Global AI In Mental Health Market is valued at US$ 1.5 Billion in 2024, and it is expected to reach US$ 25.1 Billion by 2034, with a CAGR of 32.0% during a forecast period of 2025-2034.
Many AI activities are underway in the healthcare industry, some of which are geared at enhancing mental health and well-being. These AI projects aimed at improving mental health and well-being worldwide are likely to be the market's primary driving force. AI in the healthcare market has historically been showcasing significant growth owing to the rapid adoption of ML and AI software in the healthcare sector. The emergence of the COVID-19 pandemic provided a chance to demonstrate the power and sophistication that AI can bring to the healthcare sector. During the second pandemic wave, hospitals and clinics around the world used AI-based virtual assistants, inpatient care bots, and AI-assisted surgery robots to deal with the constant influx of patients, which would otherwise have overrun the entire hospital operation cycle. The increasing size and complexity of datasets driving the need for AI, rising demand to reduce rising healthcare costs, and increasing imbalance between the health workforce and patients driving the need for improved healthcare services are the major market drivers.
Several healthcare practitioners are skeptical of AI solutions' ability to diagnose medical problems effectively. Given this, convincing providers that AI-based solutions are cost-effective, efficient, and safe solutions that bring convenience to doctors and better care for patients is difficult. On the other hand, healthcare providers are increasingly accepting of the potential benefits of AI-based solutions and the range of applications they serve.
The AI in Mental Health market is segmented on the basis of application, technology and component. Based on application, the market is segmented into Conversational Interfaces and Patient Behavioral Pattern Recognition. The technology segment includes Machine Learning, Deep Learning, Natural Language Processing (NLP), and Others. By component, the market is segmented into Software-as-a-Service (SaaS) and Hardware.
The Software-as-a-Service (SaaS) segment is expected to account for the majority of the artificial intelligence in the healthcare market. Many organizations are developing software solutions for various healthcare applications, which is a crucial factor contributing to the Software-as-a-Service (SaaS) segment's growth. Strong demand among Software-as-a-Service (SaaS) developers (particularly at medical institutions and universities) and expanding AI applications in the healthcare industry are among the primary factors driving the AI platform's growth in the Software-as-a-Service (SaaS) market. Some of the best AI platforms are Google AI Platform, TensorFlow, Microsoft Azure, Premonition, Watson Studio, Lumiata, and Infrrd.
Machine learning (ML) and deep learning are expected to gain the most significant proportion because ML, a commonly used type of AI, is one of the most rapidly growing sectors in technology. Machine learning is contributing to the transformation of mental health in two ways: crisis prediction and the formulation of treatment plans/identification of biomarkers. By studying critical behavioural biomarkers, machine learning algorithms can assist mental health doctors in determining whether a patient is at risk of developing any mental health illness. Furthermore, the algorithms may aid in monitoring a treatment plan's efficacy.
The North America region is expected to hold the largest share of artificial intelligence in the mental health market over the forecast period. The primary factors driving the growth of the North American market are the increasing adoption of AI technology across the continuum of care, particularly in the United States, and high healthcare spending combined with the onset of the COVID-19, which has accelerated the adoption of AI in clinics and hospitals across the region. Asia-Pacific, on the other hand, is expected to expand quickly during the projection period due to increased investment in AI across Asian countries. One of the important reasons driving the market is the expansion of global market participants in Asian markets such as China, India, and others. Furthermore, significant IT infrastructure innovation and development, as well as entrepreneurial firms focusing on AI-based solutions, are generating revenue in this region.
