy, reduce medical errors, and improve efficiency.

Furthermore, AI aids in data integration, model

Introduction

Global Generative AI in Healthcare Market size is expected to be worth around US$ 17.2 Billion by 2032 from US$ 1.1 Billion in 2023, growing at a CAGR of 37% during the forecast period from 2024 to 2032. In 2022, North America led the market, achieving over 36.0% share with a revenue of US$ 0.2 Billion.

Generative AI is enhancing medical imaging, aiding clinical decisions, and streamlining operations. Its application in virtual nursing assistants could save healthcare providers up to USD 20 billion annually. Additionally, its integration into clinical settings, including diagnostics, telemedicine, patient care management, and telehealth applications, has secured its top market share.

However, challenges such as data privacy concerns, the need for high-quality data sets, and sophisticated infrastructure may hinder its growth. Balancing AI’s potential benefits with these challenges is crucial for sustainable market expansion.

Recent developments illustrate the dynamic nature of this market, with major investments and collaborations focused on harnessing GPT-4 and other advanced AI technologies for healthcare applications. Microsoft Corp. and Epic Systems Corp. recently collaborated to integrate generative AI into electronic health records to increase patient outcomes and effectiveness of healthcare delivery.

North America has led in terms of healthcare infrastructure and adoption rate of new technologies; while Asia Pacific appears poised for explosive growth as technological innovations meet rising healthcare demands and supportive government initiatives.

At present, the market for generative AI in healthcare is at an important juncture, only just beginning to realize its full potential. Projected growth highlights a shift toward more AI-integrated healthcare solutions which promise increased efficiency, better patient outcomes and significant economic advantages.

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.

The global AI in healthcare market is projected to grow significantly, registering a CAGR of 33.7% from 2024 to 2031, with market value increasing from $19.27 Billion in 2023 to $189.55 Billion by 2031.

Generative AI in healthcare market is estimated to be USD 3.3 billion in 2025 and is projected to reach USD 39.8 billion by 2035, representing a CAGR of 28%.

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.

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 convergence of big data, artificial intelligence (AI), and the rising adoption of electronic health records (EHRs) is fueling this expansion. A projected Compound Annual Growth Rate (CAGR) of 20% (a reasonable estimate given the rapid technological advancements in this space) suggests a market valued at approximately $2 billion in 2025, with a forecast to reach $6 billion by 2033. Key drivers include the need for improved clinical decision support, accelerated drug discovery, personalized medicine initiatives, and enhanced patient care through more effective data analysis. The market is segmented by deployment (cloud, on-premise), application (drug discovery, clinical research, diagnostics), and end-user (hospitals, pharmaceutical companies, research institutions). Leading companies such as Raapid, Datavid, Wisecube AI, Cambridge Semantics, Ontotext, and Elsevier are actively shaping the market landscape through innovative solutions and strategic partnerships. However, challenges like data interoperability issues, data privacy concerns, and the high cost of implementation pose restraints to widespread adoption. The market's trajectory indicates significant opportunities for growth across various segments. The cloud-based deployment model is expected to dominate due to its scalability and cost-effectiveness. The pharmaceutical industry's increasing reliance on data-driven insights for drug discovery and development is a significant growth catalyst. Similarly, the integration of medical knowledge graphs into diagnostic tools promises improved accuracy and efficiency. The North American market currently holds the largest share, followed by Europe, driven by strong technological adoption and regulatory support. However, emerging markets in Asia-Pacific are poised for significant growth as healthcare infrastructure improves and digitalization efforts accelerate. The forecast period (2025-2033) will witness a continuous rise in market penetration, driven by innovation and increasing awareness of the benefits of knowledge graphs in optimizing healthcare operations and outcomes.

Artificial intelligence (AI) in healthcare market is expected to grow from USD 22.61 bn in 2024 to USD 35.71 bn in 2025 and USD 765.12 bn by 2035, at CAGR of 35.9%.

Explore North America Artificial Intelligence in Healthcare Market, including size, share, growth, trends, and industry analysis, with forecasts extending to 2033.

The China artificial intelligence (AI) in healthcare market size reached USD 1,285.8 Million in 2024. Looking forward, IMARC Group expects the market to reach USD 8,593.3 Million by 2033, exhibiting a growth rate (CAGR) of 23.50% during 2025-2033. The growing geriatric population in the country, increasing innovations in machine learning (ML), deep learning, and natural language processing (NLP), and rising implementation of favorable government regulations crucial factors impelling the growth of the market.

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2025-2033. Our report has categorized the market based on offering, technology, application, and end user.

