India Artificial Intelligence in Healthcare Market Report 2022 to 2030

India Artificial Intelligence (AI) in Healthcare Market Analysis

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India's Artificial Intelligence (AI) in the healthcare market is projected to grow from $0.13 Bn in 2022 to $2.92 Bn by 2030, registering a CAGR of 48.22% during the forecast period of 2022-30. The market will be driven by Increasing healthcare demands due to the rising population, government support, and initiatives. The market is segmented by healthcare components & by healthcare applications. Some of the major players include IBM Watson Health, Qure.ai & PharmEasy.

ID: IN10INDH003 CATEGORY: Digital Health GEOGRAPHY: India AUTHOR: Vidhi Upadhyay

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India Artificial Intelligence (AI) in Healthcare Market Executive Summary

India's artificial intelligence in the healthcare Market is projected to grow from $0.13 Bn in 2022 to $2.92 Bn by 2030, registering a CAGR of 48.22% during the forecast period of 2022-30. In terms of revenue and employment, India's healthcare sector ranks among the top contributors to the Indian economy. Because of digitization, innovation, and innovative hybrid business models with the combination of traditionalists and technological firms, the sector has risen fast in the previous years. Furthermore, the increased proportion of lifestyle diseases in metropolitan areas caused by high cholesterol, high blood pressure, obesity, poor diet, and increasing alcohol consumption is increasing the demand for specialized care services. By 2025, India's AI spending is estimated to reach $11.78 billion.

AI is already being used in the diagnosis and early detection techniques. NITI Aayog, the Indian government's public think tank, policy framework, and program framework, has been exploring the use and implementation of AI in the early identification of diabetes and is currently working on the use of AI as a screening tool in eye care. AI algorithms are now being used in healthcare for early illness detection, drug development trials, precise patient monitoring, and self-care. It has the potential to make healthcare more inexpensive and accessible in the most remote locations, as well as increase efficiency in areas where it currently exists. The need for more in-hospital and out-of-hospital therapies is being driven by an aging population with a rising burden of obesity and diabetes.

Apollo Hospitals India has created an artificial intelligence (AI) application (developed on Microsoft Azure) to detect the risk of cardiovascular disease in order to initiate early intervention. Manipal Hospital is one of the first institutions in the country to deploy IBM Watson, a cognitive technology platform, to improve cancer treatment.

India artificial intelligence in healthcare market report 2022 to 2030

Market Dynamics

Market Growth Drivers

India has a significant number of both young (25%) and aged (14%) people with specific health needs. The growing desire for innovative and cost-effective healthcare services is one of the key drivers of AI adoption in India. The country's vast population and expanding burden of noncommunicable diseases have put pressure on the healthcare sector to discover innovative ways of providing care. AI has the potential to enhance the accuracy and speed of diagnoses, lower healthcare costs, and boost access to care, particularly in remote and underserved areas. Furthermore, the increasing availability of healthcare data and the increasing implementation of electronic health records (EHRs) are boosting the adoption of AI in healthcare in India. The massive volumes of data generated by EHRs, medical devices, and wearables may be used to train AI algorithms, which can subsequently help physicians make more accurate diagnoses, detect high-risk patients, and customize treatment programs.

Market Restraints

Public health data, which is also one of the potential risk factors, is the fundamental necessity for integrating AI in healthcare. Massive volumes of patient data are required for AI-based applications and solutions. Inaccurate choices, such as incorrect drug prescriptions or disease diagnoses, can be exacerbated by fragmented or faulty data. A high level of AI-based automation may jeopardize physicians' capacity to spot errors effectively at any stage of AI integration, leading to an overreliance on AI-based solutions. AI should be utilized to supplement rather than automate healthcare decision-making. Around 77% of total respondents across industries expressed concern that increased AI deployment might result in job cutbacks within their organizations. Moreover, in India, almost 60% of health expenditure in India is out of pocket (OOP), a larger proportion when compared to economies such as China (30%) and Brazil (25%) which can also challenge the full fledge expansion of the AI in healthcare systems.

