Integrating AI into Therapy: How Technology is Enhancing the Therapeutic Process

The integration of AI in therapy offers benefits like improved accessibility, personalized care, and support between sessions. It explores AI tools such as chatbots, virtual reality, and predictive analytics, which enhance the therapeutic experience. The article also highlights how AI provides therapists with valuable insights and time-saving tools while addressing ethical concerns related to data privacy and bias. The future of AI in therapy holds great promise for enhancing client outcomes, while still prioritizing human empathy. Learn more about how AI could replace human therapists by clicking here.

The Use of AI in Therapy

As artificial intelligence (AI) becomes more sophisticated, its role in mental health care is expanding in exciting and transformative ways. AI integration in therapy refers to the use of machine learning algorithms, natural language processing, and other AI technologies to assist or enhance therapeutic processes. This can include everything from AI-driven chatbots that provide immediate support to data analytics tools that help therapists better understand client needs.

The Rise of Technology in Mental Health

The rise of digital tools in mental health care has been propelled by a growing demand for accessible, flexible, and cost-effective mental health solutions. With the increase in people seeking therapy, especially after the global challenges brought by the pandemic, technology is stepping in to help bridge gaps in access to care. AI-powered platforms can provide valuable support to those who may face barriers to traditional therapy, such as financial constraints, geographical limitations, or long wait times.

AI's Expanding Role in Therapeutic Interventions

Today, AI supports therapy in various ways, from supplementing traditional in-person sessions to providing standalone digital interventions. Some AI tools help monitor a client’s mood, deliver reminders for coping strategies, and assist with cognitive behavioral therapy (CBT) exercises. Others, like virtual reality (VR) therapy, allow clients to confront fears in a controlled digital environment, supported by AI systems that adjust scenarios based on the client's responses. Through these advancements, AI is not replacing therapists but rather serving as an accessible extension of support—offering real-time feedback and actionable insights to clients and practitioners alike.

The integration of AI in therapy signifies a profound shift in mental health care, making it more available, personalized, and responsive to individual needs. As we explore the ways in which technology complements human empathy and clinical expertise, we’re uncovering new pathways to healing and self-discovery for people around the world.

Types of AI Tools Used in Therapy

The integration of AI in therapy has brought about a range of tools designed to support clients in novel and accessible ways. Below is an overview of some of the primary AI applications in mental health care and how advanced technologies such as natural language processing (NLP) and machine learning (ML) enable them to offer personalized responses and valuable insights.

1. Chatbots and Virtual Therapists

AI-driven chatbots, like Woebot and Wysa, are designed to simulate conversational interactions with users, offering emotional support, guidance, and coping strategies. These chatbots can provide quick, on-demand support outside of scheduled therapy sessions, allowing individuals to access help whenever they need it. By engaging users in guided conversations, chatbots can walk them through therapeutic exercises, like cognitive behavioral techniques, to address issues such as anxiety, stress, and depression.

Natural Language Processing (NLP) powers these chatbots, enabling them to understand and respond to text input in a way that feels natural and supportive. NLP allows the AI to analyze and interpret language patterns, detect emotional cues, and respond with empathy—emulating aspects of human conversation. By continuously learning from user interactions, chatbots improve their responses over time, making them more effective at providing tailored support.

2. Virtual Reality (VR) Therapy Environments

In virtual reality therapy, clients use VR headsets to enter immersive, controlled environments where they can confront fears, practice social interactions, or experience relaxation exercises. VR therapy is especially helpful for treating conditions like post-traumatic stress disorder (PTSD), phobias, and social anxiety. For instance, a VR program might allow a client to gradually face fear-inducing scenarios, like public speaking or flying, while a therapist monitors and guides the session.

Machine learning algorithms enhance VR therapy by dynamically adjusting scenarios based on a client’s real-time responses. By tracking metrics like heart rate or response speed, the AI can tailor the environment to be more or less challenging, helping clients progress at their own pace. This data-driven adaptability makes VR a powerful tool for exposure therapy and desensitization exercises.

3. Predictive Analytics for Mental Health

Predictive analytics tools use machine learning to analyze client data and identify patterns that can signal mental health trends or predict potential crises. These tools can process a variety of data—such as self-reported mood logs, behavioral patterns, and even social media posts—to detect shifts that might indicate a decline in mental health. For example, an app might monitor changes in language or sentiment and alert therapists if a client is showing signs of heightened stress, depression, or risk of self-harm.