Background:The infodemic we are experiencing with AI related publications in healthcare is unparalleled. The excitement and fear surrounding the adoption of rapidly evolving AI in healthcare applications pose a real challenge. Collaborative learning from published research is one of the best ways to understand the associated opportunities and challenges in the field. To gain a deep understanding of recent developments in this field, we have conducted a quantitative and qualitative review of AI in healthcare research articles published in 2023.Methods:We performed a PubMed search using the terms, “machine learning” or “artificial intelligence” and “2023”, restricted to English language and human subject research as of December 31, 2023 on January 1, 2024. Utilizing a Deep Learning-based approach, we assessed the maturity of publications. Following this, we manually annotated the healthcare specialty, data utilized, and models employed for the identified mature articles. Subsequently, empirical data analysis was performed to elucidate trends and statistics. Similarly, we performed a search for Large Language Model(LLM) based publications for the year 2023.Results:Our PubMed search yielded 23,306 articles, of which 1,612 were classified as mature. Following exclusions, 1,226 articles were selected for final analysis. Among these, the highest number of articles originated from the Imaging specialty (483), followed by Gastroenterology (86), and Ophthalmology (78). Analysis of data types revealed that image data was predominant, utilized in 75.2% of publications, followed by tabular data (12.9%) and text data (11.6%). Deep Learning models were extensively employed, constituting 59.8% of the models used. For the LLM related publications,after exclusions, 584 publications were finally classified into the 26 different healthcare specialties and used for further analysis. The utilization of Large Language Models (LLMs), is highest in general healthcare specialties, at 20.1%, followed by surgery at 8.5%.Conclusion:Image based healthcare specialities such as Radiology, Gastroenterology and Cardiology have dominated the landscape of AI in healthcare research for years. In the future, we are likely to see other healthcare specialties including the education and administrative areas of healthcare be driven by the LLMs and possibly multimodal models in the next era of AI in healthcare research and publications.Data Files Description:Here, we are providing two data files. The first file, named FinalData_2023_YIR, contains 1267 rows with columns including 'DOI', 'Title', 'Abstract', 'Author Name', 'Author Address', 'Specialty', 'Data type', 'Model type', and 'Systematic Reviews'. The columns 'Specialty', 'Data type', 'Model type', and 'Systematic Reviews' were manually annotated by the BrainX AI research team. The second file, named Final_LLM_2023_YIR, consists of 584 rows and columns including 'DOI', 'Title', 'Abstract', 'Author Name', 'Author Address', 'Journal', and 'Specialty'. Here, the 'Specialty' column was also manually annotated by the BrainX AI Research Team.

The India AI in healthcare market size was worth around USD 0.83 billion in 2023 and is predicted to grow to around USD 17.75 billion by 2032

In 2023, ** percent of surveyed healthcare leaders were planning to invest in artificial intelligence (AI) to predict outcomes and for clinical decision support, an increase from ** and ** percent in 2021, respectively. Meanwhile, the share of leaders investing in AI to optimize operational efficiency and to integrate diagnostics did not change over the past three years.

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.

Introduction

Global AI In Medical Imaging Market size is expected to be worth around USD 14826.8 Billion by 2032 from USD 1,015.6 Billion in 2023, growing at a CAGR of 34.7% during the forecast period from 2023 to 2032. In 2023, North America led the market, achieving over 45.2% share with a revenue of USD 380.8 Million.

Artificial Intelligence (AI) is rapidly emerging as a transformative technology across multiple sectors, particularly in healthcare. It is set to revolutionize medical diagnosis and treatment, with diagnostic medical imaging being a key area of focus. Imaging modalities such as X-rays, MRIs, and CT scans produce vast amounts of data, often overwhelming healthcare professionals with the complexity and volume of analysis. By incorporating AI, these challenges can be mitigated, leading to significant improvements in diagnostic accuracy and efficiency.

AI leverages advanced algorithms to process and analyze complex imaging data, making it an invaluable clinical tool in diagnostic imaging. The technology is increasingly being refined to assist in detecting and assessing a variety of medical conditions. Studies in computer-aided diagnostics consistently highlight AI's ability to deliver high levels of specificity, sensitivity, and accuracy. These capabilities are especially impactful in identifying subtle radiographic changes, which can have substantial implications for public health outcomes.

Research into AI's role in diagnostic medical imaging continues to expand. AI has shown remarkable potential in identifying imaging abnormalities and enhancing the precision of tissue-based diagnoses and characterizations, offering a promising future for improved healthcare delivery.