Competitive Landscape

Key Players

IBM Watson Health
Google Health
Philips Healthcare
GE Healthcare
NVIDIA Corporation
Amazon Web Services
Qure.ai (IND)
PharmEasy (IND)
HealthifyMe (IND)

Notable Insights

February 2023, The Centre has identified AIIMS Delhi as an artificial intelligence (AI) center of excellence in healthcare
March 2022, Google Health seeks to improve healthcare through the use of artificial intelligence and smartphones
April 2020, The SAMRIDH Healthcare Blended Finance Facility, backed by USAID and delivered by IPE Global, committed 2.45 Crore INR ($350,000 USD) to Qure.ai to scale Artificial Intelligence (AI)-enabled chest X-ray screening software, qXR, at 50 sites across more than 15 Indian states

1. Executive Summary
1.1 Digital Health Overview
1.2 Global Scenario
1.3 Country Overview
1.4 Healthcare Scenario in Country
1.5 Digital Health Policy in Country
1.6 Recent Developments in the Country

2. Market Size and Forecasting
2.1 Market Size (With Excel and Methodology)
2.2 Market Segmentation (Check all Segments in Segmentation Section)

3. Market Dynamics
3.1 Market Drivers
3.2 Market Restraints

4. Competitive Landscape
4.1 Major Market Share

4.2 Key Company Profile (Check all Companies in the Summary Section)

4.2.1 Company
4.2.1.1 Overview
4.2.1.2 Product Applications and Services
4.2.1.3 Recent Developments
4.2.1.4 Partnerships Ecosystem
4.2.1.5 Financials (Based on Availability)

5. Reimbursement Scenario
5.1 Reimbursement Regulation
5.2 Reimbursement Process for Diagnosis
5.3 Reimbursement Process for Treatment

6. Methodology and Scope

India Artificial Intelligence (AI) in Healthcare Market Segmentation

Artificial Intelligence (AI) in Healthcare Market is segmented as mentioned below:

By Healthcare Component (Revenue, USD Billion):

Software Solutions
Hardware
Services

By Healthcare Applications (Revenue, USD Billion):

Robot-Assisted Suregery
Virtual Assistants
Administrative Workflow Assistants
Connected Machines
Diagnosis
Clinical Trials
Fraud Detection
Cybersecurity
Dosage Error Reduction

Methodology for Database Creation

Our database offers a comprehensive list of healthcare centers, meticulously curated to provide detailed information on a wide range of specialties and services. It includes top-tier hospitals, clinics, and diagnostic facilities across 30 countries and 24 specialties, ensuring users can find the healthcare services they need.

Additionally, we provide a comprehensive list of Key Opinion Leaders (KOLs) based on your requirements. Our curated list captures various crucial aspects of the KOLs, offering more than just general information. Whether you're looking to boost brand awareness, drive engagement, or launch a new product, our extensive list of KOLs ensures you have the right experts by your side. Covering 30 countries and 36 specialties, our database guarantees access to the best KOLs in the healthcare industry, supporting strategic decisions and enhancing your initiatives.

How Do We Get It?

Our database is created and maintained through a combination of secondary and primary research methodologies.

1. Secondary Research

With many years of experience in the healthcare field, we have our own rich proprietary data from various past projects. This historical data serves as the foundation for our database. Our continuous process of gathering data involves:

Analyzing historical proprietary data collected from multiple projects.
Regularly updating our existing data sets with new findings and trends.
Ensuring data consistency and accuracy through rigorous validation processes.

With extensive experience in the field, we have developed a proprietary GenAI-based technology that is uniquely tailored to our organization. This advanced technology enables us to scan a wide array of relevant information sources across the internet. Our data-gathering process includes:

Searching through academic conferences, published research, citations, and social media platforms
Collecting and compiling diverse data to build a comprehensive and detailed database
Continuously updating our database with new information to ensure its relevance and accuracy

2. Primary Research

To complement and validate our secondary data, we engage in primary research through local tie-ups and partnerships. This process involves:

Collaborating with local healthcare providers, hospitals, and clinics to gather real-time data.
Conducting surveys, interviews, and field studies to collect fresh data directly from the source.
Continuously refreshing our database to ensure that the information remains current and reliable.
Validating secondary data through cross-referencing with primary data to ensure accuracy and relevance.

Combining Secondary and Primary Research

By integrating both secondary and primary research methodologies, we ensure that our database is comprehensive, accurate, and up-to-date. The combined process involves:

Merging historical data from secondary research with real-time data from primary research.
Conducting thorough data validation and cleansing to remove inconsistencies and errors.
Organizing data into a structured format that is easily accessible and usable for various applications.
Continuously monitoring and updating the database to reflect the latest developments and trends in the healthcare field.

Through this meticulous process, we create a final database tailored to each region and domain within the healthcare industry. This approach ensures that our clients receive reliable and relevant data, empowering them to make informed decisions and drive innovation in their respective fields.