Machine learning plays a crucial role in these applications by building predictive models from vast amounts of data. These models can uncover trends and correlations that might otherwise go unnoticed, helping therapists make more informed decisions about treatment plans and early interventions. With predictive insights, therapists can proactively adjust their approach, providing more personalized and timely care.

4. Sentiment Analysis and Mood Tracking Apps

AI-based mood tracking apps, such as Ginger and Moodpath, help users keep track of their emotions over time. By asking clients to record daily feelings or complete periodic assessments, these apps collect data that can reveal patterns in mood, stress levels, and coping mechanisms. Some apps use sentiment analysis to review text entries, identifying emotional themes and providing users with insights about their mental health over time.

Natural language processing and machine learning enable these apps to process language, recognize mood patterns, and offer personalized recommendations for mental health improvement. This allows individuals to gain awareness of their emotional fluctuations and learn how to manage triggers or stressors more effectively.

5. AI-Powered Tools for Therapists

In addition to client-facing applications, AI tools designed for therapists can significantly enhance clinical practice. Tools such as AI-driven transcription services or note-taking systems help therapists by reducing the time spent on administrative tasks. Additionally, some platforms provide analytics dashboards that give therapists insights into clients’ progress over time, making it easier to identify effective treatment strategies.

Through machine learning, these tools are capable of analyzing trends across sessions, pointing out progress indicators or areas that may require attention. For example, by analyzing session data, an AI-powered dashboard might highlight that a client’s self-reported anxiety has been consistently high on certain days, allowing therapists to further explore underlying causes.

Empowered by Natural Language Processing and Machine Learning

NLP and ML lie at the core of these applications, enabling AI tools to interpret and respond to language, adapt to user needs, and generate valuable insights for both clients and therapists. These technologies continuously learn and refine their performance, creating an ever-evolving support system that brings efficiency, personalization, and immediacy to the therapeutic process.

Benefits of AI in Therapy

The integration of AI into mental health care offers numerous benefits that make therapy more accessible, personalized, and continuous. These advancements are transforming how people receive support, breaking down barriers, and enhancing the therapeutic experience in impactful ways. Here are some key benefits:

1. Accessibility

AI-powered tools play a critical role in expanding mental health care to underserved populations, particularly in areas where traditional therapy may be unavailable or prohibitively expensive. Through AI chatbots, virtual therapy platforms, and mobile apps, individuals can access mental health resources and support regardless of their location, income, or physical limitations. For example:

• Remote Access: AI-based tools, available on smartphones or computers, allow individuals in rural or remote areas to access mental health support without needing to travel long distances. This is particularly valuable for people who face geographical or logistical barriers to traditional therapy.
• Affordability: Many AI-driven apps offer free or low-cost services, making mental health support more financially accessible. This can be a game-changer for people who cannot afford regular sessions with a licensed therapist.
• Overcoming Stigma: For those who may feel apprehensive about seeking in-person therapy due to stigma, AI-powered mental health apps offer an anonymous, private space where they can begin exploring therapeutic resources.

By breaking down these barriers, AI is helping people access critical mental health care, creating opportunities for early intervention and support that might otherwise be out of reach.

2. Personalization

One of AI’s greatest strengths in therapy is its ability to analyze data and personalize the therapeutic experience. Traditional therapy requires time to understand a client’s unique needs and preferences, but AI can rapidly gather insights from data to create highly tailored approaches. Here’s how this personalization works:

• Tailored Treatment Plans: AI algorithms can process data from self-reported mood logs, behavioral patterns, and engagement with therapy exercises to design individualized treatment plans. For example, an AI-based app may recognize that a user responds well to specific CBT exercises and suggest similar activities in future sessions.
• Dynamic Adaptation: Machine learning enables AI systems to adapt interventions in real time. As the AI learns about a user’s preferences and progress, it can adjust its responses and recommendations to best support the user’s evolving needs.
• Precision in Identifying Issues: AI can highlight patterns in language, mood, and behavior that might signal underlying mental health issues. This helps identify triggers, stressors, and effective coping strategies more quickly, enabling more focused and effective support.

By offering personalized recommendations, AI tools help create a therapeutic experience that is as unique as each client, empowering them to engage with their treatment in ways that resonate most with them.

