The AI in mental health market size was estimated at US$ 880.77 million in 2022 it is predicted to grow at a CAGR of 38.2% from 2023 to 2032 to reach around US$ 22,384.27 million by the end of 2032.
The AI in mental health market report offers an exclusive study of the present state expected at the market dynamics, opportunities, market scheme, growth analysis and regional outlook. The report presents energetic visions to conclude and study the market size, market aspiration, and competitive environment. The research also focuses on the important achievements of the market, research & development, and regional (country by country) growth of the leading vendors operating in the market
The study offers intricate dynamics about different aspects of the global AI in mental health market, which aids companies operating in the market in making strategies development decisions. The study also elaborates on remarkable changes that are highly anticipated to configure growth of the global AI in mental health market during the forecast period. It also includes a key indicator analysis that highlights growth prospects of this market and approximate statistics related to growth of the market in terms of value (US$ Mn) and volume (tons).
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Report Scope of the AI in Mental Health Market
| Report Coverage | Details |
| Market Size in 2022 | USD 880.77 million |
| Revenue Forecast by 2032 | USD 22,384.27 million |
| Growth rate from 2023 to 2032 | CAGR of 38.2% |
| Base Year | 2022 |
| Forecast Period | 2023 to 2032 |
| Regions Covered | North America, Europe, Asia Pacific, Latin America, Middle East & Africa |
| Companies Covered | Wysa Ltd, Woebot Health, Ginger, Marigold Health, Mindstrong Health, Bark Technologies, BioBeats, Cognoa, Lyra Health, MeQuilibrium, others |
This study covers an elaborate segmentation of the global AI in mental health market, along with important information and a competition outlook. The report mentions company profiles of players that are currently influence the global AI in mental health market, wherein various developments, expansions, and winning strategies practiced and execute by leading players have been presented in detail.
AI in Mental Health Market Segmentations:
| By Technology | By Application | By Component |
| Machine Learning and Deep Learning
Natural Language Processing (NLP) Others |
Conversational Interfaces
Patient Behavioral Pattern Recognition |
Software-as-a-Service (SaaS)
Hardware |
Research Methodology
The research methodology acquire by analysts for assemble the global AI in mental health market report is based on detailed primary as well as secondary research. With the help of in-depth insights of the market-affiliated information that is obtained and legitimated by market-admissible resources, analysts have offered riveting observations and authentic forecasts for the global market.
During the primary research phase, analysts interviewed market stakeholders, investors, brand managers, vice presidents, and sales and marketing managers. Based on data obtained through interviews of genuine resources, analysts have emphasized the changing scenario of the global market.
For secondary research, analysts study numerous annual report declaration, white papers, market association declaration, and company websites to obtain the necessary understanding of the global AI in mental health market.
TABLE OF CONTENT
Chapter 1. Introduction
1.1. Research Objective
1.2. Scope of the Study
1.3. Definition
Chapter 2. Research Methodology
2.1. Research Approach
2.2. Data Sources
2.3. Assumptions & Limitations
Chapter 3. Executive Summary
3.1. Market Snapshot
Chapter 4. Market Variables and Scope
4.1. Introduction
4.2. Market Classification and Scope
4.3. Industry Value Chain Analysis
4.3.1. Raw Material Procurement Analysis
4.3.2. Sales and Distribution Technology Analysis
