Proposal: Language-Based Red Flag Detection for Slurred Speech and Neurological Distress in AI Interactions
Submitted by: Concerned ChatGPT User
Context: Personal experience with a family member’s stroke
Purpose: Safety enhancement proposal for conversational AI tools
Background
Human users increasingly rely on ChatGPT and similar assistants for everyday tasks, including during moments of cognitive strain. However, there is a critical safety gap:
When a user is experiencing slurred or disordered speech due to a medical event (e.g. stroke or TIA), the AI may prioritize task completion over health assessment, potentially masking signs of emergency.
In a real-world example, a family member of mine survived a stroke because a nurse on the phone recognized the signs in their confused, slurred, and disjointed speech. This life-saving intervention occurred through human intuition — something current AI tools do not possess.
Problem
ChatGPT is designed to interpret prompts and complete tasks based on the text output of speech-to-text systems, like Whisper. However:
These systems may normalize slurred speech, removing telltale signs.
The AI then produces clean, helpful responses that may hide the user’s distress from third parties (e.g., a recipe copied and sent to a family member, who otherwise might have recognised mistyping and otger errors that could flag something wrong).
This creates a masking effect, where AI-generated output conceals a potential medical crisis.
Proposed Solution: Language-Level Flagging of Red Flags
Introduce a lightweight triage awareness system within ChatGPT’s language model layer, trained to detect possible signs of disordered speech in the text itself, including:
Unusual repetition of sibilant consonants (e.g. “sssorry,” “faaaaceee”)
This should help even where a user is utilising the speech interface.
You might also chose to recognise disjointed or phonetic spelling
Garbled syntax paired with medical complaints (e.g., “numb… fasssh… tired”)
Incoherent task requests with urgency or confusion
If such patterns are detected:
Trigger a soft flag (e.g., “Just checking — are you feeling okay? I noticed some unusual language in your message.”)
Offer a non-intrusive prompt to contact a friend or seek help
Allow the user to opt into a quick-check mode or continue their task uninterrupted
Ethical Framing
The goal is not to diagnose, but to surface possible concern in a respectful, privacy-conscious way.
Escalations should be consent-based and interruptible, so users maintain autonomy.
Pattern sensitivity could also be customisable over time, based on each user’s normal speech patterns, with full transparency.
Rationale
Emergency call handlers are trained to recognise distress not just from what is said, but how.
With the rise of AI as a cognitive assistant, ChatGPT may be the only “listener” a person is interacting with during an emergency.
Without safeguards, well-meaning outputs could delay human recognition of a critical condition.
Implementation Suggestions
Partner with experts in speech pathology, emergency response, and neurology to define signal patterns.
Use anonymized, labeled transcripts from emergency service calls for training and tuning.
Develop threshold-based alert tiers, similar to spam filters or content moderation.
Closing
This is not a call for diagnosis, but for better detection of distress patterns hidden in prompts, especially when slurred or disjointed language is paired with symptoms like confusion, numbness, or extreme fatigue.
In a world where people increasingly turn to AI first, even one life saved by catching a red flag is worth the effort.
Thank you for considering this proposal.