3. Support Outside Sessions

AI-based mental health apps provide a unique benefit by offering continuous support outside of scheduled therapy sessions. Traditional therapy typically involves weekly or bi-weekly appointments, leaving gaps where clients may lack real-time support or reminders to practice therapeutic techniques. AI tools help bridge these gaps, providing round-the-clock assistance and encouragement.

• Real-Time Mood Tracking: Many AI apps allow users to log their emotions, thoughts, and experiences throughout the day. This constant monitoring helps individuals become more aware of their mental state and track their progress over time, fostering self-reflection and growth.
• Reminders and Coping Techniques: AI companions can send personalized reminders to clients to practice relaxation exercises, mindfulness, or breathing techniques. For example, if a client reports high stress in the evening, an AI app might suggest a calming activity at that time of day.
• On-Demand Support: AI chatbots and virtual assistants are available 24/7, providing a source of immediate comfort and guidance in moments of distress. These tools can walk clients through calming exercises, help them reframe negative thoughts, or simply provide a listening ear until they can next meet with their therapist.

This continuous, on-demand support helps clients feel more connected and supported between sessions, reinforcing therapeutic practices and promoting a proactive approach to mental health management. Through these benefits—greater accessibility, personalized treatment, and real-time support—AI is playing an essential role in enhancing mental health care. By making therapy more adaptable and available, AI is helping more people embark on their mental health journeys with the support and resources they need to thrive.

AI-Assisted Tools for Therapists

AI tools are not just transforming the client experience but are also proving invaluable to therapists by streamlining administrative tasks, enhancing insights, and allowing for more meaningful client interactions. Here are some of the main ways AI is assisting therapists:

1. Enhanced Insights Through Data Analysis

AI tools can analyze session data to provide therapists with deeper insights into each client’s journey, often revealing patterns and connections that might otherwise be missed.

• Identifying Trends and Patterns: AI systems can process large volumes of client data, such as mood logs, session notes, and behavioral patterns, to identify recurring issues or progress over time. For instance, if a client’s anxiety peaks consistently on specific days or after certain activities, AI can highlight this trend, allowing the therapist to focus on potential triggers.
• Progress Tracking: With AI-powered analytics, therapists can track progress over weeks or months, observing changes in mood, behavior, or engagement with treatment exercises. This quantitative feedback helps therapists adjust treatment plans based on real, data-driven insights.
• Customized Intervention Suggestions: Some AI tools provide suggestions based on collected data, such as specific therapeutic exercises or interventions that align with the client’s progress. By recommending these tools, therapists can ensure that their approach is always aligned with their client’s unique needs, optimizing outcomes.

AI-powered data analysis essentially acts as a second pair of eyes, giving therapists a more comprehensive understanding of the client’s mental health landscape. This enriched perspective supports faster, more targeted interventions.

2. Time-Saving Benefits Through Note-Taking and Transcription Tools

Administrative work, such as session notes and transcription, can take up a significant portion of a therapist’s time, often limiting the hours available for direct client interaction. AI-powered note-taking and transcription tools are designed to alleviate this burden, allowing therapists to focus more on the client rather than on documentation.

• Automated Transcriptions: AI transcription tools can convert spoken conversations from sessions into written text, producing session records in real-time. This allows therapists to review past sessions quickly, without needing to take notes during the session itself. For example, tools like Otter.ai or Rev are popular transcription services that convert audio into text, saving hours that would otherwise be spent on manual transcription.
• Summary and Note-Taking Assistance: Some advanced AI tools can analyze transcriptions and provide concise summaries of key points discussed during a session. This can include identifying recurring themes or highlighting emotional expressions. With these summaries, therapists can capture session highlights without having to manually sift through every detail, making case review more efficient.
• Reminders for Follow-Up: Certain AI tools can set reminders for specific follow-up actions, helping therapists stay on track with treatment plans. If a therapist sets a goal with a client, such as practicing a certain coping skill, AI can track this and remind the therapist to discuss the client’s progress in future sessions.

By reducing the amount of time therapists spend on documentation, AI enables them to devote more of their energy to client care and engagement. It also reduces the cognitive load associated with trying to remember every detail from past sessions, allowing therapists to fully concentrate on each session’s present moment. Through these data-driven insights and time-saving tools, AI is enhancing the therapist’s ability to provide targeted, efficient, and empathetic care. These tools don’t replace the therapist’s expertise or intuition but rather complement their practice, improving productivity and expanding the therapeutic experience for clients.

