Peer-reviewed article
Artificial intelligence guidance in ethically challenging clinical scenarios in child and adolescent psychiatry: a qualitative study in the context of Turkiye.
İçen S, Köken AH ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This study examines how three large language models (ChatGPT, Gemini, and GROK) respond to ethically challenging scenarios in child and adolescent psychiatry within Turkey's legal and cultural context. Researchers submitted case vignettes involving issues like adolescent confidentiality and parental conflict to the three models and analyzed their responses for ethical reasoning, legal accuracy, and cultural sensitivity. The models demonstrated different approaches: one emphasized therapeutic communication, another focused on procedural compliance, and a third offered practical suggestions. All models referenced Turkish legal frameworks and acted as 'thinking companions' to support clinician decision-making rather than replacing clinical judgment.
Key points
- All three LLMs addressed core ethical principles (autonomy, non-maleficence, beneficence, justice) and referenced Turkish legal frameworks including the Child Protection Law and mandatory reporting obligations
- The models differed in their engagement with sociocultural sensitivities: GROK 3 emphasized therapeutic communication, Gemini 2.5 Flash used a rule-based procedural style, and ChatGPT 4.0 provided concise practical suggestions
- LLMs frequently acted as 'thinking companions,' offering ethical and legal justifications while leaving interpretive responsibility with clinicians
- Effectiveness depends on algorithmic precision, explainability, empathy, and cultural alignment rather than accuracy alone
- LLMs may help ease emotional burden and enhance ethical reflection in complex clinical situations
Limitations
This is a qualitative analysis of how LLMs respond to hypothetical scenarios rather than an evaluation of actual clinical outcomes or impact on child well-being. The study does not assess whether using these models actually improves clinical decision-making, reduces harm to children, or changes treatment practices in real-world settings.
Abstract
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.