AI in Mental Healthcare: How Is It Used and What Are the Risks?

Over 60 million Americans report experiencing mental illness, and at least 25 percent of these patients are unable to access care, typically due to cost. Clinical researchers and tech startups alike are seeking to mend these gaps with artificial intelligence, leveraging the technology to improve early diagnosis, offer personalized treatments and follow up with at-risk patients. Results have been promising — research has suggested that AI tools can improve a wide range of patient outcomes, from coping with anxiety to quitting smoking.  

However, the introduction of AI mental health tools has not been without challenges. On multiple occasions, AI companions have been criticized for inciting violence and self-harm among teen users. Even lauded AI interventions have raised questions about privacy and the ethics of trusting AI analysis of human behavior. 

 

How Is AI Used in Mental Health?

The use of AI in mental healthcare dates back to the development of early therapy chatbots such as Alan Turing’s ELIZA in the 1960s, which mimicked therapist-patient interactions using early natural language processing (NLP) technology. In the early 2000s, machine learning, sentiment analysis, speech recognition and wearable technology became prominent avenues for providing mental health support. Today, AI is broadly implemented in mental healthcare, offering support like: 

Cognitive Behavioral Therapy

Since the 2010s, apps like Woebot and Wysa have offered AI therapy to users. These tools are designed to offer immediate support and reduce barriers to seeking help, such as stigma or cost. Research suggests that AI-powered cognitive behavioral therapy (CBT) — delivered via mobile apps or desktops — offers results comparable to traditional CBT. 

While AI is not a substitute for human therapists or clinical judgment, its applications have expanded the boundaries of what’s possible in mental healthcare. Encouragingly, the FDA recently cleared a digital therapeutic to support antidepressant medication to treat major depressive disorder, signaling a growing acceptance of AI as a legitimate treatment for certain mental illnesses.

Early Detection

Today, deep learning and predictive analytics have opened new possibilities for detecting mental health conditions through data sources like social media posts, smartphone usage patterns and physiological data from wearables. 

For example, the Detection and Computational Analysis of Psychological Signals (DCAPS), which analyzes language, physical gestures, and social signals via natural language processing, machine learning and computer vision, assesses post-combat soldiers in need of mental healthcare. Additionally, AI models assess speech patterns and facial expressions to identify and predict conditions like Alzheimer’s disease. 

In the United Kingdom, Limbic designed an AI-powered psychological assessment and triage tools in clinical settings with the UK’s National Health Service. According to the company, the e-triage tool has achieved an accuracy rate of 93 percent when diagnosing eight common mental illnesses — including PTSD and anxiety. As a result, patients need fewer treatment adjustments over time, and clinicians report saving over 10 minutes per referral.

Neurological Analysis

AI is already used for neurological analysis in mental healthcare, analyzing complex brain and behavioral data to enhance diagnosis and treatment. AI algorithms process neuroimaging data, such as MRI and EEG scans, to detect patterns linked to disorders like depression, schizophrenia and PTSD. These tools also monitor brainwave activity for conditions like ADHD and sleep disorders and predict treatment responses by integrating neurological, behavioral and genetic data. 

For example, recent advancements in computer vision analysis have allowed AI to analyze brain images, creating personalized treatment plans for children with schizophrenia. The cutting-edge method circumvents an extensive, costly testing regimen for determining the best prescription.

Similarly, an AI system called Network Pattern Recognition identifies patient needs by analyzing their responses to survey questions. This technology and others assist mental health providers in making data-informed decisions about treatment plans.

Patient Communication

For many providers, AI has become a critical piece of patient engagement strategy, with providers leveraging AI to field patient phone calls, schedule appointments and deliver health education. 

AI-driven chatbots and virtual assistants, for instance, use natural language processing (NLP) to simulate conversations, offering immediate responses to patient inquiries, guiding them through therapeutic exercises or providing emotional support. These tools can also help patients track their moods, set mental health goals and adhere to treatment plans by sending reminders or motivational messages. Additionally, AI systems analyze patients’ language, tone and word choice to detect signs of mental health issues, such as anxiety or depression, which can guide clinicians in tailoring interventions.

 

Benefits of AI in Mental Healthcare

AI-assisted mental healthcare offers numerous benefits, increasing not only accessibility but also efficiency and precision, according to some studies. These are some examples of the impact AI has had on mental healthcare:

Offering Accessible Care 

Available remotely 24/7, AI therapists provide more affordable and accessible options for patients, free from waiting rooms and overbooked providers. From virtual reality meditation to cognitive behavioral therapy and other forms of talk therapy, the applications of round-the-clock AI care are plentiful in mental health. AI can elevate guided journaling between psychotherapy sessions, for example, or provide real-time coping mechanisms during a panic attack — something human practitioners rarely have the bandwidth to provide. 

