The article examines a growing problem with artificial intelligence systems when they are used in mental health contexts: hallucinations, or confidently presented but incorrect or fabricated responses. When AI models attempt to give psychological guidance, they sometimes invent details, misinterpret emotional cues, or offer advice that may be unsafe or inappropriate. This is especially concerning because users often trust the tone and authority of AI replies, even when they are inaccurate.
A central issue is that current AI models are trained on vast swaths of text, not on lived human experience or clinical expertise. While they can mimic supportive language, they lack genuine understanding or empathy. When asked to provide mental health advice, they can generate plausible-sounding but harmful suggestions—such as trivializing serious symptoms, misdiagnosing conditions, or encouraging self-guided “treatments” that are not evidence-based.
The article highlights real-world examples where well-meaning users seeking help have been given guidance that could worsen their condition. Because AI lacks the ability to recognize crisis situations or nuanced emotional states reliably, responses can miss red flags or falsely reassure users. Experts warn that such misinformation can delay people from seeking appropriate professional support and might even encourage harmful actions.
Ultimately, the piece emphasizes that while AI has potential to support mental health care—such as by offering resources, psychoeducation, or triage guidance—it must be used with caution and robust safeguards. Developers, clinicians, and policymakers are urged to collaborate on standards and oversight to prevent unsafe AI advice, ensuring that technology augments, rather than undermines, trusted clinical care.
