Skip to main content

Verified by Psychology Today

Artificial Intelligence

Is AI the Key to Improving Mental Healthcare Accessibility?

AI's balancing act: Improving mental healthcare without losing the human touch.

Key points

  • AI can improve mental health diagnosis, therapy, and treatment, increasing access and personalizing care.
  • AI automation can help address therapist shortages, extend care to more people, and reduce costs.
  • AI can assist people struggling with autism or PTSD, while mitigating biases and enhancing mental healthcare.
Shvets Production/Pexels
Source: Shvets Production/Pexels

I recently had the pleasure of connecting with Karolina Komarnicka, CEO of Space of Mind, a firm specializing in helping people lead better lives. As a mental health expert, Karolina revealed to me how artificial intelligence (AI) has the potential to augment human capabilities in ways that may not seem so obvious. One area where AI holds significant promise is offering opportunities to enhance mental health accessibility and treatment quality.

This article explores the multifaceted role of AI in mental health, addressing diagnosis, therapy, therapist shortages, treatment efficiency, bias mitigation, and redefined responsibilities of mental health professionals. By examining AI's performance, costs, and ethical considerations, we can assess the potential of integrating AI to improve mental healthcare access.

Diagnosing mental illness

Traditional mental health therapy relies on human interaction, where therapists lend a compassionate ear and empathize with patients. However, AI introduces a profound contrast. Tutun et al. (2023) suggested AI's potential in diagnosing mental illnesses. Mental health heavily depends on patients' ability to communicate their emotional states, making it challenging for clinicians to assess accurately.

AI uses diverse data sources, including medical records, social media, and online searches, aiding in identifying behavioral changes indicative of mental health issues. It can also analyze conversational language patterns and typing behaviors on smartphones to detect underlying conditions (Wahle et al., 2016). AI-driven chatbots, designed to emulate human practitioners, may recognize mental health issues sooner and recommend suitable interventions that humans might miss.

However, ensuring diagnostic accuracy and avoiding unwarranted assessments is crucial. False positives strain healthcare systems and increase costs, but it's essential to weigh these against false positives and negatives from human therapists. AI's precision is expected to improve as algorithms evolve, and societal awareness of mental health stigma may drive people to choose AI-based interventions over traditional therapy.

Addressing the therapist shortage

Pragmatically, there is a shortage of therapists in the United States, with over 60 percent unable to accommodate new patients (American Psychological Association, 2022). This shortage is more acute in middle- and low-income nations. AI can increase access to mental health support and enable therapists to spend more quality time with patients by automating operational processes. Advanced neural networks, including analysis of emotions and sentiment, empower mental health chatbots to play a significant role in patient care.

AI can also help individuals stay committed to their healing journeys and recognize patterns influencing their mental well-being by analyzing visual and audio data. These AI-based interventions bridge the gap caused by therapist shortages, extending care to a broader population while preserving the choice for human therapy.

Redefining the therapist-patient relationship

The transition to AI-driven mental healthcare raises concerns about the erosion of human elements like empathy and trust in therapist-patient relationships. However, feedback-driven AI systems can enhance empathic conversations (Ayers et al., 2023). AI therapists can offer scalable, convenient, and affordable teaching of psychological tools, potentially reducing costs and improving access to care.

The preference for non-human interaction

Conditions like depression and autism can hinder interpersonal interactions, making traditional therapy challenging. AI can assist in diagnosing and supporting individuals with these conditions, offering accessible and less intimidating mental healthcare. For example, AI-generated videos can help children with autism acquire essential skills in a controlled environment (Gill et al., 2024). Additionally, AI has shown promise in assisting individuals with post-traumatic stress, reducing therapy costs, and improving progress tracking through innovative solutions.

Enhancing treatment efficiency

Existing mental health treatments face limitations, but AI can personalize treatment regimens, suggesting non-pharmaceutical alternatives tailored to individual profiles. One critical issue is predicting self-harm before it happens. AI's ability to predict suicidal tendencies through data analysis can outperform human assessments, providing valuable data-driven insights (Fonseka et al., 2019).

Mitigating healthcare practitioner bias

Human biases can affect the quality of care, including under-diagnosing conditions like autism in women. AI can mitigate biases by employing impartial algorithms for diagnosis and treatment recommendations. Combining AI-generated diagnoses with those from human practitioners can result in a more accurate and unbiased assessment. The most promising approach may involve AI and healthcare practitioners working together, with AI providing data-driven support under the oversight of therapists.

What it all means

The decision to integrate AI into mental healthcare must be informed by performance assessments, cost considerations, and ethical implications. While AI can enhance mental healthcare access and quality, it is essential to recognize the enduring value of human expertise and the human touch in mental health diagnosis and mediation. The future may involve a collaborative approach, leveraging AI to augment human capabilities and improve mental healthcare for all. Thank you, Karolina Komarnicka, for these valuable insights!


American Psychological Association (2022). Psychologists struggle to meet demand amid mental health crisis.…

Ayers, J., Poliak, A., & Dredze, M., et al. (2023). Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Internal Medicine.…

Fonseka, T. M., Bhat, V., & Kennedy, S. H. (2019). The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors. Australian & New Zealand Journal of Psychiatry, 53 (10), 954-964.

Gill, S. S., Xu, M., Patros, P., Wu, H., Kaur, R., Kaur, K., ... & Buyya, R. (2024). Transformative effects of ChatGPT on modern education: Emerging era of AI chatbots. Internet of Things and Cyber-Physical Systems, 4, 19-23.

Tutun, S., et al. (2023) An AI-based decision support system for predicting mental health disorders. Information Systems Frontiers, 25, 1261–1276.

Wahle, F., Kowatsch, T., Fleisch, E., Rufer, M., & Weidt, S. (2016). Mobile sensing and support for people with depression: a pilot trial in the wild. JMIR mHealth and uHealth, 4(3), e5960.

More from Bobby Hoffman Ph.D.
More from Psychology Today
More from Bobby Hoffman Ph.D.
More from Psychology Today