Improving Client Engagement and Outcomes

AI-integrated therapy tools are increasingly enhancing client engagement and promoting positive outcomes, particularly through interactive apps and platforms that resonate with clients of all ages. These tools often incorporate gamification, real-time feedback, and adaptive personalization, making therapy more approachable and less intimidating, especially for younger clients who are comfortable with digital interfaces.

1. Engagement Through Interactive Apps and Platforms

Interactive apps and platforms designed with AI offer features that actively engage clients, encouraging them to participate more fully in their therapeutic journey. This engagement is particularly beneficial for young clients, who may find traditional therapy intimidating or less relatable.

• Gamification and Incentivization: Many AI-powered mental health apps incorporate gamified elements, such as earning points for completing tasks or receiving virtual rewards for reaching milestones. These incentives create a sense of achievement, making it more likely that clients will stay engaged and actively participate in therapeutic exercises.
• Real-Time Feedback and Personalized Reminders: AI apps can offer immediate feedback on mood entries, journal logs, or exercises, providing clients with a sense of progress. Personalized reminders to practice techniques or check in with their emotions make it easier for clients to form positive mental health habits and keep therapy top of mind outside of scheduled sessions.
• Adaptive User Experience: By using machine learning, these apps can adapt to each client’s preferences, offering tailored exercises or content based on previous engagement. For instance, if a client responds well to meditation but less to journaling, the app may prioritize meditation exercises to keep the client engaged.

These interactive elements help foster a stronger therapeutic alliance and increase clients’ investment in their own mental health journey.

2. Studies on Improved Outcomes and Reduced Dropout Rates

Emerging research highlights the positive impact of AI-integrated therapy tools on client engagement, outcomes, and therapy retention. Studies suggest that these tools can improve mental health metrics while also helping reduce dropout rates, a significant challenge in traditional therapy.

• Improved Outcomes and Engagement: A study conducted on the Woebot app, an AI-driven mental health chatbot, found that users experienced significant reductions in anxiety and depression symptoms within two weeks of engagement. The study showed that the immediate, non-judgmental feedback provided by the chatbot contributed to a positive therapeutic experience for users, leading to better engagement and faster results (Fitzpatrick et al., 2017).
• Reduced Dropout Rates: Research suggests that integrating digital platforms in therapy can help lower dropout rates, particularly among younger users who appreciate the flexibility of on-demand support. For example, a meta-analysis of digital mental health interventions found that these tools were effective in reducing dropout rates compared to traditional therapy alone, especially among clients in the 18-35 age range (Torous et al., 2018).
• Increased Access and Usage in Adolescent Populations: A 2021 study published in JMIR Mental Health found that digital therapy interventions, including AI-supported programs, significantly improved engagement and outcomes in adolescents with depression and anxiety. Adolescents reported feeling more comfortable discussing sensitive topics through digital platforms, which allowed them to engage more openly and consistently in their therapy (Montagni et al., 2021).

These studies underscore the benefits of AI-integrated tools, not only in enhancing engagement but also in supporting lasting outcomes. By making therapy more accessible, responsive, and relevant to today’s digital age, AI is helping therapists retain clients and maximize the therapeutic process’s effectiveness. Through interactive, adaptable experiences, AI-driven platforms are transforming therapy into a more engaging and approachable process. Research shows that these tools can lead to improved mental health outcomes and increased retention, demonstrating the potential of AI to enhance both client engagement and long-term wellness.

Challenges and Ethical Considerations

The integration of AI in therapy presents a range of challenges and ethical considerations that must be carefully addressed to ensure client safety, privacy, and fairness. As these tools become more prevalent, it’s essential to balance innovation with responsibility, particularly in the sensitive area of mental health care.

1. Data Privacy and Security Concerns

AI-driven mental health tools often require access to personal and potentially sensitive data, including information about clients’ mental health, mood patterns, and therapeutic history. This raises critical concerns around data privacy and security.

