As artificial intelligence was introduced into practically every industry, there were inevitable concerns and disruptions. Of course, when AI enters an extremely sensitive area like healthcare – one that deals with people’s personal data – the discussion gets even more heated. But using AI in mental health services takes it all up a notch – after all, this is where you deal with the patient’s most intimate feelings, plus, some conditions are still (lamentably) stigmatized. On the other hand, while mental healthcare used to be an art form in the times of Freud, AI is extremely valuable in that its impartiality allows it to elevate the data-backed, scientific side of the profession.
And this approach is already bearing fruits. In this article, we discuss where AI is used for mental health, what its strong sides are, what the challenges are, and draw on our own experience at Lionwood.software as a healthcare AI development services provider.
The overall state of AI in healthcare and mental healthcare
The global AI in healthcare market in general (including, but not limited to, mental health) had reached $19.27bln already by 2023, and is projected to grow at a CAGR of 38.5% until 2030. The breakthrough started with note-taker or “scribe” applications, developed both by giants like Microsoft and Oracle, as well as startups like Corti. Soon, however, AI found its way into areas closer to the core: diagnosis, treatment, and mental health research. Mental health was a part of that trend early on: in 2023, the market was estimated at $1.13bln, with countless startups appearing through 2023-2025 and institutions starting to adopt artificial intelligence where it’s relatively safe. Of course, concerns persist about privacy and accuracy, but these are now studied increasingly rigorously, as the world is preparing for future regulations.
What kinds of artificial intelligence are used for mental health?
Importantly, AI itself is quite a diverse group of technologies, and what’s truly fascinating is that most of them are already being used for mental health in some way or another.
- NLP (Natural Language Processing) found its niche not just in chatbots but also in sentiment analysis, perhaps spurred by the similar development in marketing.
- Predictive analytics technologies can then be applied to detect early signs of conditions like depression or anxiety, etc. Notably, IBM Watson Health is now using AI to assess mental health risks in this way.
- A more exotic type is facial and voice recognition that allows to identify micro-expressions and intonation, which is good for things like suicide prevention. Examples include COgito Companion and CBT platforms like Ginger.
- Generative AI is also used quite successfully, by creating personalized self-help content, guided meditation scripts, and the like – so that the patient does not need to read an entire 500-page self-help book where they only really need 15 pages that are scattered across the tome.
What roles can AI play in mental healthcare?
With these diverse types of AI, it’s only natural that it’s used in several important roles in mental healthcare. What unites them is that they all rely on what AI is best at: (a) processing large amounts of data and finding patterns without inherent biases; (b) automating processes that would traditionally require another paid specialist, thus making mental health services more accessible.
AI as a therapist
This is the closest artificial intelligence gets to being in the doctor’s chair, and its ticket to this role is it being available 24/7, unlike anyone except the most fanatically dedicated professionals. Since methods like cognitive behavioral therapy require that sort of dedication, AI-led interventions allow users to manage stress, anxiety, depression, and other conditions without having to phone their therapist all the time.
Artificial Intelligence in mental health research
The power of AI in analyzing large datasets means they can detect and (preliminarily) diagnose conditions based on diverse types of information, from social media activity to brain scans, genetic data, and clinical records. In practice, this translates into artificial intelligence being engaged in epidemiology (via sentiment analysis), diagnostics, and drug discovery. AI can also simulate brain activity and predict treatment responses in some cases, but these still require more human-led research.
AI chatbot for mental health
As a “lightweight” version of the digital therapist role, chatbots, too, cater to the need for availability and accessibility, and are now typically used as a first line of support. For example, Lionwood.software developed a chatbot that can provide first-line assistance but redirects the user to a human therapist in case their condition is assessed as critical or requiring further investigation, to make mental health services accessible to Ukrainian teenagers.
Artificial Intelligence for scheduling and administrative tasks
Finally, the most widely accepted role of AI in mental health is that of a trained “secretary” who can assess priorities, automate appointment scheduling, deal with patient documentation, retrieve data, and work with insurance claims. Prioritization is, of course, the most interesting aspect here, since predictive analytics trained on epidemiological and nosological data can really optimize appointment scheduling by identifying those patients who are under the highest risk.
Can AI replace mental healthcare practitioners?
In short: not entirely. Mental healthcare originally evolved as a science/art mix that requires empathy, so the technology is a welcome addition – but merely an addition nonetheless. The sound strategy is (at least for now) to complement healthcare practitioners’ work with 24/7 support and crisis detection, but there’s always a human in the loop.
However, AI has successfully done some tasks in the therapist’s chair, and the question of how far it can go (again, for now, as most countries still lack regulations) largely depends on several factors: data, ethics, public perceptions, and the actual list of conditions it’s successful in helping handle.
Where does the data come from?
The first and most important question here is data. When AI is pushed into the therapist’s role it needs to juxtapose the data it has acquired from case studies and other sources with what it gets from (and about) the patient themselves. These include:
- clinical records and EHR
- self-reports, including what’s retrieved using NLP and voice recognition
- possibly wearables and IoT devices
- social media
A lot here depends on how the data is structured and prompted, especially with surveys and chatbot interactions. Social media is a more controversial topic, but has been explored as a potentially efficient method, especially if the model is trained to recognize language patterns and online behavior in general.