“We don’t have enough providers for that many people,” Jessica Jackson, a licensed psychologist based in Texas, previously told Built In. “So even if I am interested, I have to find someone who can see me, who has time on their calendar, and I have to be able to afford it. For some people, that knocks them out of therapy completely.”

Providing Personalized Treatment

Beyond providing access to more generalized self-improvement coaching, AI has the potential to create hyper-personalized treatment in mental healthcare. By analyzing genetic, environmental and behavioral data, AI can recommend interventions tailored to each patient’s unique profile. A chatbot can provide specific psychoeducation on coping with obsessive compulsive disorder (OCD) or adapt a therapeutic approach to address perfectionist tendencies, for example. Or, AI can analyze a patient’s genetic makeup to predict which antidepressant will be most effective. 

Making Accurate Diagnoses

Research by IBM and the University of California revealed that AI has the power to detect various mental illnesses with an accuracy rate between 63 and 92 percent. Accuracy fluctuated depending on the illness being screened and data quality. The lack of clarity surrounding certain mental illnesses, like PTSD, poses a challenge for training mental health diagnostic models. Mental illness presents in diverse and oftentimes unpredictable ways, making it difficult to train a model which symptoms to look for. 

Ongoing Coaching for Therapists

Beyond patient outreach, some providers leverage AI within on-demand training platforms, which can be used to analyze the patient’s and provider’s speech patterns. Such technology gives healthcare professionals feedback on their skills and recommends areas of improvement. 

 

Risks of AI-Assisted Mental Healthcare

Lack of Human Empathy

While AI tools can provide efficient and scalable mental health solutions, they lack the capacity for genuine human empathy. This limitation can affect the therapeutic alliance between patients and providers, often a cornerstone of effective mental healthcare. Balancing AI automation with human involvement is necessary to maintain the emotional connection crucial for mental health treatment.

“AI does not replace the importance of human-to-human interactions that drive human nature and are essential to maintain strong mental health,” Nicole Yeasley, co-founder and COO of KindWorks.ai, previously told Built In. “But AI can augment those deeper human interactions with ongoing lower touch support to improve mental health — like nudges, reminders, exercises and education.”

Gaps in Understanding 

AI systems often excel in identifying patterns within specific, well-defined datasets, but they struggle with the complexity and variability of many mental illnesses. Language barriers or jargon-filled conversations can trip up AI chatbots, while conditions like personality disorders, PTSD or co-occurring disorders require a nuanced understanding that AI may not yet achieve. Additionally, the lack of human experience means AI tools might be less suited to unpacking trauma or navigating complex family dynamics than they are for coaching patients on tried-and-true strategies for navigating stress, anxiety or depression. Addressing these gaps will require ongoing advancements in AI training and integration with expert clinical judgment. 

Unpredictability 

The unpredictability of AI in mental healthcare poses significant risks, as errors or unexpected behavior can have serious consequences for vulnerable individuals. For instance, an AI chatbot designed to provide emotional support may generate harmful or insensitive responses due to biases or misinterpretation of user input, potentially exacerbating a user’s distress. 

In a tragic case, a teen user of a popular AI companion took his life after he was encouraged to commit suicide. Family members of the teen blame his death on the chatbot, with which the boy had shared intimate conversations — including ones about suicidal ideation. In another, parents of a young teen claim an AI chatbot for encouraging the child to engage in violent behavior, like killing his family and self-harming. Cases like this highlight the grave consequences of unregulated, unpredictable AI behavior, which can be difficult to prevent or even understand.

Privacy

The use of AI in mental healthcare involves the collection and analysis of protected health information, such as emotional states, therapy session transcripts, and biometric information. The stakes surrounding data privacy are elevated in a mental health setting, where patients may discuss past trauma or suicidal thoughts. Without robust safeguards, this data could be vulnerable to breaches or misuse, posing significant risks to patient confidentiality. Transparent data governance and strict security measures are essential to protect patient privacy.

AI Bias

AI systems used in mental healthcare are susceptible to bias, as they often rely on training data that may not adequately represent diverse populations. This can lead to disparities in diagnoses and treatment recommendations, potentially exacerbating existing inequalities in mental healthcare access and outcomes. Ensuring AI models are trained on inclusive datasets is critical to mitigating these risks.