pith:HGIVJKTW
MedRedFlag: Investigating how LLMs Redirect Misconceptions in Real-World Health Communication
LLMs often accept false premises in real health questions instead of redirecting to correct the misconception.
arxiv:2601.09853 v3 · 2026-01-14 · cs.CL · cs.AI
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Claims
Our analysis reveals that LLMs often fail to redirect problematic questions, even when the problematic premise is detected, and provide answers that could lead to suboptimal medical decision making.
The semi-automated pipeline accurately identifies real-world health questions that require redirection due to embedded false premises, and clinician responses provide the appropriate benchmark.
LLMs often fail to redirect health questions containing misconceptions, unlike clinicians, exposing safety gaps in patient-facing medical AI.
Receipt and verification
| First computed | 2026-06-04T01:08:39.847334Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
399154aa7642762571c5d109fb31c809bf91bb7889328c45630a561817deadc6
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/HGIVJKTWIJ3CK4OF2EE7WMOIBG \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 399154aa7642762571c5d109fb31c809bf91bb7889328c45630a561817deadc6
Canonical record JSON
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