Artificial Intelligence (AI) in Healthcare Market Size, Share & Growth

Updated on : November 13, 2024

Artificial Intelligence (AI) in Healthcare Market Size

The global AI in Healthcare market size was valued at USD 20.9 billion in 2024 and is estimated to reach USD 148.4 billion by 2029, growing at a CAGR of 48.1% during the forecast period from 2024 to 2029. The growth of AI in the healthcare market is driven by the generation of large and complex healthcare datasets, the pressing need to reduce healthcare costs, improving computing power and declining hardware costs, and the rising number of partnerships and collaborations among different domains in the healthcare sector, and growing need for improvised healthcare services due to imbalance between healthcare workforce and patients.

Artificial Intelligence (AI) in Healthcare Market Statistics Forecast to 2029

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AI in Healthcare Market  Trends & Dynamics:

Driver: Generation of large and complex healthcare datasets

Generating extensive and intricate healthcare datasets is a pivotal driver for AI in the healthcare Market Size. Advanced technologies enable the accumulation of diverse patient information, from medical records to genomic data. This abundance of data catalyzes AI applications, facilitating the identification of patterns and insights crucial for diagnostics, personalized medicine, and treatment planning. Integrating big data analytics and AI promises to revolutionize healthcare processes, enhancing accuracy and efficiency. In the coming years, the impact of this data-driven approach is expected to be high, ushering in a transformative era in healthcare delivery with improved patient outcomes and streamlined operations.

Restraint: Reluctance among medical practitioners to adopt AI-based technologies.

Stemming from concerns about job displacement, skepticism regarding AI system reliability, and unease over integration into established practices, this reluctance impedes the market's growth. Addressing the challenge requires significant investments in training, contributing to a learning curve that discourages healthcare professionals. Overcoming this obstacle necessitates focused initiatives that underscore education and foster collaboration between technology developers and healthcare institutions. Such efforts are crucial for fostering understanding and acceptance, enabling the realization of AI's potential in healthcare, including enhanced diagnostics, improved treatment plans, and ultimately superior patient outcomes.

Opportunities: The growing potential of AI-based tools for elderly care

Factors such as increased life expectancy, shifting demographics, and challenges in traditional caregiving contribute to the growing potential of AI in elderly care. AI holds the transformative potential to enhance elderly care, ensuring effective and affordable solutions. Through continuous health monitoring, AI facilitates early detection of health issues, while fall detection algorithms improve safety. AI-driven medication management ensures adherence to treatment plans, and personalized care plans optimize interventions based on individual health data. Cognitive assistance and social interaction facilitated by AI contribute to mental well-being, particularly for seniors with conditions like dementia. The integration of companion robots and virtual assistants addresses loneliness.

Moreover, AI streamlines routine tasks, improving resource allocation in healthcare settings and enhancing cost efficiency. AI in elderly care represents a paradigm shift, offering proactive, personalized, and cost-effective solutions to ensure the well-being of the aging population. AI opens opportunities for more effective applications in healthcare, such as predictive analytics for disease outbreaks, personalized treatment plans based on genetic profiles, and advanced diagnostic tools that improve accuracy and speed in identifying medical conditions.

Challenge: Lack of curated healthcare data

The profound potential of AI in healthcare faces a substantial impediment – the scarcity of curated healthcare data. This bottleneck hampers AI performance, leading to inaccurate predictions and potential patient harm. Data fragmentation, privacy concerns, high costs, and expertise barriers exacerbate the challenge. For instance, in November 2023, the World Health Organization (WHO) released guidelines outlining essential regulatory considerations for applying artificial intelligence (AI) in healthcare. Emphasizing safety, efficacy, and collaboration, the document addresses risks related to AI's use of health data, advocating for robust legal and regulatory frameworks to ensure privacy and security. The guidelines highlight six critical areas for regulating AI in healthcare: transparency, risk management, external validation of data, commitment to data quality, addressing complex regulations like GDPR and HIPAA, and encouraging collaboration among stakeholders. Proposed solutions encompass standardization initiatives, public-private partnerships for responsible data sharing, synthetic data generation, and AI-powered curation tools to streamline the process. Overcoming this obstacle requires proactive measures like data standardization, collaboration, and technological advancements. Addressing specific healthcare sub-areas, exploring ethical considerations, and analyzing regulatory roles will further enrich the understanding and advancement of AI in healthcare.

