Kenya Artificial Intelligence in Healthcare Market Report 2022 to 2030

Kenya Artificial Intelligence (AI) in Healthcare Market Analysis

$ 3999

Kenya's Artificial Intelligence (AI) in Healthcare Market is projected to grow from $4.83 Mn in 2022 to $101.48 Mn by 2030, registering a CAGR of 46.32% during the forecast period of 2022-30. The market will be driven by government initiatives and startups working to develop and implement AI solutions for healthcare. The market is segmented by healthcare components & by healthcare applications. Some of the major players include IBM Watson Health, NVIDIA & M-Tiba.

ID: IN10KEDH003 CATEGORY: Digital Health GEOGRAPHY: Kenya AUTHOR: Vidhi Upadhyay

Buy Now

Kenya Artificial Intelligence (AI) in Healthcare Market Executive Summary

Kenya's Artificial Intelligence (AI) in the healthcare market is projected to grow from $4.83 Mn in 2022 to $101.48 Mn by 2030, registering a CAGR of 46.32% during the forecast period of 2022-30. Kenya's health system is broken down into three categories: public providers, private non-profit organizations, and private for-profit healthcare providers. Primary healthcare centers provide basic state-funded public healthcare. Private clinics of varying quality can be found in most major urban areas, including coastal beach resort areas like Diani and Malindi. Private hospitals are mostly concentrated in Nairobi, with a few options in Mombasa. The private Aga Khan University hospital in Nairobi is well-known and popular among foreign visitors. Kenya has one of the highest maternal mortality rates in the world, with mothers delayed in seeking care.

Acceding to the Government AI Readiness Index, Kenya is ranked number 78 globally but is ranked first amongst the 6 EAC member states. Kenya scored 28.75 on the technology pillar, which is lower than the global average of 35.17. Kenya, on the other hand, leads the region in terms of technology adoption and advancement. The Kenyan government has launched the National Health Information System (NHIS), a digital platform aimed at improving health data management and promoting the use of medical technology. In Kenya, a few startup companies and organizations are developing and implementing AI solutions for healthcare. M-Tiba, for example, is a mobile health platform that allows users to access healthcare services via their mobile phones. The platform also employs AI and machine learning algorithms to provide users with personalized healthcare recommendations. Ilara Health is a Kenyan startup that offers low-cost diagnostic equipment and services to Kenya's underprivileged areas. The company employs artificial intelligence-powered algorithms to assist doctors and healthcare providers in making accurate and timely diagnoses.

kenya artificial intelligence in healthcare market

Market Dynamics

Market Growth Drivers

Kenya is experiencing a shortage of healthcare workers, particularly in rural areas. AI can assist in addressing this scarcity by automating routine tasks, freeing up healthcare workers to focus on more complicated cases. Healthcare costs are high in Kenya, and AI can greatly lower them by improving efficiency, reducing waste, and reducing errors. Kenya's startup ecosystem is expanding, and several startups are collaborating to develop and implement AI solutions for healthcare. Furthermore, the Kenyan government has launched a number of initiatives to encourage the use of technology in healthcare. The National Health Information System (NHIS), for example, seeks to digitize healthcare data and promote the use of technology in healthcare.

Market Restraints

Kenya has limited infrastructure, particularly in rural areas, which may limit the adoption and utilization of artificial intelligence in healthcare. Quality medical data is critical for developing and implementing AI solutions in healthcare. Kenya's healthcare data, on the other hand, is fragmented, and there are concerns about data privacy and security. Creating and implementing AI solutions necessitates technical expertise, which is scarce in Kenya. In Kenya, the regulatory framework for artificial intelligence in healthcare is still being developed, and there are concerns about the ethical implications of using AI in healthcare.

Competitive Landscape

Key Players

IBM Watson Health
NVIDIA
Google Health
GE Healthcare
Siemens Healthineers
M-Tiba (KEN)
Ilara Health (KEN)

Notable Insights

March 2023, In Kenya, a collaborative data science hub is using artificial intelligence to improve health equity. Aga Khan University and the University of Michigan (U-M) held a series of meetings in Nairobi to advance a cutting-edge data science hub that uses machine learning to improve health and care delivery in local communities.

February 2023, A new tool developed by a team of Penn State researchers and Jacaranda Health - an organization dedicated to improving maternal and neonatal health outcomes in Africa - has been shown to improve health outcomes for new and expectant mothers in Kenya by providing better and more immediate access to maternal health care services.

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

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.