Will Robots Replace Human Therapists? Use Cases for Generative AI in Mental Health Care

The mental health industry is experiencing a critical shortage of qualified specialists, particularly in the US, where there is only one mental health professional for every 350 people. Against such grave statistics, the hopes are high for generative AI to bridge the supply and demand gap and find application in mental health.

This blog will look closer at the potential for using generative AI in mental healthcare and its current limitations.

How Could General AI Improve Mental Healthcare?

Using AI technology in mental healthcare isn’t wholly new. Mental health apps, such as Woebot, a chatbot based on the best practices of cognitive behavioral therapy, have been using AI algorithms for years. So how is generative AI different from other present-day AI technologies?

Unlike generative AI, the algorithms used in therapeutic apps like Woebot are rule-based, i.e. trained to follow specific conversational patterns that do not deviate from the range of pre-set scenarios. On the other hand, generative pre-trained language (GPT) models use the already present data to produce output that looks and sounds generic, and Large Language Models (LLM) are capable of processing large verbal data sets.

The capability to sift through verbal and visual data and detect patterns in it can assist mental health practitioners in their work. Below is a list of possible applications of generative AI in mental healthcare.

1. Diagnostics and early disease detection

Generative AI may prove especially helpful in mental health diagnostics. By analyzing patients’ linguistic and speech patterns, generative AI can help mental health practitioners identify patients in crisis. They also can provide valuable insights for determining certain mental health conditions that may be reflected in the patient’s speech. For example, they may detect verbal patterns preceding the beginning of a manic episode and help doctors effectively curb its manifestation.

2. Evaluating the efficiency of treatment

Patients' verbal patterns can also provide insights into the efficiency of treatment. Generative AI can identify early signs of medication effects or subtle changes in phrases and word choice, indicating that cognitive behavioral therapy has a positive impact. This can be done in real-time and, combined with clinical knowledge, can offer valuable insights to therapists and clinicians.

3. Selecting optimal treatment options

Chat GPT can also help mental health practitioners select optimal treatment methods by sifting through the most up-to-date and relevant research to cope with their conditions. As opposed to offering generalized care for a particular mental health ailment, such an approach may help doctors personalize treatment methods by providing treatments for individual symptoms.

4. Providing preliminary consultations

Here’s the big news - many users have already been asking ChatGPT for information about mental health issues and topics. Unfortunately, since ChatGPT isn’t tailored to offer these types of consultations, the info it provides is based on bits and pieces found on the Internet and can't be considered 100% correct. However, if GPT models are fed accurate and clinically proven data, they could be successfully used for offering preliminary consultations and advice to potential patients.

As you can see, generative AI could quickly boost the efficiency of mental health services, improve patients’ experience, and assist practitioners in looking for better treatments.

Generative AI Limitations

Can generative AI replace a human therapist, though? If GPT models can produce initial responses, can they also be trained to offer therapeutic interventions and conduct therapy sessions? The answer is quite ambiguous.?

Many current psychotherapy approaches are based on compassion and human support, and their treatment effects are highly reliant on a level of cooperation and bonding between a patient and a therapist. Most humans have shared experiences, such as pain, attachment, or uncertainty, none of which artificial intelligence has. This renders it inappropriate for a vast number of therapy modalities requiring high levels of connection and empathy. In a nutshell, generative AI is incapable of forming a therapeutic alliance, a primary driving force behind a healing process in therapy.

Moreover, generative AI tools like ChatGPT are still incapable of reading into subtle nuances of human facial expressions and tone. They can miss important cues, which can easily be read by humans. Thus, its use still requires human supervision and should be handled carefully.

Generative AI can prove efficient in manual-based treatment modalities, such as cognitive behavioral or interpersonal therapy, with a ready-met set of interventions that can quickly be learned and applied in a conversation. Still, such language models should be properly trained using clinically approved data and tailored specifically for therapeutic purposes. Casual ChatGPT use, in this respect, can be potentially harmful, even though it can provide general information on mental health topics and spill light on some of the critical issues.

Final Thoughts

Generative AI won’t be replacing human therapists just yet, so we can’t expect it to instantly resolve the mental healthcare crisis which has been underway for decades. Still, It does have much promising potential in boosting the efficiency of mental healthcare services. As such, it can assist mental health professionals in diagnostics, treatment selection, early disease detection, client support, and patient care. It can also be used, although with consideration and precaution, in manual-based therapy modalities, such as cognitive-behavioral therapy.

Need more insights on how you can use AI to enhance your healthcare practice? Contact us now for a free consultation!