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pith:2026:HGIVJKTWIJ3CK4OF2EE7WMOIBG
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MedRedFlag: Investigating how LLMs Redirect Misconceptions in Real-World Health Communication

Ayman Ali, Lionel Wong, Monica Agrawal, Sraavya Sambara, Vishala Mishra, Yuan Pu

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

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

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

arxiv: 2601.09853 · arxiv_version: 2601.09853v3 · doi: 10.48550/arxiv.2601.09853 · pith_short_12: HGIVJKTWIJ3C · pith_short_16: HGIVJKTWIJ3CK4OF · pith_short_8: HGIVJKTW
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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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    "submitted_at": "2026-01-14T20:23:02Z",
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