Evaluating Artificial Intelligence in Mental Health Care: Benefits, Drawbacks, and Future Scope

Abstract

Mental disorders affect about one in eight globally, with over 75% of those in low- and middle-income countries receiving no treatment (WHO; The Daily Star). This gap arises from a lack of clinicians, geographic barriers, stigma, and costs. AI provides a scalable solution: digital platforms like Woebot and Wysa offer 24/7 therapeutic support at low cost (Sigosoft; Fitzpatrick et al.). AI can screen, triage, coach, and respond to users instantly, addressing unmet mental health needs. Yet, promise equals peril. There are important safety, efficacy, and ethical hazards to AI therapy. Meta-analyses report modest symptom decrease (Hedges' g ≈ 0.64 depressive symptoms) but with broad confidence intervals and scarce long-term data [Li et al.]. Data privacy, algorithmic bias, hallucinated or dangerous content, and unclear regulation contribute to doubts about uninhibited deployment (Rahsepar et al.; Svensson). Detractors note that AI cannot truly experience empathy or bear moral responsibilities or accountability and that this contributes to potential misuse or over-reliance (Wells, Stanford HAI).

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