Privacy in Mental Healthcare by Data Protection

Abstract

AI methods for psychological assessment relied only on neuro-computational analysis and data-driven modeling, which quantified feelings and actions. The capacity of machine learning to detect cognitive distortions, diagnoses, and treatment strategies is used to assess theories. However, the ethical guidelines that regulate professional treatment sessions are often not followed by these technological techniques. However, AI systems are unable to comprehend the scenario, maintain secrets, or experience other people's emotions. The hazards go beyond data privacy to the core elements of self-perception and personal identity development when AI applications impede on issues like identity crises, emotional instability, and the handling of traumatic situations. The possibility of re-traumatization, the theft of personal narratives, or the use of mental states as commodities for behavior profiling and financial gain is increased when there is a lack of robust data governance in this domain.

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