Gender Associations in LLM-Mediated ADHD Self-Diagnosis

A research study evaluating GPT-4o demonstrates that demographic cues influence large language model outputs during zero-shot ADHD self-diagnostic queries, with explicit gender cues amplifying specific subtype associations and potentially triggering over-correcting bias mitigation mechanisms.

This research matters because uncalibrated artificial intelligence responses in healthcare self-assessment tools can distort diagnostic outcomes along demographic lines. As public reliance on LLMs for medical triage grows, algorithmic over-correction presents operational and clinical risks that can misdirect patient pathways. Ensuring reliable and clinically aligned demographic handling is essential for maintaining safety and trust in AI-enabled health technologies.

Large language models (LLMs) are increasingly utilised by individuals for medical self-diagnosis, yet the influence of demographic cueing on diagnostic outputs remains insufficiently understood.

Forward consideration, not a verified fact.