Artificial Intelligence (AI) in Healthcare Market Ecosystem

Artificial Intelligence (AI) in Healthcare Market Segmentation

AI in Healthcare Market Share

The market for Software segment to hold largest market share during the forecast period.

The integration of non-procedural languages marks a transformative shift in the AI landscape of healthcare, traditionally dominated by procedural languages like Python and Java. These intuitive, declarative languages, such as SQL, offer a potential game-changer by emphasizing outcomes over step-by-step instructions. This shift democratizes AI development, enabling healthcare professionals to contribute directly, fostering collaboration, and leveraging domain expertise. Non-procedural languages enhance model explainability, streamline workflows, and focus on core clinical knowledge, promising significant segmental growth in areas like clinical decision support systems, medical imaging analysis, personalized medicine, and public health. Despite challenges, the potential benefits position non-procedural languages as a compelling avenue for advancing AI in healthcare, promising improved patient care and outcomes.

Deep learning segment in the machine learning technology to hold the largest share in the AI in Healthcare market during the forecast period.

Deep learning's transformative impact on healthcare lies in its ability to construct hierarchical representations through artificial neural networks (ANNs). These interconnected layers of neurons emulate the human brain's structure, learning from extensive datasets to extract intricate features and patterns. In medical imaging, deep learning excels in tasks like image classification, detecting diseases in X-rays and MRIs, and object segmentation for precise analysis. Natural Language Processing (NLP) enables the extraction of valuable information from clinical notes and research papers, facilitating diagnosis and drug discovery. Moreover, deep learning predicts molecular interactions in drug development and precision medicine, identifies drug targets, and tailors treatments based on individual genetic profiles. Clinical decision support, personalized healthcare plans, and predictive analytics further demonstrate the potential of deep learning.

Patient Data & Risk Analysis segment in application to hold the highest market share of the AI in Healthcare market during the forecast period.

Natural Language Processing (NLP) in healthcare enables computers to analyze, generate, and translate human language. It unlocks insights from unstructured data, streamlines tasks, empowers patients through chatbots, and enhances personalized medicine, revolutionizing healthcare delivery. Natural Language Processing (NLP) plays a pivotal role in revolutionizing patient data analysis and risk assessment within AI in healthcare. By converting unstructured text in medical records into structured data, NLP enables rapid and scalable analysis. It empowers clinicians to identify at-risk patients by detecting nuanced details often missed in structured data. For instance, in January 2023, IQVIA Inc’s (US) NLP Risk Adjustment Solution, applied by a large US healthcare payer, successfully automated, and digitalized their risk adjustment process, enhancing efficiency by over 25%. Utilizing NLP, they improved medical record reviews, enabling nurses to identify conditions more accurately and submit reimbursement claims to CMS with increased precision. The solution's clinically intelligent NLP, processing millions of records an hour, ensured high coding accuracy (>90% precision and recall), reduced review time, and provided a comprehensive audit trail for accepted ICD10-CM codes, enhancing overall risk adjustment submissions.

Patients segment to account for largest CAGR of the AI in Healthcare market during the forecast period.

Integrating artificial intelligence (AI) with smartphones and wearables is revolutionizing the healthcare landscape. This powerful combination is democratizing health data, allowing patients to actively participate in their well-being by tracking vital signs, sleep patterns, activity levels, and moods. The wealth of personal health data generated is analyzed by AI algorithms, enabling the identification of patterns, prediction of health risks, and personalization of treatment plans. This proactive and data-driven approach is reshaping healthcare, providing individuals with a deeper understanding of their health. 

