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.

Introduction

AI in Healthcare Statistics: Artificial intelligence (AI) is swiftly reshaping the healthcare sector, transforming areas such as diagnostics, treatment planning, patient management, and drug development. By analyzing large volumes of data and delivering precise insights, AI is boosting clinical decision-making, enhancing patient outcomes, and optimizing healthcare operations.

Key advancements in machine learning, natural language processing, and other AI technologies are propelling this shift, with healthcare systems worldwide increasingly adopting these innovations to improve efficiency and offer more personalized care. The ongoing potential of AI to refine healthcare delivery is reshaping the industry's future.

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.

In a sunlit hospital room in San Diego, a pediatrician glances at a screen not to read a chart but to receive a real-time, AI-generated diagnosis that considers thousands of similar cases. This is not fiction, it’s today’s reality. AI has become a silent partner in healthcare, revolutionizing diagnosis, treatment,...

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 valued to increase USD 30.23 billion, at a CAGR of 33.1% from 2024 to 2029. Push for digitization in healthcare will drive the artificial intelligence (AI) market in healthcare.

Major Market Trends & Insights

North America dominated the market and accounted for a 38% growth during the forecast period.
By Application - Medical imaging and diagnostics segment was valued at USD 1.52 billion in 2023
By Component - Software segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 961.16 million
Market Future Opportunities: USD 30230.60 million
CAGR : 33.1%
North America: Largest market in 2023

Market Summary

The market is a dynamic and rapidly evolving sector, driven by advancements in core technologies such as machine learning and natural language processing. These technologies are revolutionizing healthcare delivery through applications like predictive analytics, medical imaging, and virtual nursing assistants. According to recent reports, the global AI in healthcare market is expected to reach a significant market share by 2027, growing at a steady pace due to increasing adoption rates and the need for digitization in healthcare. For instance, AI-based tools are increasingly being used to improve elderly care, with applications ranging from fall detection to medication management.
However, challenges such as physician and provider skepticism, data privacy concerns, and regulatory issues persist. Despite these challenges, the opportunities for AI in healthcare are vast, with potential applications in disease diagnosis, treatment planning, and population health management.

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

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How is the Artificial Intelligence (AI) In Healthcare Market Segmented and what are the key trends of market segmentation?

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.

Artificial Intelligence (AI) is revolutionizing the healthcare sector by enhancing various applications, from treatment optimization and diagnostics to patient engagement and fraud detection. Natural language processing and machine learning algorithms enable AI-powered virtual assistants to assist in clinical decision support, while computer vision systems analyze medical images for disease prediction and radiation therapy planning. Genomic data analysis and drug discovery platforms leverage AI to uncover new insights and accelerate research. Data mining techniques and predictive modeling are crucial for risk stratification and clinical trial optimization, while deep learning models improve healthcare chatbots and robotic surgery systems' precision.

The market for AI in healthcare is expanding rapidly, with remote patient monitoring and AI-powered diagnostics witnessing significant growth. According to recent studies, the market for AI in healthcare is projected to reach 61.2 billion USD by 2026, representing a 41.5% increase from its current size. Additionally, the adoption of AI in healthcare is expected to grow by 38.2% in the next five years. AI's impact on healthcare is multifaceted, from improving patient outcomes and reducing costs to enhancing operational efficiency and enabling personalized medicine. Wearable sensor data and electronic health records are essential data sources for AI applications in healthcare, while healthcare data interoperability and big data analytics are crucial for driving innovation and improving patient care.

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The Medical imaging and diagnostics segment was valued at USD 1.52 billion in 2019 and showed a gradual increase during the forecast period.

AI's role in healthcare is continuously evolving, with ongoing developments in precision oncology, disease prediction models, and drug repurposing. AI-powered fraud detection systems and biometric authentica

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.

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.

