When Rating Scales Fall Short: LLM-Assisted Discovery of ADHD Signals in Turkish Teacher Narratives
arXiv:2606.02509v1 Announce Type: new Abstract: Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders in childhood, and its diagnosis relies on assessments combining clinician judgment with standardized rating scales and reports from parents and teachers. While structured instruments such as the Conners' Teacher Rating Scale-Revised Short Form (CTRS-R:S) quantify ADHD-related behaviors, teachers also provide open-ended narratives that may contain complementary signals not captured by structured assessments. However, it remains unclear to what ex
The increasing sophistication of Large Language Models (LLMs) allows for novel applications in qualitative data analysis, particularly in fields with rich narrative data like medical diagnostics.
This development indicates a growing utility of advanced AI in healthcare, moving beyond structured data analysis to extract nuanced insights from human-generated narratives, potentially improving diagnostic accuracy and personalized care.
Traditional diagnostic methods, heavily reliant on structured scales, are augmented by AI's ability to uncover subtle, complementary signals from qualitative data, thus broadening diagnostic capabilities and potentially leading to earlier or more precise interventions.
- · AI/ML researchers in healthcare
- · Healthcare providers
- · Patients with complex neurodevelopmental disorders
- · Developers of specialized LLMs
- · Companies relying solely on traditional diagnostic scale development
- · Manual qualitative data analysts
AI tools will become increasingly integrated into diagnostic processes for neurodevelopmental and mental health conditions.
This integration could accelerate the identification of new diagnostic markers and lead to more personalized treatment plans.
The success in this domain may spur broader adoption of LLM-assisted qualitative analysis across other medical specialties and social sciences, potentially transforming data interpretation paradigms.
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