All of this, however, is tightly linked with privacy and security concerns. However, using the technology in mental health services raises significant privacy and security concerns. Sensitive mental health data must be handled with strict encryption and compliance with regulations such as HIPAA (Health Insurance Portability and Accountability Act) in the U.S. and GDPR (General Data Protection Regulation) in Europe. There is also the risk of data breaches, unauthorized access, or misuse of personal health information, which can lead to discrimination or stigmatization.
Another critical issue is user consent and data transparency, because patients are likely not to be aware of how their data is collected, processed, or shared. This is why any use of AI in healthcare will likely be regulated very soon, and for now, developers are told to stay in the safe waters, largely based on successful precedents.
Ethical considerations and public perceptions
The ethical considerations, then, influence public perceptions. What’s intriguing here is that the very impartiality of technology that makes it unlikely to sit in the therapist’s chair anytime soon is also a major factor that makes people pro-AI when it comes to mental health advice. In other words, while traditional human-led therapy had its own fair share of ethical concerns, these are largely cancelled out by AI, but then compensated by the new ones that the technology created itself.
The short list includes privacy and confidentiality, biases, informed consent, and accountability. In terms of public perceptions, a survey involving 466 participants, revealed that only 35% had ever consulted a mental health professional – meaning these challenges were already present before the tech entered the picture.
And many patients feel that artificial intelligence is more trustworthy, or at least as trustworthy as a human therapist. Some also feel more secure when confiding their feelings to an impersonal being.
However, there’s also a generic aura surrounding AI; in the UK, it’s mostly the younger citizens, aged 18-34 who express apprehension that AI as such is a negative factor for mental health (16% in this age group versus 10% of 55+ year-olds).
The conditions AI can help with
Meanwhile, there are several common conditions AI can help with already, in various ways:
- Depression: chatbots have proven their worth in CBT support, mood tracking, and providing personalized coping strategies;
- Anxiety: AI is not bad in providing guided meditation, breathing exercises, and real-time emotional analysis;
- PTSD: Sentiment analysis helps monitor emotional triggers, plus, coupled with VR, artificial intelligence can help create the setting for exposure therapy;
- OCD: there are already exposure response prevention apps;
- Bipolar: AI models can track fluctuations between episodes based on NLP and wearable data;
- Schizophrenia: mostly early symptom detection from pattern recognition, plus medication adherence monitoring;
- Addictions: there are attempts, some successful, at creating relapse prevention tools with behavioral coaching;
- Eating and sleep disorders: pattern recognition allows personalized counselling.
How artificial intelligence is already used in mental healthcare: indicative cases
Woebot – AI-Powered CBT Therapy
Woebot is an AI chatbot that provides cognitive behavioral therapy (CBT) support to users experiencing depression, anxiety, and stress. Available 24/7, it engages users in conversations, tracks mood patterns, and offers evidence-based therapeutic techniques. Studies show that users report reduced symptoms of depression and anxiety after consistent interaction with Woebot.
Ellipsis Health – Mental Health Diagnosis
Ellipsis Health uses AI-driven voice analysis to assess emotional and mental health problems such as depression and anxiety. By analyzing tone, speech patterns, and word choice, the platform provides clinicians with insights into a patient’s emotional state. This technology helps in early diagnosis and remote patient monitoring, improving accessibility to mental health assessments.
NoTrivia – Mental Health Chatbot for Ukrainian Teenagers
NoTrivia is a chatbot designed to provide free mental health support to Ukrainian teenagers, particularly during the country’s ongoing crisis. Available on Telegram, it connects users with professional psychologists and offers resources to navigate emotional challenges. Initially hindered by scalability issues, Lionwood helped by developing a custom CRM system and expanding the chatbot’s capabilities to handle more complex user needs. This project aims to provide urgent mental health assistance to youth affected by war and difficult circumstances.
Mindstrong – Mood and Cognitive Tracking
Mindstrong Health utilizes AI to analyze smartphone usage patterns, such as typing speed and screen interaction, to detect cognitive and mood changes. This allows for early detection of mental health conditions, particularly in individuals with depression, bipolar disorder, or schizophrenia. By continuously monitoring behavioral data, Mindstrong helps clinicians intervene before symptoms escalate.
VR Therapy for PTSD – AI-Driven Virtual Reality Exposure
AI-enhanced Virtual Reality Exposure Therapy (VRET) is used to help PTSD patients confront and process traumatic memories in a controlled, immersive environment. Programs like Bravemind, developed by the University of Southern California, use AI to adapt VR scenarios to an individual’s specific trauma. This method has shown promising results in reducing PTSD symptoms among veterans and trauma survivors.
Conclusions
Overall, the future of artificial intelligence in mental health looks very promising – both from the medical and philanthropic point of view. On the one hand, its ability to detect patterns allows for better early diagnosis and drug discovery; on the other, automation makes mental healthcare more accessible to the wider demographics. As of today, there still are privacy concerns and regulatory uncertainty, but these are the inevitable companion for every innovation.
At Lionwood.software, we define working on AI-driven healthcare solutions as one of our professional priorities. If you’re looking to innovate and enhance mental health support with AI, let’s collaborate to build impactful, cutting-edge solutions – we suggest starting with a discovery stage to let your product absorb the best practices currently generated by dozens of successful projects.