BackgroundResearch related to Artificial Intelligence (AI) in healthcare applications is evolving. It is essential to incorporate collaborative learning from published research to comprehend the challenges and accessibility of opportunities when integrating AI in healthcare systems. To investigate the role of AI, a qualitative and quantitative year in review study was conducted, encompassing the evaluation of literature published in 2024 to gain insight into the recent advancements of the field.MethodsTo find research articles about integrating new AI technologies into healthcare systems, a PubMed search using the terms “2024”, “artificial intelligence”, and “large language models” was conducted. The search was restricted to human subject research and used a deep-learning-based approach to assess the reliability of publications as of December 31, 2024 on January 1, 2025. In addition, for each publication, each mature article was manually annotated for the AI model type (e.g., LLM, DL, ML), healthcare specialty, and the data type used (image, text, tabular, or audio).Additionally,qualitative and quantitative analyses were performed to illuminate statistics and trends of combined published articles.ResultsOur PubMed search yielded 28,180 total articles; 1,693 were initially labeled mature, after which 1,551 articles were analyzed after exclusions. Similar to the prior years, we excluded systematic reviews in the final analysis and were excluded in this year's dataset.The most prevalent specialties within our PubMed search originated from imaging (407), head and neck (127), and General (122). Analysis of AI model types showed that the Large Language Model (LLM) was the most popular utilized in 479 publications, followed by AI General (448), and DL (372). Qualitative data was obtained on the data types, and it was revealed that the image data was predominant and used in 57.0% of the mature sources, followed by text (33.1%), followed by tabular (7.59%). The utilization of Large Language Models (LLMs) is the highest in publications associated with education at 18.6%, followed by General at 13.6%. These results indicate that LLMs are frequently applied in educational contexts and administrative tasks amongst the healthcare specialties for research.ConclusionHealthcare specialties, including imaging, head and neck, and general medicine, have taken over the realm of AI in healthcare. Other specialties that distinctive types of AI and LLMs could likely drive in the future include education, pathology, as well as surgery. It is essential to use a collaborative approach to investigate the multimodal models of AI in healthcare applications to provide a thorough encapsulation of AI in healthcare.Data Files DescriptionOne data file is provided, which illustrates the annotations of the mature sources used in our review. The first file is named Annotated_OnlyMature_Unique_2024_YIR_All_Publications - Annotated_OnlyMature_Unique_2024_YIR_All_Publications and includes ‘Title’, ‘DOI’, ‘Abstract’, ‘Author Address’, ‘Specialty’, ‘Model’, and 'Data Type’. The ‘Specialty’, ‘Model’, and ‘Data Type’ were predominantly analyzed by the BrainXAI research team to produce our meta-analysis of the mature sources of AI. This year we have excluded systematic reviews from the dataset compared to the 2023 year in review dataset, but can be provided on request.

The AI Training Dataset In Healthcare Market size was valued at USD 341.8 million in 2023 and is projected to reach USD 1464.13 million by 2032, exhibiting a CAGR of 23.1 % during the forecasts period. The growth is attributed to the rising adoption of AI in healthcare, increasing demand for accurate and reliable training datasets, government initiatives to promote AI in healthcare, and technological advancements in data collection and annotation. These factors are contributing to the expansion of the AI Training Dataset In Healthcare Market. Healthcare AI training data sets are vital for building effective algorithms, and enhancing patient care and diagnosis in the industry. These datasets include large volumes of Electronic Health Records, images such as X-ray and MRI scans, and genomics data which are thoroughly labeled. They help the AI systems to identify trends, forecast and even help in developing unique approaches to treating the disease. However, patient privacy and ethical use of a patient’s information is of the utmost importance, thus requiring high levels of anonymization and compliance with laws such as HIPAA. Ongoing expansion and variety of datasets are crucial to address existing bias and improve the efficiency of AI for different populations and diseases to provide safer solutions for global people’s health.

AI in healthcare is accelerating transformation, from diagnosing illnesses faster to streamlining hospital workflows. Impact spans advanced diagnostics to drug development and patient documentation. For example, AI is cutting drug development timelines by over 50% in some cases, and ambient‑AI scribes are already easing burnout for clinicians. Explore the stats...

In 2021, 42 percent of healthcare organizations in the European Union were currently using AI technologies for disease diagnosis, while a further 19 percent had plans to employ this technology within the next 3 years. Furthermore, 33 percent of healthcare organizations surveyed planned to use patient monitoring AI tools in the next 3 years How much impact does AI have on saving time in healthcare? An online survey from several European countries concluded that the implementation of AI could free up significant portions of time in healthcare – with nearly half of the hours worked by medical equipment preparers and one-third of the hours of medical assistants. While, according to another survey, physicians in Europe could spend almost ** percent more time with patients instead of administrative tasks with the help of AI. The same held true for nurses, whose time with patients would increase by *** percent thanks to AI, according to estimates. Attitudes and opinions regarding AI in healthcare In 2021, a quarter of respondents surveyed in the European Union reported trusting AI-enabled decisions in patient monitoring, higher than any other AI applications. Meanwhile, only * percent trusted AI-enabled decisions in disease diagnostics, with the majority preferring to combine it with expert judgment from healthcare professionals. Overall, the opinions of EU respondents on the impact of AI in healthcare were positive, with the majority agreeing that the use of AI could result in improvement in the quality of diagnosis decisions and treatment

