{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:6T4DV47U3XGNBQKACHJSQAHQ7W","short_pith_number":"pith:6T4DV47U","schema_version":"1.0","canonical_sha256":"f4f83af3f4ddccd0c14011d32800f0fd8b67473e6ab293b763937e3161b2b31f","source":{"kind":"arxiv","id":"2304.10691","version":2},"attestation_state":"computed","paper":{"title":"SkinGPT-4: An Interactive Dermatology Diagnostic System with Visual Large Language Model","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Bin Zhang, Jiannan Xu, Juexiao Zhou, Liyuan Sun, Longxi Zhou, Xiaonan He, Xin Gao, Xingyu Liao, Xiuying Chen, Yuetan Chu","submitted_at":"2023-04-21T01:17:09Z","abstract_excerpt":"Skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases, impacting a considerable portion of the population. Nonetheless, the field of dermatology diagnosis faces three significant hurdles. Firstly, there is a shortage of dermatologists accessible to diagnose patients, particularly in rural regions. Secondly, accurately interpreting skin disease images poses a considerable challenge. Lastly, generating patient-friendly diagnostic reports is usually a time-consuming and labor-intensive task for dermatologists. To tackle these challenges,"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2304.10691","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2023-04-21T01:17:09Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"e00832f6a639ad6ae69c11a5b637fae9c51b36fc50e67c92e5a47f5d0f1eb2fb","abstract_canon_sha256":"6bf5b1d751d0791758ad46243f92ba81bd2fc69623139203fc9dea48ad54aa71"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:18:38.629122Z","signature_b64":"RB7qaAWxfNnMSb2ieutDTtYqaWbYjO9oqAUXeIqe/1H+8ZTk4KvKmgmz0EfSY9VXaPoNHMUQkTgAT2a8fEZYAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4f83af3f4ddccd0c14011d32800f0fd8b67473e6ab293b763937e3161b2b31f","last_reissued_at":"2026-07-05T06:18:38.628656Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:18:38.628656Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SkinGPT-4: An Interactive Dermatology Diagnostic System with Visual Large Language Model","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Bin Zhang, Jiannan Xu, Juexiao Zhou, Liyuan Sun, Longxi Zhou, Xiaonan He, Xin Gao, Xingyu Liao, Xiuying Chen, Yuetan Chu","submitted_at":"2023-04-21T01:17:09Z","abstract_excerpt":"Skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases, impacting a considerable portion of the population. Nonetheless, the field of dermatology diagnosis faces three significant hurdles. Firstly, there is a shortage of dermatologists accessible to diagnose patients, particularly in rural regions. Secondly, accurately interpreting skin disease images poses a considerable challenge. Lastly, generating patient-friendly diagnostic reports is usually a time-consuming and labor-intensive task for dermatologists. To tackle these challenges,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.10691","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2304.10691/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2304.10691","created_at":"2026-07-05T06:18:38.628713+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.10691v2","created_at":"2026-07-05T06:18:38.628713+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.10691","created_at":"2026-07-05T06:18:38.628713+00:00"},{"alias_kind":"pith_short_12","alias_value":"6T4DV47U3XGN","created_at":"2026-07-05T06:18:38.628713+00:00"},{"alias_kind":"pith_short_16","alias_value":"6T4DV47U3XGNBQKA","created_at":"2026-07-05T06:18:38.628713+00:00"},{"alias_kind":"pith_short_8","alias_value":"6T4DV47U","created_at":"2026-07-05T06:18:38.628713+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2311.12882","citing_title":"LLMs-Healthcare : Current Applications and Challenges of Large Language Models in various Medical Specialties","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2506.05831","citing_title":"HeartcareGPT: A Unified Multimodal ECG Suite for Dual Signal-Image Modeling and Understanding","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08493","citing_title":"CapCLIP: A Vision-Language Representation Alignment Approach for Wireless Capsule Endoscopy Analysis","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6T4DV47U3XGNBQKACHJSQAHQ7W","json":"https://pith.science/pith/6T4DV47U3XGNBQKACHJSQAHQ7W.json","graph_json":"https://pith.science/api/pith-number/6T4DV47U3XGNBQKACHJSQAHQ7W/graph.json","events_json":"https://pith.science/api/pith-number/6T4DV47U3XGNBQKACHJSQAHQ7W/events.json","paper":"https://pith.science/paper/6T4DV47U"},"agent_actions":{"view_html":"https://pith.science/pith/6T4DV47U3XGNBQKACHJSQAHQ7W","download_json":"https://pith.science/pith/6T4DV47U3XGNBQKACHJSQAHQ7W.json","view_paper":"https://pith.science/paper/6T4DV47U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.10691&json=true","fetch_graph":"https://pith.science/api/pith-number/6T4DV47U3XGNBQKACHJSQAHQ7W/graph.json","fetch_events":"https://pith.science/api/pith-number/6T4DV47U3XGNBQKACHJSQAHQ7W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6T4DV47U3XGNBQKACHJSQAHQ7W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6T4DV47U3XGNBQKACHJSQAHQ7W/action/storage_attestation","attest_author":"https://pith.science/pith/6T4DV47U3XGNBQKACHJSQAHQ7W/action/author_attestation","sign_citation":"https://pith.science/pith/6T4DV47U3XGNBQKACHJSQAHQ7W/action/citation_signature","submit_replication":"https://pith.science/pith/6T4DV47U3XGNBQKACHJSQAHQ7W/action/replication_record"}},"created_at":"2026-07-05T06:18:38.628713+00:00","updated_at":"2026-07-05T06:18:38.628713+00:00"}