4.3.3. Downstream Buyer Analysis
Chapter 5. COVID 19 Impact on AI in Mental Health Market
5.1. COVID-19 Landscape: AI in Mental Health Industry Impact
5.2. COVID 19 – Impact Assessment for the Industry
5.3. COVID 19 Impact: Global Major Government Policy
5.4. Market Trends and Opportunities in the COVID-19 Landscape
Chapter 6. Market Dynamics Analysis and Trends
6.1. Market Dynamics
6.1.1. Market Drivers
6.1.2. Market Restraints
6.1.3. Market Opportunities
6.2. Porter’s Five Forces Analysis
6.2.1. Bargaining power of suppliers
6.2.2. Bargaining power of buyers
6.2.3. Threat of substitute
6.2.4. Threat of new entrants
6.2.5. Degree of competition
Chapter 7. Competitive Landscape
7.1.1. Company Market Share/Positioning Analysis
7.1.2. Key Strategies Adopted by Players
7.1.3. Vendor Landscape
7.1.3.1. List of Suppliers
7.1.3.2. List of Buyers
Chapter 8. Global AI in Mental Health Market, By Technology
8.1. AI in Mental Health Market, by Technology, 2023-2032
8.1.1 Machine Learning and Deep Learning
8.1.1.1. Market Revenue and Forecast (2020-2032)
8.1.2. Natural Language Processing (NLP)
8.1.2.1. Market Revenue and Forecast (2020-2032)
8.1.3. Others
8.1.3.1. Market Revenue and Forecast (2020-2032)
Chapter 9. Global AI in Mental Health Market, By Application
9.1. AI in Mental Health Market, by Application, 2023-2032
9.1.1. Conversational Interfaces
9.1.1.1. Market Revenue and Forecast (2020-2032)
9.1.2. Patient Behavioral Pattern Recognition
9.1.2.1. Market Revenue and Forecast (2020-2032)
Chapter 10. Global AI in Mental Health Market, By Component
10.1. AI in Mental Health Market, by Component, 2023-2032
10.1.1. Software-as-a-Service (SaaS)
10.1.1.1. Market Revenue and Forecast (2020-2032)
10.1.2. Hardware
10.1.2.1. Market Revenue and Forecast (2020-2032)
Chapter 11. Global AI in Mental Health Market, Regional Estimates and Trend Forecast
11.1. North America
11.1.1. Market Revenue and Forecast, by Technology (2020-2032)
11.1.2. Market Revenue and Forecast, by Application (2020-2032)
11.1.3. Market Revenue and Forecast, by Component (2020-2032)
11.1.4. U.S.
11.1.4.1. Market Revenue and Forecast, by Technology (2020-2032)
11.1.4.2. Market Revenue and Forecast, by Application (2020-2032)
11.1.4.3. Market Revenue and Forecast, by Component (2020-2032)
11.1.5. Rest of North America
11.1.5.1. Market Revenue and Forecast, by Technology (2020-2032)
11.1.5.2. Market Revenue and Forecast, by Application (2020-2032)
11.1.5.3. Market Revenue and Forecast, by Component (2020-2032)
11.2. Europe
11.2.1. Market Revenue and Forecast, by Technology (2020-2032)
11.2.2. Market Revenue and Forecast, by Application (2020-2032)
11.2.3. Market Revenue and Forecast, by Component (2020-2032)
11.2.4. UK
11.2.4.1. Market Revenue and Forecast, by Technology (2020-2032)
11.2.4.2. Market Revenue and Forecast, by Application (2020-2032)
11.2.4.3. Market Revenue and Forecast, by Component (2020-2032)
11.2.5. Germany
11.2.5.1. Market Revenue and Forecast, by Technology (2020-2032)
11.2.5.2. Market Revenue and Forecast, by Application (2020-2032)
11.2.5.3. Market Revenue and Forecast, by Component (2020-2032)
11.2.6. France
11.2.6.1. Market Revenue and Forecast, by Technology (2020-2032)
11.2.6.2. Market Revenue and Forecast, by Application (2020-2032)
11.2.6.3. Market Revenue and Forecast, by Component (2020-2032)
11.2.7. Rest of Europe
11.2.7.1. Market Revenue and Forecast, by Technology (2020-2032)
11.2.7.2. Market Revenue and Forecast, by Application (2020-2032)
11.2.7.3. Market Revenue and Forecast, by Component (2020-2032)
11.3. APAC
11.3.1. Market Revenue and Forecast, by Technology (2020-2032)
11.3.2. Market Revenue and Forecast, by Application (2020-2032)
11.3.3. Market Revenue and Forecast, by Component (2020-2032)
11.3.4. India
11.3.4.1. Market Revenue and Forecast, by Technology (2020-2032)
11.3.4.2. Market Revenue and Forecast, by Application (2020-2032)