AI in Healthcare Market Growth & Statistics

The AI in healthcare market growth is experiencing significant growth as artificial intelligence technologies revolutionize the healthcare industry .The growth of AI in healthcare is accelerating as artificial intelligence technologies transform the healthcare landscape. AI-powered solutions are improving diagnostics, patient outcomes, and operational efficiency, contributing to the rapid expansion of the market. Key applications of AI in healthcare include medical imaging, predictive analytics, and robotic surgeries, all of which enhance accuracy and reduce human error. With advancements in machine learning and deep learning algorithms, AI is enabling quicker diagnoses, more personalized treatments, and better patient care.

AI in healthcare statistics show a clear upward trend in adoption, with AI playing an increasingly vital role in drug discovery, clinical trials, and patient management systems. As healthcare providers prioritize digital transformation and cost-effective solutions, the growth of AI in healthcare is set to continue, offering innovative technologies that will shape the future of medicine.

AI in Healthcare Market Regional Analysis

The AI in Healthcare market in the Asia Pacific is estimated to grow at a higher CAGR during the forecast period.

The factor driving the growth of the AI in healthcare Industry in the Asia Pacific region is the rise in the number of cancer patients in Asia Pacific countries. According to the report from the National Library of Medicine, in 2023, the Asia-Pacific region, home to over 60% of the global population, will account for half of all cancer cases and 58% of cancer-related deaths. Worldwide, there were 19.2 million new cancer cases and 9.9 million deaths, with the Asia-Pacific region witnessing nearly 50% of the new cases and over half of the cancer-related fatalities. These figures underscore the substantial cancer burden in the Asia-Pacific region. Given that nurses constitute more than half of the oncology healthcare workforce, acquiring new knowledge and embracing evidence-based practices is crucial for delivering efficient and effective cancer care.

Artificial Intelligence (AI) in Healthcare Market by Region

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Top AI in Healthcare Companies  - Key Market Players:

Major vendors in the AI in healthcare companies include

• Koninklijke Philips N.V. (Netherlands),
• Microsoft (US),
• Siemens Healthineers AG (Germany),
• Intel Corporation (US),
• NVIDIA Corporation (US),
• Google Inc. (US),
• GE HealthCare Technologies Inc. (US),
• Medtronic (US),
• Micron Technology, Inc (US),
• Amazon.com Inc (US),
• Oracle (US), and Johnson & Johnson Services, Inc. (US). Apart from this, Merative (US), General Vision, Inc., (US), CloudMedx (US), Oncora Medical (US), Enlitic (US), Lunit Inc., (South Korea), Qure.ai (India), Tempus (US), COTA (US), FDNA INC. (US), Recursion (US), Atomwise (US), Virgin Pulse (US), Babylon Health (UK), MDLIVE (US), Stryker (US), Qventus (US), Sweetch (Israel), Sirona Medical, Inc. (US), Ginger (US), Biobeat (Israel) are among a few emerging companies in the AI in Healthcare market growth.

AI in Healthcare Market Report Scope :

Artificial Intelligence (AI) in Healthcare Market Growth Highlights

This research report categorizes the AI in Healthcare market size Offering, Technology, Application, and End User, and Region.

Recent Developments in AI In Healthcare Industry :

• In October 2023, Microsoft (US) launched new data and AI solutions, Microsoft Cloud at the HLTH 2023 conference, aiming to empower healthcare organizations in unlocking insights and enhancing patient and clinician experiences. The introduced industry-specific data solutions in Microsoft Fabric provide a unified analytics platform, simplifying the integration of diverse health data sources and enabling secure access to valuable insights.
• In November 2023, Koninklijke Philips N.V., (Netherlands) collaborated with Vestre Viken Health Trust in Norway, deploying its AI Manager platform to enhance radiology workflows. The AI-enabled bone fracture application streamlined diagnoses, allowing radiologists to focus on complex cases. This initiative, spanning 30 hospitals and serving around 3.8 million people, marked Philips' most extensive AI deployment in Europe, contributing to improved patient care and accelerated diagnostic processes.

Key Questions Addressed in the Report:

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