The Promises and Perils of AI in Mental Health Care

Mental health care is facing a crisis of scale. Demand has skyrocketed, driven by rising rates of anxiety, depression, isolation, and burnout across all age groups. Meanwhile, access to qualified professionals remains limited, expensive, and — for many — completely out of reach.

In response, the mental health sector is increasingly turning to artificial intelligence (AI) and machine learning (ML) to fill the gap. These technologies offer the promise of scalable, accessible support for millions of people. But they also raise serious ethical, emotional, and societal concerns.

In my opinion, AI and ML have a powerful role to play in augmenting mental health care — but not in replacing the human connection at its core.

The Promise: Personalised, Predictive Mental Health Support

At their best, AI systems can:

Detect patterns in user behaviour that suggest shifts in mental health (e.g. changes in sleep, activity, social engagement, or language use)
Analyse speech, text, or journal entries to detect tone, sentiment, and key indicators of distress
Predict triggers or relapse risks for individuals living with chronic conditions such as anxiety, PTSD, or bipolar disorder
Provide always-on check-ins, mood tracking, and even guided exercises like breathing, journaling, or cognitive behavioural prompts

These tools, when used ethically and transparently, can act as early warning systems — helping people understand their mental states better and enabling clinicians to intervene before a crisis hits.

From what I’ve seen, AI-driven tools like Woebot, Wysa, and Youper have shown promise in delivering low-intensity support at scale, especially for individuals who might not otherwise engage with traditional therapy.

Where ML Shines: Pattern Recognition and Precision Support

Machine learning models excel at identifying signals in large volumes of data — and mental health is no exception.

With permission and proper safeguards, these models can learn from:

Text messages, voice notes, or journal entries
Biometric data from wearables (e.g. heart rate variability, sleep patterns, physical activity)
App usage trends and digital interactions

Imagine a system that notices subtle cues — a shift in tone over time, decreased social engagement, poor sleep quality — and gently flags the user or their clinician. These predictive insights could enable proactive care, rather than reactive treatment.

In theory, this is where AI offers the greatest value: augmenting human professionals with real-time, data-driven insights to inform personalised care plans.

The Perils: Emotional Substitution and AI Dependency

But the risks are just as real — and they’re growing.

One of the biggest concerns is the rise of AI companions, AI girlfriends, and virtual friends marketed as emotional support systems. Platforms like Replika and various “AI girlfriend” apps have exploded in popularity, particularly among younger and socially isolated users.

These systems simulate empathy, companionship, and affection. And while they can provide comfort, they also pose a serious risk of emotional dependency — where users begin to rely on artificial personas for validation, support, and human connection.

From my perspective, this creates a dangerously hollow version of care. Because no matter how advanced the large language model is, it’s still just mimicking understanding, not truly offering it.

Humans need reciprocity, authenticity, and unpredictability — things no AI, no matter how advanced, can genuinely provide.

The Risk of Bypassing Human Support

There’s also a risk that individuals experiencing distress may delay or avoid seeking real professional help, believing their AI chatbot is “good enough.”

This is particularly dangerous for those dealing with:

Suicidal ideation
Complex trauma
Severe depression or psychosis
Conditions requiring medical oversight or medication

AI tools lack the ethical frameworks, duty of care, and clinical training required to manage such cases responsibly. There’s no therapeutic alliance, no liability, and no accountability when things go wrong.

In my opinion, AI should never be positioned as a replacement for therapy — but as a tool to support or complement it.

Ethics, Data Privacy, and Consent

Another major concern is data privacy.

Mental health data is deeply personal. Entrusting it to a machine — especially one operated by a commercial entity — introduces significant risks:

How is the data stored?
Who has access to it?
Can it be sold or used for marketing?
Is it protected under medical data legislation?

Users must have complete transparency and control over how their mental health data is collected, stored, and used. Anything less is not just unethical — it’s dangerous.

The Future: AI as an Assistant, Not a Therapist

From my perspective, the most responsible role for AI in mental health care is as a companion tool — not a clinician.

Used ethically, AI can:

Help people monitor and understand their mental state
Offer non-invasive, low-risk coping mechanisms
Reduce the stigma around seeking help
Free up human therapists to focus on more complex cases

But it must never pretend to be human. It must never promise what it can’t deliver — genuine empathy, therapeutic judgment, or a duty of care.

Technology With a Human Touch

AI and ML are powerful tools — but they must be applied with caution, transparency, and humility in the mental health space.

They can enhance care, scale support, and offer new ways of understanding ourselves. But they can also isolate us, distract us from real connection, and commodify our most vulnerable moments.

In my opinion, the right approach is a hybrid one: AI for early insights, habit tracking, and low-level support — always paired with human judgment, compassion, and real-world care.

Because while algorithms can recognise patterns, only people can truly understand pain.