Pith Number

pith:TVCLVDSL

pith:2026:TVCLVDSLUAFINYIQ3AQETNC3YR

not attested not anchored not stored refs resolved

DermAgent: A Self-Reflective Agentic System for Dermatological Image Analysis with Multi-Tool Reasoning and Traceable Decision-Making

Feilong Tang, Lie Ju, Ming Hu, Siyuan Yan, Wei Feng, Xieji Li, Yize Liu, Zongyuan Ge

DermAgent anchors each skin image prediction in retrieved cases and guidelines then self-corrects via critic gates to raise diagnostic accuracy above standard models.

arxiv:2605.14403 v1 · 2026-05-14 · cs.CV

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{TVCLVDSLUAFINYIQ3AQETNC3YR}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

DermAgent consistently outperforms state-of-the-art MLLMs and medical agent baselines across zero-shot fine-grained disease diagnosis, concept annotation, and clinical captioning tasks, exceeding GPT-4o by 17.6% in skin disease diagnostic accuracy and 3.15% in captioning ROUGE-L.

C2weakest assumption

The dual-modality retrieval from 413,210 diagnosed cases and 3,199 guideline chunks provides unbiased, comprehensive anchoring for every prediction, and the critic module's confidence-coverage-conflict gates reliably detect and correct hallucinations without introducing new errors or over-correction.

C3one line summary

DermAgent orchestrates seven vision-language tools in a Plan-Execute-Reflect loop with dual-modality retrieval from 413k cases and a critic module to outperform GPT-4o by 17.6% in zero-shot dermatological diagnosis accuracy.

References

33 extracted · 33 resolved · 4 Pith anchors

[1] https://dermnetnz.org/

[2] https://www.mayoclinic.org/diseases-conditions

[3] Qwen3-VL Technical Report 2025 · doi:10.48550/arxiv.2511.21631

[4] Huatuogpt-vision, towards injecting medical visual knowledge into multimodal llms at scale 2024 · doi:10.48550/arxiv.2406.19280

[5] https://doi.org/10.48550/arXiv.2302.00785 2023 · doi:10.48550/arxiv.2302.00785

Receipt and verification

First computed	2026-05-17T23:39:07.459967Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9d44ba8e4ba00a86e110d82049b45bc46a6e97315478183141f90004ff2af2e4

Aliases

arxiv: 2605.14403 · arxiv_version: 2605.14403v1 · doi: 10.48550/arxiv.2605.14403 · pith_short_12: TVCLVDSLUAFI · pith_short_16: TVCLVDSLUAFINYIQ · pith_short_8: TVCLVDSL

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/TVCLVDSLUAFINYIQ3AQETNC3YR \
  | 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: 9d44ba8e4ba00a86e110d82049b45bc46a6e97315478183141f90004ff2af2e4

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "ecbce4e7e338026f359f1072060c5209f7c4de3e364bbe662ecb7dbfffaf49cf",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-14T05:41:11Z",
    "title_canon_sha256": "d42a931dedcc7a55c3a6f1c75d7c51e5f1b2fbcc27c72312240bae7fded11678"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14403",
    "kind": "arxiv",
    "version": 1
  }
}