Tanzania Artificial Intelligence (AI) in Healthcare Market Report 2022 to 2030

Tanzania Artificial Intelligence (AI) in Healthcare Market Analysis

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Tanzania's Artificial Intelligence (AI) In Healthcare Market is projected to grow from $2.76 Mn in 2022 to $57.36 Mn by 2030, registering a CAGR of 46.12% during the forecast period of 2022-2030. The market will be driven by a rise in the availability of skilled professionals and government initiatives to improve the AI Healthcare infrastructure. The market is segmented by healthcare components & by healthcare applications. Some of the major players include IBM Watson Health, GE Healthcare & Sali Labs.

ID: IN10TZDH003 CATEGORY: Digital Health GEOGRAPHY: Tanzania AUTHOR: Vidhi Upadhyay

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Tanzania Artificial Intelligence (AI) In Healthcare Market Executive Summary

Tanzania's Artificial Intelligence (AI) In Healthcare Market is projected to grow from $2.76 Mn in 2022 to $57.36 Mn by 2030, registering a CAGR of 46.12% during the forecast period of 2022-30. Tanzania had a total of 8,458 health facilities as of 2020. A large percentage, nearly 7.2 thousand, were dispensaries. In the same year, the country had 369 healthcare facilities and 926 medical centres. International donors supplement healthcare financing by contributing up to 40% of the healthcare budget. The US government makes a major contribution to Tanzanian government assistance programmes through USAID and the CDC. Health insurance coverage remains low, with only 32% of Tanzanians having health insurance as of 2019. Only 1% of those people have private health insurance. Tanzania is plagued by malaria, dengue fever, and cholera. There have also been reports of sleeping sickness caused by tsetse fly bites in the north, including the Serengeti. Other diseases, such as rift valley fever, are more common in rural areas with limited access to sanitation.

Tanzania's Radiant Imaging and Diagnostics Centre has used AI software to analyse medical images and recognise anomalies, allowing for a more rapid and precise diagnosis. The Arusha Lutheran Medical Centre is diagnosing and treating cervical cancer with AI-powered medical technologies. The artificial intelligence tool analyses Pap smear findings to recognise abnormal cells and assists doctors in making more accurate diagnoses. Wearable devices have been used by Tanzania's Muhimbili National Hospital to monitor pregnant women with hypertension and diabetes, allowing healthcare professionals to detect and manage any potential complications. AI adoption and utilisation in Tanzanian healthcare are still in their initial phases, but there are encouraging signs. Tanzania's government and healthcare providers are recognising the potential of artificial intelligence to improve healthcare delivery, and more initiatives are expected to be initiated in the near future.

Tanzania Artificial Intelligence (AI) In Healthcare Market

Market Dynamics

Market Growth Drivers

Tanzania Health Information System (THIS) analyses healthcare data using AI algorithms to recognize patterns and trends, allowing healthcare professionals to make informed choices regarding resource allocation and patient management. Also, The Tanzania Health Research Data Repository employs artificial intelligence to analyse large amounts of health data and identify potential research areas, allowing researchers to make data-driven decisions about their research projects. Tanzania has one medical doctor for every 20,000 people, according to World Health Organization data. Increasing demand for high-quality healthcare services in the country improves the scope of AI Healthcare adoption in Tanzania. Furthermore, the increasing prevalence of chronic diseases such as diabetes and hypertension necessitates the development of more efficient and effective healthcare solutions, which AI can provide.

Market Restraints

One of the most significant challenges is the country's scarcity of skilled AI professionals. The advancement of AI-powered healthcare solutions necessitates expertise in both healthcare and AI, which is in limited supply in Tanzania. Another barrier to AI adoption is a lack of technological resources and infrastructure. Many Tanzanian healthcare providers still use paper-based records, making it difficult to collect and manage the healthcare data required for AI development. Furthermore, the cost of implementing AI solutions can be significant, which may limit their adoption in resource-constrained settings.

Competitive Landscape

Key Players

IBM Watson Health
GE Healthcare
Philips Healthcare
NVIDIA
Siemens Healthineers
Afya Research Africa
Sali Labs (TZA)

Notable Insights

In October 2021, In Tanzania, UNESCO held a training on Artificial Intelligence for Disaster Response.

In August 2021, Siemens Healthineers partnered with Pacific Diagnostics in Dar es Salaam, Tanzania to meet the rising healthcare requirement in growing and emerging markets, improving access to healthcare in Tanzania.

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

Artifical Intelligence (AI) in Healthcare Market Segmentation

The 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.