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Report Attribute |
Specifications |
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Market Size Value In 2024 |
USD 1.5 Billion |
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Revenue Forecast In 2034 |
USD 25.1 Billion |
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Growth Rate CAGR |
CAGR of 32.0 % from 2025 to 2034 |
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Quantitative Units |
Representation of revenue in US$ Billion and CAGR from 2025 to 2034 |
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Historic Year |
2021 to 2024 |
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Forecast Year |
2025-2034 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Application, By Technology, By Component |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
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Competitive Landscape |
Marigold Health, Mindstrong Health, Bark Technologies, Wysa Ltd, Woebot Health, Ginger, BioBeats, Cognoa, Lyra Health, MeQuilibrium, Meru, New life solution Inc., Quartet, Spring Care Inc., Talkspace Inc. and Others |
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Customization Scope |
Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global AI In Mental Health Market Snapshot
Chapter 4. Global AI In Mental Health Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Impact of Covid-19 Analysis
Chapter 5. Market Segmentation 1: By Applications Estimates & Trend Analysis
5.1. By Applications & Market Share, 2024 & 2034
5.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for the following By Applications:
5.2.1. Conversational Interfaces
5.2.2. Patient Behavioral Pattern Recognition
Chapter 6. Market Segmentation 2: By Technology Estimates & Trend Analysis
6.1. By Technology & Market Share, 2024 & 2034
6.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for the following By Technology:
6.2.1. Machine Learning and Deep Learning
6.2.2. Natural Language Processing (NLP)
6.2.3. Others
Chapter 7. Market Segmentation 3: By Component Estimates & Trend Analysis
7.1. By Component & Market Share, 2024 & 2034
7.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for the following By Component:
7.2.1. Software-as-a-Service (SaaS)
7.2.2. Hardware
Chapter 8. AI In Mental Health Market Segmentation 4: Regional Estimates & Trend Analysis
8.1. North America
8.1.1. North America AI In Mental Health Market revenue (US$ Million) estimates and forecasts By Applications, 2021 - 2034
8.1.2. North America AI In Mental Health Market revenue (US$ Million) estimates and forecasts By Technology, 2021 - 2034
8.1.3. North America AI In Mental Health Market revenue (US$ Million) estimates and forecasts by Component, 2021 - 2034
8.1.4. North America AI In Mental Health Market revenue (US$ Million) estimates and forecasts by country, 2021 - 2034
8.2. Europe
8.2.1. Europe AI In Mental Health Market revenue (US$ Million) By Applications, 2021 - 2034
8.2.2. Europe AI In Mental Health Market revenue (US$ Million) By Technology, 2021 - 2034
8.2.3. Europe AI In Mental Health Market revenue (US$ Million) estimates and forecasts by Component, 2021 - 2034
8.2.4. Europe AI In Mental Health Market revenue (US$ Million) by country, 2021 - 2034
8.3. Asia Pacific
8.3.1. Asia Pacific AI In Mental Health Market revenue (US$ Million) By Applications, 2021 - 2034
8.3.2. Asia Pacific AI In Mental Health Market revenue (US$ Million) By Technology, 2021 - 2034
8.3.3. Asia Pacific AI In Mental Health Market revenue (US$ Million) estimates and forecasts by Component, 2021 - 2034
8.3.4. Asia Pacific AI In Mental Health Market revenue (US$ Million) by country, 2021 - 2034
8.4. Latin America
8.4.1. Latin America AI In Mental Health Market revenue (US$ Million) By Applications, (US$ Million), 2021 - 2034
8.4.2. Latin America AI In Mental Health Market revenue (US$ Million) By Technology, (US$ Million),2021 - 2034
8.4.3. Latin America AI In Mental Health Market revenue (US$ Million) estimates and forecasts by Component, 2021 - 2034
8.4.4. Latin America AI In Mental Health Market revenue (US$ Million) by country, (US$ Million), 2021 - 2034
8.5. Middle East & Africa
8.5.1. Middle East & Africa AI In Mental Health Market revenue (US$ Million) By Applications, (US$ Million), 2021 - 2034
8.5.2. Middle East & Africa AI In Mental Health Market revenue (US$ Million) By Technology, (US$ Million), 2021 - 2034
8.5.3. Middle East & Africa AI In Mental Health Market revenue (US$ Million) estimates and forecasts by Component, 2021 - 2034
8.5.4. Middle East & Africa AI In Mental Health Market revenue (US$ Million) by country, (US$ Million), 2021 - 2034
Chapter 9. Competitive Landscape
9.1. Major Mergers and Acquisitions/Strategic Alliances
9.2. Company Profiles
9.2.1. Wysa Ltd,
9.2.2. Woebot Health,
9.2.3. Ginger,
9.2.4. Marigold Health,
9.2.5. Mindstrong Health,
9.2.6. Bark Technologies,
9.2.7. BioBeats, Cognoa,
9.2.8. Lyra Health,
9.2.9. MeQuilibrium
9.2.10 Meru
9.2.11 New Life Solution Inc.
9.2.12 Quartet
9.2.13 Spring Care Inc.
9.2.14 Talkspace Inc.
By Application
By Technology
By Component
By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
To know more about the research methodology used for this study, kindly contact us/click here.