• Data Storage and Protection: AI tools rely on data to function effectively, which means client information must be stored, often in cloud-based servers. This poses a risk of data breaches or unauthorized access, which could compromise client confidentiality. Ensuring robust encryption, secure data storage practices, and strict access controls are essential steps to protect this sensitive information.
• Data Ownership and Transparency: Clients may not always fully understand what data is being collected, how it’s used, or who ultimately has access to it. Transparency around data collection, usage, and storage practices is essential to build trust. Clients should always have access to clear information regarding their rights to data privacy, including options to delete or withhold certain data.

2. Potential Bias in AI Algorithms

AI algorithms are trained on large datasets, but if these datasets are unbalanced or limited in diversity, they may introduce bias into therapeutic recommendations or responses. This is particularly concerning in mental health, where one-size-fits-all approaches may overlook or misinterpret the needs of marginalized groups.

• Bias in Datasets: AI models trained on data that lacks diversity can unintentionally produce biased outcomes. For example, a chatbot trained predominantly on data from one demographic may not fully understand or respond effectively to individuals from other cultural or socioeconomic backgrounds. This can lead to misunderstandings, alienation, or ineffective support for diverse client populations.
• Inadvertent Stereotyping and Overgeneralization: If not carefully monitored, AI might reinforce harmful stereotypes or make assumptions about clients based on limited data. Human oversight is crucial to identify and correct these biases, ensuring that AI-driven tools offer fair, unbiased support across all demographics.

To minimize bias, developers should prioritize diverse datasets and conduct regular reviews to monitor for unintended discriminatory patterns. Transparent reporting on algorithmic accuracy and fairness is also essential.

3. Need for Human Oversight

While AI can provide valuable support in therapy, it cannot replace the nuanced judgment and empathy that human therapists bring to mental health care. Human oversight is crucial to avoid potentially harmful situations and ensure that clients receive appropriate support.

• Understanding Complex Situations: Mental health issues are often complex, and an algorithm may miss nuances that a human therapist would catch. For example, subtle shifts in tone or context might indicate a risk that requires immediate attention, which AI alone might not fully recognize. Human oversight allows for intervention in these critical moments.
• Boundary of AI Responsibility: AI tools can support, but they should not assume the responsibility of making therapeutic decisions independently. Establishing clear guidelines for when AI can provide feedback or suggestions—and when a human therapist needs to intervene—is vital for maintaining safety and efficacy.

4. Ethical Standards and Informed Consent

Ethical standards in AI-integrated therapy emphasize the need for informed consent, transparency, and the safeguarding of client autonomy and confidentiality.

• Informed Consent: Clients should be fully informed about how AI is used in their therapy, including any data collection practices, potential risks, and limitations. Clear and accessible consent forms should outline these aspects, allowing clients to make an informed decision about using AI-based tools.
• Confidentiality and Client Autonomy: Maintaining client confidentiality is paramount, even in AI-driven environments. Therapists and developers must ensure that AI tools comply with existing confidentiality laws and best practices, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States. Clients should have control over their data and the right to opt out of AI-based interactions if they wish.

Navigating these data privacy concerns, algorithmic biases, and ethical considerations is essential for the responsible integration of AI into therapy. By prioritizing transparency, human oversight, and ethical standards, mental health professionals and developers can help ensure that AI enhances rather than compromises the therapeutic process.

Future of AI in Therapy

The future of AI in therapy holds exciting possibilities, with advancements in technology expected to deepen AI’s role in mental health care, transform therapeutic models, and redefine the therapist’s role. As AI becomes more sophisticated, it could enhance both the therapeutic process and outcomes, ultimately benefiting clients and mental health practitioners alike.

1. Advanced Emotional Intelligence and Behavioral Insights

Future AI systems are likely to evolve beyond current text-based interactions, incorporating more advanced emotional and behavioral analysis to better understand clients’ needs.

• Reading Emotional Cues: Emerging technologies in sentiment analysis and affective computing may enable AI to recognize emotional cues through voice tone, facial expressions, and body language. For example, advanced AI could detect subtle shifts in a client’s tone or facial expressions during sessions, providing insights into their emotional state that therapists could consider when offering guidance or interventions. This could enhance AI-driven tools’ capacity to empathize and respond appropriately, making digital support feel more human and tailored.
• Deeper Behavioral Insights: With access to more complex data, future AI tools could provide in-depth behavioral insights, predicting patterns or potential triggers. For instance, AI might alert therapists to emerging trends in a client’s behavior that suggest they are at risk of relapse or encountering new challenges, even before the client articulates these issues. Such insights could help therapists proactively address concerns, making therapy more preventive and responsive.