11.3.4.3. Market Revenue and Forecast, by Component (2020-2032)
11.3.5. China
11.3.5.1. Market Revenue and Forecast, by Technology (2020-2032)
11.3.5.2. Market Revenue and Forecast, by Application (2020-2032)
11.3.5.3. Market Revenue and Forecast, by Component (2020-2032)
11.3.6. Japan
11.3.6.1. Market Revenue and Forecast, by Technology (2020-2032)
11.3.6.2. Market Revenue and Forecast, by Application (2020-2032)
11.3.6.3. Market Revenue and Forecast, by Component (2020-2032)
11.3.7. Rest of APAC
11.3.7.1. Market Revenue and Forecast, by Technology (2020-2032)
11.3.7.2. Market Revenue and Forecast, by Application (2020-2032)
11.3.7.3. Market Revenue and Forecast, by Component (2020-2032)
11.4. MEA
11.4.1. Market Revenue and Forecast, by Technology (2020-2032)
11.4.2. Market Revenue and Forecast, by Application (2020-2032)
11.4.3. Market Revenue and Forecast, by Component (2020-2032)
11.4.4. GCC
11.4.4.1. Market Revenue and Forecast, by Technology (2020-2032)
11.4.4.2. Market Revenue and Forecast, by Application (2020-2032)
11.4.4.3. Market Revenue and Forecast, by Component (2020-2032)
11.4.5. North Africa
11.4.5.1. Market Revenue and Forecast, by Technology (2020-2032)
11.4.5.2. Market Revenue and Forecast, by Application (2020-2032)
11.4.5.3. Market Revenue and Forecast, by Component (2020-2032)
11.4.6. South Africa
11.4.6.1. Market Revenue and Forecast, by Technology (2020-2032)
11.4.6.2. Market Revenue and Forecast, by Application (2020-2032)
11.4.6.3. Market Revenue and Forecast, by Component (2020-2032)
11.4.7. Rest of MEA
11.4.7.1. Market Revenue and Forecast, by Technology (2020-2032)
11.4.7.2. Market Revenue and Forecast, by Application (2020-2032)
11.4.7.3. Market Revenue and Forecast, by Component (2020-2032)
11.5. Latin America
11.5.1. Market Revenue and Forecast, by Technology (2020-2032)
11.5.2. Market Revenue and Forecast, by Application (2020-2032)
11.5.3. Market Revenue and Forecast, by Component (2020-2032)
11.5.4. Brazil
11.5.4.1. Market Revenue and Forecast, by Technology (2020-2032)
11.5.4.2. Market Revenue and Forecast, by Application (2020-2032)
11.5.4.3. Market Revenue and Forecast, by Component (2020-2032)
11.5.5. Rest of LATAM
11.5.5.1. Market Revenue and Forecast, by Technology (2020-2032)
11.5.5.2. Market Revenue and Forecast, by Application (2020-2032)
11.5.5.3. Market Revenue and Forecast, by Component (2020-2032)
Chapter 12. Company Profiles
12.1. Wysa Ltd
12.1.1. Company Overview
12.1.2. Product Offerings
12.1.3. Financial Performance
12.1.4. Recent Initiatives
12.2. Woebot Health
12.2.1. Company Overview
12.2.2. Product Offerings
12.2.3. Financial Performance
12.2.4. Recent Initiatives
12.3. Ginger
12.3.1. Company Overview
12.3.2. Product Offerings
12.3.3. Financial Performance
12.3.4. Recent Initiatives
12.4. Marigold Health
12.4.1. Company Overview
12.4.2. Product Offerings
12.4.3. Financial Performance
12.4.4. Recent Initiatives
12.5. Mindstrong Health
12.5.1. Company Overview
12.5.2. Product Offerings
12.5.3. Financial Performance
12.5.4. Recent Initiatives
12.6. Bark Technologies
12.6.1. Company Overview
12.6.2. Product Offerings
12.6.3. Financial Performance
12.6.4. Recent Initiatives
12.7. BioBeats
12.7.1. Company Overview
12.7.2. Product Offerings
12.7.3. Financial Performance
12.7.4. Recent Initiatives
12.8. Cognoa
12.8.1. Company Overview
12.8.2. Product Offerings
12.8.3. Financial Performance
12.8.4. Recent Initiatives
12.9. Lyra Health
12.9.1. Company Overview
12.9.2. Product Offerings
12.9.3. Financial Performance
12.9.4. Recent Initiatives
12.10. MeQuilibrium
12.10.1. Company Overview
12.10.2. Product Offerings
12.10.3. Financial Performance
12.10.4. Recent Initiatives
Chapter 13. Research Methodology
13.1. Primary Research
13.2. Secondary Research
13.3. Assumptions
Chapter 14. Appendix
14.1. About Us
14.2. Glossary of Terms
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