A Proactive Multi-Agent Dialogue Framework for Assessing Social Language Disorder Traits in Autism
arXiv:2605.22993v1 Announce Type: cross Abstract: Characteristic linguistic behaviors associated with Social Language Disorder (SLD) in autism spectrum disorder, including echoic repetition, pronoun displacement, and stereotyped media quoting, are largely absent from spontaneous conversation and only emerge under specific conversational conditions. In structured clinical assessments, this latency means that questioning strategy selection is a critical yet underappreciated determinant of how much diagnostic information a conversation yields. Whether large language models (LLMs) can be guided to
The rapid advancement and application of large language models are enabling novel approaches to complex diagnostic challenges in healthcare, such as identifying nuanced social language disorders that evade traditional methods.
This breakthrough demonstrates LLMs' expanding utility in detailed, nuanced clinical assessment, potentially improving early diagnosis and intervention for conditions like autism spectrum disorder, which impacts millions globally.
LLMs can now be guided to proactively generate specific conversational conditions that reveal diagnostic traits, moving beyond passive analysis to an active diagnostic role in identifying subtle linguistic markers.
- · AI developers specializing in healthcare applications
- · Healthcare providers and diagnostic centers
- · Individuals with social language disorders and their families
- · Researchers in autism spectrum disorder
- · Traditional, less precise diagnostic methods
- · Specialized clinical assessment tools that cannot adapt
LLMs will become an essential component of diagnostic toolkits for neurological and developmental disorders, enhancing efficiency and accuracy.
This improved diagnostic capability could lead to earlier interventions, potentially improving long-term outcomes and reducing societal healthcare burdens.
The success in this domain could accelerate the adoption of AI-driven diagnostic frameworks across a wider range of medical conditions, transforming diagnostic medicine.
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Read at arXiv cs.AI