AI in Healthcare Market Estimated to Hit USD 3,55,780 Million by 2032

The AI in healthcare market is expected to hit around USD 3,55,780 million by 2032, increasing from USD 15,100 million in 2022, and growing at a double-digit CAGR of 37.66% from 2023 to 2032.

Ottawa, Dec. 05, 2023 (GLOBE NEWSWIRE) -- The global AI in healthcare market size accounted for USD 20,040 million in 2023 and is projected to surpass around USD 1,27,470 million by 2029, as a result of the increasing adoption of advanced technology, innovation in clinical research and rising demand for customized healthcare, According to Precedence Research. 

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Artificial intelligence refers to the ability of a computer system to learn from data and make judgments to increase the likelihood of achieving a goal. The use of AI in Healthcare is growing as more recent technologies are improved and updated. Artificial intelligence (AI) has been applied to several healthcare processes and applications, including virtual assistants, clinical trials, wearables, cybersecurity, administrative workflow assistants, robotic surgery assistance, diagnosis, dosage error reduction, and fraud error detection. Practical and precise healthcare solutions drive the market. As an AI application in Healthcare, it is anticipated to keep growing to demonstrate its worth in raising overall healthcare outcomes, reducing treatment costs, and improving patient care.

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Artificial intelligence (AI) emulates human cognitive processes, primarily centered around learning and using analysis to resolve complex problems. This aspect of intelligence involving hardware and software components is often called machine learning. From a software perspective, artificial intelligence (AI) is closely linked to algorithms. Artificial neural networks (ANNs) provide a conceptual framework for implementing these algorithms, mimicking some features of the functioning of the human brain.

The Healthcare field is an up-and-coming area for AI applications. In 2020, researchers developed numerous systems aimed at augmenting clinical decision-making processes. Making systems understand and use information from Machine learning and algorithms in healthcare is complex. It is a big challenge to create a system that can think both logically and with uncertainty, put medical details in context and code, determine which diagnosis is essential, and suggest the appropriate treatment for the disease. AI in Healthcare is complicated, and ensuring a computer system can handle all these tasks smoothly is challenging.

The development of target therapies and precise medication in cancer, lung diseases, and neurodegenerative diseases is supported by advancements in genomics and proteomics, contributing to growth in early detection. Using this technique can assess the target biomarkers of diseases.

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Recently developed AI gadgets in the healthcare market have significantly contributed to the global market expansion in healthcare.

Top healthcare innovation in 2023:

  • Detection of neurodegenerative disease (Parkinson's disease, Alzheimer's disease and many others) earlier with Machine learning
  • Deactivating the viruses by air curtains
  • Using ultrasound and electromagnetic tracking to improve breast cancer treatment
  • AI inform Orthopaedic insoles for Diabetics patient

Enhancing the Growth of the Healthcare Industry with Machine Learning and Deep Learning Methodologies

The growing use of digital technology in the healthcare industry is driving up the use of artificial intelligence globally. Better patient care and lower healthcare expenses are two benefits of this technology. This expansion is attributed to several factors, including increased chronic illnesses, an ageing population, and the demand for individualized medications. Healthcare professionals are now integrating AI and machine learning into healthcare systems to improve patient care and diagnose diseases early in neurodegenerative disorders. These AI tools are used by content analytics, natural language processing, data analytics, deep learning, predictive analytics, and care services to support early diagnosis.

The increased processing power of artificial intelligence (AI) systems has fueled recent developments in machines. The increased processing power of AI systems has driven the recent advances in machine learning and deep learning. This development is anticipated to speed up the use of algorithms in healthcare by reducing processing times. One such is the 2020 release of GE Healthcare's Suite for Thoracic Care. This novel instrument facilitates the identification of anomalies on chest X-rays associated with COVID-19, including tuberculosis and pneumonia. It speeds up the diagnosis process and helps medical staff and systems guarantee successful treatment.

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Additionally, in 2020, Microsoft made a significant investment of $ 20 million in COVID-19 research. This investment focuses on leveraging artificial intelligence technology and data sciences to address critical areas like hospital resources and diagnostics, contributing to the ongoing battle against the pandemic.

In Healthcare, deep learning is crucial in tracing potential cancerous areas in medical images, like X-rays. It's also applied in "radiomics," where it detects important features in imaging data that may not be visible to the human eye. This combo of radiomics and deep learning, often seen in cancer-related image analysis, offers more accurate diagnoses than older computer-aided detection tools. CT Imaging is a standard and mainly used method for disease diagnosis, most probably used in cancer to detect which tumour has grown. CBCT scans are collected during treatment and suffer poorer tissue differentiation and resolution than CT scans.

Furthermore, deep learning makes a jump in speech recognition, a type of natural language processing (NLP). Deep learning methods are puzzles - the pieces they use don't make sense to us, making it challenging to explain the model's outcome. Deep learning makes a jump in speech recognition, a type of natural language processing (NLP). Deep learning models are like puzzles – the pieces they use don't make much sense to us. So, explaining why they make certain decisions is tricky because the parts don't directly translate into something we understand. Even with this complexity, these advancements improve medical diagnostics and language-related tasks.