The global healthcare data and analytics services market is experiencing robust growth, driven by the increasing volume of healthcare data generated from electronic health records (EHRs), wearable devices, and other sources. The market's expansion is fueled by the rising need for improved patient care, operational efficiency, and evidence-based decision-making within healthcare organizations. Key trends include the adoption of cloud-based analytics platforms, the rise of artificial intelligence (AI) and machine learning (ML) applications for predictive analytics and personalized medicine, and a growing focus on data security and privacy regulations like HIPAA. Major players like Accenture, Optum, and IBM are investing heavily in developing advanced analytics solutions and expanding their service offerings to cater to this growing demand. While the market faces challenges such as data integration complexities and the need for skilled professionals in data science and analytics, the long-term outlook remains positive. We project a Compound Annual Growth Rate (CAGR) of 15% for the forecast period, reflecting the continuous advancements in technology and the increasing adoption of data-driven strategies in the healthcare sector. This growth will be further spurred by government initiatives promoting digital health and value-based care models, which heavily rely on robust data analysis capabilities. The competitive landscape is characterized by a mix of large multinational corporations and specialized analytics firms. The top players are continuously investing in acquisitions, partnerships, and research & development to maintain their market share and offer innovative solutions. The market is segmented based on service type (predictive analytics, descriptive analytics, diagnostic analytics), deployment mode (cloud, on-premise), and end-user (hospitals, pharmaceutical companies, payers). Regional variations in market growth are expected, with North America and Europe maintaining dominant positions due to advanced healthcare infrastructure and higher adoption rates of data analytics technologies. However, growth in Asia-Pacific is anticipated to accelerate, driven by increasing investments in healthcare infrastructure and digitalization efforts in developing economies. The overall market size is projected to reach significant figures by 2033, presenting lucrative opportunities for established players and emerging businesses alike.

Overview

New York, NY – August 11, 2025 : Global AI in Healthcare Market is projected to grow significantly, from US$ 26.8 Billion in 2024 to US$ 696.0 Billion by 2034. This growth reflects a robust CAGR of 38.5% during the forecast period. The rising demand for faster, accurate, and scalable healthcare solutions is fueling adoption. AI is transforming how care is delivered by enabling quicker data analysis and real-time decision-making. As healthcare systems look to modernize, AI technologies are b newsecoming central to diagnosis, treatment planning, and patient management.

AI technologies are being rapidly adopted across multiple healthcare areas. These include medical imaging, diagnostics, drug discovery, and personalized medicine. AI systems process large volumes of patient data and deliver insights faster than traditional methods. This capability supports timely clinical decisions and better outcomes. As medical data grows in complexity and volume, AI becomes essential for extracting relevant information. Its accuracy and speed help improve clinical workflows and patient safety while reducing errors in diagnosis and treatment.

A major driver of market growth is the need for improved diagnostic accuracy. In fields like radiology, AI tools can detect tumors, infections, or fractures with high precision. Predictive models powered by AI also help in early identification of at-risk patients. These tools aid in creating optimized, personalized treatment plans. By integrating AI, healthcare providers can improve diagnostic timelines and reduce the burden on specialists. This trend highlights AI's value in achieving faster, more reliable medical evaluations and proactive care strategies.

Strategic collaborations are shaping the AI healthcare landscape. In July 2024, Microsoft joined forces with Mass General Brigham and the University of Wisconsin-Madison. Their goal is to build AI models that support diagnosis for over 23,000 conditions. These partnerships showcase how AI enhances radiology and boosts clinician performance. Beyond diagnostics, AI helps automate administrative tasks like patient scheduling, billing, and documentation. These efficiencies allow staff to focus more on care delivery. The result is better resource allocation and reduced operational costs for healthcare facilities.

The future of AI in healthcare looks promising, with advancements in machine learning and data analytics. AI is also driving the evolution of telemedicine. Remote patient monitoring and virtual consultations are now more accessible and accurate. These tools improve healthcare delivery, especially in underserved or remote areas. The growing capabilities of AI algorithms open doors for more precise, affordable, and efficient solutions. As investment and innovation continue, AI will play a vital role in transforming healthcare systems worldwide for the better.

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.

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

This paper documents a puzzle. Despite the numerous popular press discussions of artificial intelligence (AI) in healthcare, there has been relatively little adoption. Using data from Burning Glass Technologies on millions of online job postings, we find that AI adoption in healthcare remains substantially lower than in most other industries, and that under 3% of the hospitals in our data posted any jobs requiring AI skills from 2015-2018. The low adoption rates mean any statistical analysis is limited. Nevertheless, the adoption we do observe shows that larger hospitals, larger counties, and integrated salary model hospitals are more likely to adopt.

Access comprehensive global health data on disease prevalence, mortality rates, treatment effectiveness, and healthcare infrastructure.

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