Machine Learning Is Bridging the Gap in Mental Health Services

June 12, 2025

AI-powered chatbots are emerging as a scalable solution to address mental health issues in a world where one billion people have mental disorders but lack access to quality care. Machine learning (ML) tools can provide therapeutic guidance in multiple languages, enable early screening through wearable data analysis and predict individual treatment responses. Experts predict that over the next five years, clinically validated AI mental health tools will expand significantly.

Around one billion people globally have mental disorders, and many people don’t have access to the necessary resources or quality services to get help. In lower and middle-income countries, mental health disorders are growing fast, yet they have the fewest clinical therapists available.

To keep pace with the need for mental health services, people are turning to AI-powered chatbots.

“Digital health tools are vital in addressing healthcare inequities,” IEEE member Carmen Fontana said. “For a patient living in a remote, rural area, this may mean providing mental health services via an AI-enabled app on their phone. For a non-native speaker, it means providing generative AI-powered care in their native language.”

This has the potential to transform traditional mental health practices by offering service providers and patients an additional tool for navigating mental healthcare.

“Machine learning has the potential to help bridge the gap between the growing demand for mental health care and the limited availability of providers,” said IEEE Fellow Chenyang Lu. “AI-powered chatbots offer a scalable and accessible way to deliver mental health support, allowing anyone with internet access to receive basic therapeutic guidance and digital therapy, regardless of geographic or economic barriers.”

How ML is Used Within Mental Healthcare

There are several indicators that mental health support remains one of the central ways that people use chatbots.

But how effective are these chatbots at offering the necessary support patients need?

“These models are not built for mental health,” said IEEE member Hui Ding, “however, once they are trained with a high-quality dataset in mental health, satisfying performance in accuracy and efficiency can be achieved.”

Recent advancements in machine learning have introduced new tools designed for addressing mental illness and health. Unlike more general chatbots, these are created to specialize in understanding and processing human language. These medical large language models (LLMs) typically contain more knowledge-based content, such as disease diagnoses, medication recommendations and explanations of diseases.

“More recently, generative AI and large language models have gained attention for their ability to engage in natural, human-like conversations, making them appealing for mental health applications,” said Lu.

These tools can aid mental health specialists in their services and diagnosis by “providing early screening of conditions, such as depression, by analyzing patterns in wearable data and enabling more tailored, effective care by predicting how individual patients will respond to treatments,” he added.

However, these models may lack capabilities in other important aspects in psychiatry, such as therapeutic communication and empathy.

“Machine learning can help to complement the diagnosis of mental illnesses, but it will never replace the final medical evaluation,” said IEEE Senior Member Cristiane Pimentel. “It still requires rigorous ethical and clinical care.”

Addressing Data and Bias Concerns

While the growing use of machine learning in the mental health space is providing solutions for underserved communities, data privacy, accuracy and bias concerns still remain.

Depending on the type of data that a chatbot was trained on, the results may be biased in their answers. For example, Pimentel said that “if a study was conducted in Africa, the conditions of the country and population will probably not be the same as for Canada. This can compromise the reliability and accuracy of the models.”

Another concern, according to Lu, is generalizability.

“Models that perform well within one hospital system or patient population often experience performance degradation or instability when deployed in new settings, which can undermine both accuracy and trust,” he said.

LLMs are also prone to producing fabricated information, known as hallucinations, making their use in clinical settings a problem.

“These models were not originally designed for clinical use. They can ‘hallucinate,’ or generate inaccurate and potentially misleading information, which raises serious concerns about reliability and patient safety,” said Lu. “Ensuring these tools are clinically validated and carefully monitored is essential before they can be safely integrated into mental health care.”

The Future of ML in Mental Healthcare

As ML and AI’s clinical uses grow, the gap between mental health services and patients will only become smaller. To achieve this, though, it’ll take addressing regulatory concerns and implementing guardrails to enable sustainable adoption.

“Over the next five years, we can expect machine learning models to undergo clinical validation and become integrated into both clinical trials and everyday practice, particularly for early screening and personalized treatment selection,” Lu said. “Mental health chatbots will become more reliable and safer by incorporating trustworthy AI methods and leveraging clinically validated mental health data and clinician feedback. Together, these advances will drive a significant expansion of AI-powered digital mental health care, helping to close the growing gap between rising mental health needs and limited care resources.”

Learn More: Check out this article from the IEEE Standards Association focused on the “Five Healthcare and Life Sciences Trends to Watch for in 2025.”