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Large data in the healthcare industry refers to extensive and intricate collections of data collected from various sources, including social media, electronic health records, medical devices (such as sensors and ECGs), billing records, and medical devices. The use of highly developed analytical techniques to make sense of this enormous volume of data has grown significantly during the last ten years. Today's healthcare practitioners keep digital lab slides, comprehensive radiological imaging, and electronic health records. Big data is produced at various phases of patient care due to the growth of digital systems in the healthcare industry. The healthcare sector is one of the most significant users of substantial data, particularly in the US. AI has significantly changed. It facilitates faster diagnosis and treatment decisions for physicians. Integration has received active encouragement from the government.

Reducing the Cost of Treatment by Using AI in the Healthcare Market

Analyzing information using artificial intelligence can help doctors make better diagnoses, predict health issues before they happen, and provide personalized care for patients. Imagine AI as a super-smart assistant for healthcare professionals. It's like having a clever friend who can handle almost half the paperwork and routine tasks in hospitals, saving a massive $ 150 billion yearly. It means doctors can focus more on treating patients, making Healthcare better and more efficient for everyone.

According to Harvard's School of Public Health, utilizing AI to diagnose might save up to 50% on treatment costs while improving health outcomes by up to 40%. It improves clinical operations, quality and safety . Healthcare companies can use AI to make things easier, save money, and improve patient care. They can work with a good AI development services provider to do this.

AI Adoption Hurdle in Healthcare

Making and using artificial intelligence systems in Healthcare can be expensive. Because of this, many healthcare providers might not want to use these services. Also, some healthcare professionals prefer to keep how they do things the same, and they might want to avoid switching to using AI systems that need less human help. Another problem is that there needs to be standard rules for AI models. Healthcare data is often messy and different in various places, making it hard to create robust and widely applicable AI solutions. All these things make it challenging for AI solutions to be used in the healthcare industry.

Browse More Insights of Towards Healthcare:

  • The generative AI in healthcare market is estimated to grow from USD 1.07 billion in 2022 to reach an estimated USD 21.74 billion by 2032, growing at a CAGR of 35.1% between 2023 and 2032.
  • The global AI in genomics market size was valued at USD 397.64 million in 2022 and is estimated at USD 7,365.59 million by 2032, expanding at a CAGR of 23.6% from 2023 to 2032.
  • The AI in cancer diagnostics market to reach an estimated USD 2,084.34 million by 2032 and valued at USD 892.23 million in 2022, growing at a 9.35% CAGR between 2023 and 2032.
  • The AI in medical imaging market size is estimated to grow USD 762.84 million in 2022 to reach around USD 14,423.15 million by 2032, growing at a CAGR of 34.8% from 2023 to 2032.
  • The global AI in magnetic imaging (MRI) market size was estimated at USD 5.77 billion in 2022 and is expected to hit around USD 10.8 billion by 2032 with a registered CAGR of 6.23% from 2023 to 2032.

Limited Data Access and Integration Hurdles Pose Significant Challenges in Advancing AI Applications within the Healthcare Industry

In artificial intelligence (AI) and Healthcare, achieving optimal outcomes is intricately tied to the availability of high-quality data. Within the healthcare sector, characterized by the inherent value of information, this restricted access acts as a substantial barrier to AI integration. Healthcare professionals, including doctors and medical staff, encounter difficulties in data collection due to potential disruptions to their daily workflows. Consequently, this challenge results in incomplete datasets, diminishing the efficacy of AI applications in Healthcare. The intersection of information technology and medical practices necessitates strategic solutions to ensure seamless data access and collection, thereby fortifying the foundation for advanced AI implementations in the healthcare domain.

End Users Bring Significant Growth in AI in Healthcare Market:

  • Pharmaceutical and Biotechnology Companies in Drug Discovery employ AI algorithms to analyze biological data, identify potential drug targets, and streamline the drug discovery process.
  • Atomwise utilizes AI for the virtual screening of molecular compounds, accelerating the identification of drug candidates for various diseases. This approach expedites the early stages of drug development.

A growing Potential Tool for Detecting Cancer in Patients:

AI in Healthcare helps doctors make fewer mistakes when determining what's wrong with a patient. It gives precise results, supports medical staff, and is available 24/7, providing continuous service to patients. Cancer is a significant cause of death, but personalized medicine aims to improve outcomes through treatment for individuals. Radiomics, analyzing features from medical imaging, can predict patient outcomes. Each cancer type requires unique treatment plans, like customizing therapy for non-small cell lung cancer (NSCLC) with its lower 3-year survival rate. Machine learning, intense learning, is being used in medical imaging, specifically in a field called radiomics. Radiomics analyses images generated from medical scans, clinical outcomes, and radiation dose information to enhance cancer treatment through radiotherapy. This application of machine learning benefits from the growing availability of labelled medical imaging data and the improved data processing power of computers. In simple terms, computers are getting better at understanding medical images and data, improving cancer treatment by personalized radiotherapy based on these analyses.

