Patterns vs. Patients: Evaluating LLMs against Mental Health Professionals on Personality Disorder Diagnosis through First-Person Narratives
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Computer Science > Computation and Language
Title:Patterns vs. Patients: Evaluating LLMs against Mental Health Professionals on Personality Disorder Diagnosis through First-Person Narratives
Abstract:Growing reliance on LLMs for psychiatric self-assessment raises questions about their ability to interpret qualitative patient narratives. This depth over breadth case study directly compares state-of-the-art LLMs and mental health professionals in assessing Borderline (BPD) and Narcissistic (NPD) Personality Disorders based on Polish-language first-person autobiographical accounts. Within our sample, the overall diagnostic scores of the top-performing Gemini Pro models (65.48%) were 21.91 percentage points higher than the average scores of the human professionals (43.57%). While both models and human experts excelled at identifying BPD (F1 = 83.4 & F1 = 80.0, respectively), models severely underdiagnosed NPD (F1 = 6.7 vs. 50.0), showing a potential reluctance toward the value-laden term "narcissism." Qualitatively, models provided confident, elaborate justifications focused on patterns and formal categories, while human experts remained concise and cautious, emphasizing the patients' sense of self and temporal experience. Our findings demonstrate that while LLMs might be competent at interpreting complex first-person clinical data, their outputs still carry critical reliability and bias issues.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2512.20298 [cs.CL] |
| (or arXiv:2512.20298v4 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2512.20298
arXiv-issued DOI via DataCite
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Submission history
From: Kacper Dudzic [view email][v1] Tue, 23 Dec 2025 12:05:01 UTC (1,323 KB)
[v2] Mon, 27 Apr 2026 12:06:28 UTC (1,154 KB)
[v3] Tue, 5 May 2026 11:48:23 UTC (1,155 KB)
[v4] Fri, 22 May 2026 09:51:52 UTC (1,853 KB)
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