Artificial intelligence guidance in ethically challenging clinical scenarios in child and adolescent psychiatry: a qualitative study in the context of Turkiye

OpenAlex İngilizce

BACKGROUND: Ethical decision-making in child and adolescent psychiatry (CAP) is inherently complex, shaped by developmental vulnerability, evolving autonomy, and competing responsibilities to patients, families, and the legal system. Clinicians often face moral dilemmas when navigating adolescent confidentiality, parental authority, and mandatory reporting duties, especially in high-stakes or culturally sensitive contexts. As large language models (LLMs) enter clinical settings, their potential to support ethical reasoning remains underexplored, particularly outside Western paradigms. This study qualitatively investigates how different LLMs provide ethical, legal, and emotional guidance to clinicians facing ethically challenging scenarios in CAP, situated within Turkiye's sociocultural and legal landscape. METHOD: A scenario-based qualitative design was employed. Three expert-developed case vignettes reflecting ethically charged dilemmas, such as adolescent autonomy, parental conflict, and confidentiality, were submitted to the three LLMs (ChatGPT 4.0, Gemini 2.5 Flash, and GROK 3). Responses were analyzed using content and thematic analysis to identify key patterns of ethical-legal reasoning, alongside discourse analysis to examine tone, empathy, and cultural sensitivity. Two researchers, with backgrounds in CAP and medical ethics, conducted independent coding and reached consensus through a reflexive, interdisciplinary approach. RESULTS: All LLMs addressed core ethical principles (autonomy, non-maleficence, beneficence, and justice) and referenced Turkish legal frameworks such as the Child Protection Law, Patient Rights Regulation, and mandatory reporting obligations, situating their guidance within the national regulatory context. They also differed in their engagement with sociocultural sensitivities: GROK 3 emphasized therapeutic communication and relational trust, Gemini 2.5 Flash applied a highly structured, rule-based style focused on procedural compliance, while ChatGPT 4.0 provided concise and practical suggestions. Despite thematic overlaps, these varying approaches shaped how effectively the models aligned with Turkiye's clinical realities. Notably, LLMs frequently acted as "thinking companions," offering ethical and legal justifications while leaving interpretive responsibility with clinicians. CONCLUSION: LLMs in CAP hold promise not only as cognitive aids but also as emotionally attuned, context-sensitive companions in ethical decision-making processes. Their effectiveness depends not just on algorithmic precision but also on explainability, empathy, and cultural alignment. Rather than replacing clinician judgment, LLMs may serve to ease emotional burden, enhance therapeutic reflection, and foster ethically sound care in complex, high-pressure situations.