Geographical Landscape:

The market for AI in Healthcare is examined by market size trends and offering end users and applications. The AI in the healthcare market is experiencing substantial growth, driven by collaborative efforts across North America, Europe, Asia Pacific, Latin America, Middle East and Africa. North America is among the nations included in the AI in the healthcare market report due to a large number of health facilities, a growing no of significant players investing in AI development, minimally invasive procedures, growing of elderly patients, high healthcare spending in COVID-19 pandemic, and North America leads world in AI in the Healthcare Market. North America is recognized for its tendency to employ the newest and most advanced digital technologies. The advancement in the healthcare department has been supported by North America's strongly developed IT telecommunications and healthcare infrastructure.

Additionally, the healthcare sector takes advantage of supportive government regulations to encourage the integration of innovative and technological advancements such as AI. Over 50% of all Americans are considered to be affected by one or more long-term medical conditions, and the number of patients is rising day by day.

The Asia Pacific is anticipated to grow at the fastest rate during the 2023-2032 forecast period because of the reasons of expanding geriatric population base, developing medical tourism industry, producing government initiatives to raise awareness, growing research activities, and increasing demands for high-quality healthcare in the market. The most significant region in the rate of development is expected to be the Asia Pacific throughout the forecast. This can be understood by the growing number of patients in the area, government spending on constructing intelligent hospitals, and growing investment in developing infrastructure and healthcare. The world’s largest network has grown throughout Asia as smartphone adoption grows. The adoption of AI in Healthcare in this field is projected to be driven by digital technology development to meet data security and privacy needs in the healthcare sector.

Porter's five force analysis helps analyze and forecast the market scenario for individual countries. While providing a forecast analysis of the national data, the existence and accessibility of global brands, as well as the difficulties they encounter as a result of grim or Company competing with each other from local and domestic brands, are taken into account.

Recent Developments:

  • In January 2022, Exscintia, a British AI company, has teamed up with nine pharmaceutical companies, the most among AI companies. Others like IKTOS and GNS Healthcare also have partnerships. They are improving drug discovery using advanced platforms while keeping the data private.
  • In March 2021, IBM Watson’s WFO (Watson for Oncology), an AI tool, was tested for its efficacy and adequacy. WFO assists oncologists in making treatment decisions by analyzing vast medical literature, clinical trial data, and patient records. This not only aids in providing personalized treatment but also helps stay updated with the latest advancements in oncology.

Competitive Landscape:

Companies are trying to gain a competitive advantage by performing a few crucial measures. They're putting more into R&D, developing innovative and novel solutions, carrying them to market, working with other technology companies, and offering unique amenities. These strategies help them establish one another and exceed their competition.

Additionally, the market continues to grow due to people being aware and appreciating the growing number of new artificial intelligence (AI) startups. All of this points to the market becoming more dynamic and larger. Google has recently launched a generative Artificial Intelligence Tool for the industry to work with healthcare organizations and professionals. AI vertex Search is a tool used to identify essential and accurate medical information more quickly, allowing users to search through various data sources, including patients' electronic health records and clinical notes. Medtronic India recently announced a partnership with the artificial intelligence startup Qure.Ai to improve stroke therapy. Through a "hub-and-spoke" network, this partnership aims to incorporate Qure's artificial intelligence-powered products into primary and comprehensive stroke centres.

Key Market Players:

  • Enlitic
  • Google
  • IBM
  • Intel
  • Lunit
  • Microsoft
  • Nvidia
  • Siemens Healthineers
  • Philips
  • Johnson and Johnson
  • Medtronic

Market Segmentation:

By Component:

  • Software
  • Hardware
  • Service

By Application:

  • Virtual Assistant
  • Diagnosis
  • Robot-assisted Surgery
  • Clinical Trial
  • Wearables
  • Administrative Workflow Assistant
  • Cybersecurity
  • Dosage Error Reduction
  • Fraud Errors Detection
  • Connected Machines
  • Others

By Technology

  • Machine Learning
  • Natural Language Processing
  • Context-aware Computing
  • Computer Vision

By End User

  • Hospital and Healthcare Providers
  • Patients
  • Pharmaceutical and Biotechnology Companies
  • Healthcare Payers

By Geography:

  • North America
  • Europe
  • Asia-Pacific
  • Latin America
  • Middle East and Africa

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Towards Healthcare is a leading global provider of technological solutions, clinical research services, and advanced analytics to the healthcare sector, committed to forming creative connections that result in actionable insights and creative innovations. We are a global strategy consulting firm that assists business leaders in gaining a competitive edge and accelerating growth. We are a provider of technological solutions, clinical research services, and advanced analytics to the healthcare sector, committed to forming creative connections that result in actionable insights and creative innovations.


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