This level of sophistication could bridge some of the gaps that currently exist between AI and human intuition, helping to create a more holistic understanding of each client.

2. Influence on Therapist Training and Skill Development

As AI tools become more integrated into therapy, therapists will need new training to understand and utilize these technologies effectively. This could involve learning to interpret AI-provided insights, understanding algorithmic limitations, and developing skills to manage AI-human interaction in therapy.

• Training on AI Integration and Data Interpretation: Therapists may need specialized training to interpret AI-provided data on client behavior, mood, and emotional cues, as well as to integrate these insights with traditional therapeutic models. Training programs might include courses on digital literacy, data interpretation, and the ethical implications of using AI, ensuring that practitioners can navigate AI-driven therapy responsibly and skillfully.
• Developing Collaborative Skills with AI Systems: Just as therapists work with other professionals to support clients, future training may include guidance on collaborating with AI systems. Therapists will need to know when and how to rely on AI insights, when to intervene manually, and how to communicate these choices transparently to clients. This shift could change the way therapists approach treatment, viewing AI as a partner in the therapeutic process rather than merely a tool.

These advancements could reshape the therapist’s role, enhancing the depth and precision of the care they provide while also making mental health support more accessible.

3. Redefining Therapeutic Models and Expanding Accessibility

The future of AI in therapy could see the emergence of new therapeutic models, with AI serving as both a supplement and an alternative to traditional approaches, potentially making mental health care more accessible to a broader population.

• Hybrid Therapy Models: With AI handling some aspects of therapy, such as initial assessments or ongoing monitoring, a hybrid model could emerge that combines automated and human-led support. In this model, clients might complete certain therapeutic exercises through AI apps, while receiving in-person or virtual support from therapists for complex or sensitive issues. This blended approach could make therapy more flexible, efficient, and available to more people.
• Increased Accessibility for Underserved Populations: Future AI systems may be capable of providing more nuanced support for those who lack access to traditional therapy, whether due to geographic, financial, or social barriers. As AI tools become more sophisticated, they could offer high-quality mental health support on a larger scale, reaching populations in underserved or remote areas and providing them with affordable, accessible care.

4. Transforming the Broader Mental Health Landscape

The continued evolution of AI has the potential to reshape the mental health field as a whole, influencing not just individual therapy but also larger mental health systems and policies.

• Data-Driven Policy and Public Health Initiatives: With aggregated, anonymized data from AI-driven therapy platforms, researchers and policymakers could gain insights into mental health trends on a population level, identifying emerging needs or at-risk groups. This data could inform public health initiatives, resource allocation, and mental health policies, allowing for more targeted and effective interventions.
• Enhanced Precision in Mental Health Diagnostics: As AI systems improve, they could help refine diagnostic criteria for mental health conditions by identifying patterns that aren’t apparent through traditional diagnostic methods. This could lead to more accurate and personalized treatment plans, better outcomes, and a deeper understanding of mental health disorders.

The future of AI in therapy promises to create more dynamic, responsive, and accessible mental health care, enhancing the work of therapists and supporting a broader range of clients. By embracing these advancements responsibly and thoughtfully, the mental health field can harness the potential of AI to transform therapy and contribute to a healthier, more supported society.

Embracing AI in Therapy

AI is transforming the landscape of therapy, offering innovative tools that enhance access, personalization, and support both inside and outside the traditional therapeutic setting. By automating certain tasks, providing real-time feedback, and offering insights into behavioral patterns, AI has the potential to empower both therapists and clients, making mental health care more efficient and accessible.

However, as we welcome these advancements, it’s essential to remember that AI cannot replace the irreplaceable—the human empathy, nuanced understanding, and ethical discernment that therapists bring to their practice. A balanced approach that integrates AI’s strengths with human expertise will ensure that mental health care remains compassionate, ethical, and client-centered.

As AI technology continues to evolve, staying informed about these developments can help individuals make educated decisions about their mental health care options. By embracing AI responsibly, we can support a future where technology and human empathy work hand in hand to create a more inclusive, effective, and transformative therapeutic experience.

More Resources

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Artificial Intelligence in Behavioral and Mental Health Care

About the Author

Jessica Taylor is a licensed therapist and board certified coach who contributes to the promotion of mental health and addiction awareness by providing educational resources and information.