{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:Y6JXV7JSRZWQMDLCJGBTWS2XRD","short_pith_number":"pith:Y6JXV7JS","schema_version":"1.0","canonical_sha256":"c7937afd328e6d060d6249833b4b5788c735a02d3ed8d3174ded0b3ec02b5369","source":{"kind":"arxiv","id":"2504.13650","version":1},"attestation_state":"computed","paper":{"title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Beng Chin Ooi, Jiang Liu, Juncheng Li, Jun Xiao, Lingshuai Lin, Sijing Li, Tianwei Lin, Wenqiao Zhang, Xiaoda Yang, Xiaohui Song, Yucheng He, Yueting Zhuang","submitted_at":"2025-04-18T12:09:15Z","abstract_excerpt":"Medical Large Vision-Language Models (Med-LVLMs) demonstrate significant potential in healthcare, but their reliance on general medical data and coarse-grained global visual understanding limits them in intelligent ophthalmic diagnosis. Currently, intelligent ophthalmic diagnosis faces three major challenges: (i) Data. The lack of deeply annotated, high-quality, multi-modal ophthalmic visual instruction data; (ii) Benchmark. The absence of a comprehensive and systematic benchmark for evaluating diagnostic performance; (iii) Model. The difficulty of adapting holistic visual architectures to fin"},"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":"2504.13650","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-04-18T12:09:15Z","cross_cats_sorted":[],"title_canon_sha256":"56fd83e9796be1d0f63349b8fb6f1e2b4a238fb062645f40c4be9516b8fc4520","abstract_canon_sha256":"0787bc1f1252bdb3d21b1a2794ec38f6ef7f85e5b5b569c027cfab2c052fb220"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:50:59.800263Z","signature_b64":"TGhNPtBPFwb9Mr2sGGS9T/xEA2lzCoIyrvHVZMVHseuu9NsHSLQBdpRD/hTogRaHi1fX8cI+C86Ch+L8x1QIAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c7937afd328e6d060d6249833b4b5788c735a02d3ed8d3174ded0b3ec02b5369","last_reissued_at":"2026-07-05T10:50:59.799738Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:50:59.799738Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Beng Chin Ooi, Jiang Liu, Juncheng Li, Jun Xiao, Lingshuai Lin, Sijing Li, Tianwei Lin, Wenqiao Zhang, Xiaoda Yang, Xiaohui Song, Yucheng He, Yueting Zhuang","submitted_at":"2025-04-18T12:09:15Z","abstract_excerpt":"Medical Large Vision-Language Models (Med-LVLMs) demonstrate significant potential in healthcare, but their reliance on general medical data and coarse-grained global visual understanding limits them in intelligent ophthalmic diagnosis. Currently, intelligent ophthalmic diagnosis faces three major challenges: (i) Data. The lack of deeply annotated, high-quality, multi-modal ophthalmic visual instruction data; (ii) Benchmark. The absence of a comprehensive and systematic benchmark for evaluating diagnostic performance; (iii) Model. The difficulty of adapting holistic visual architectures to fin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.13650","kind":"arxiv","version":1},"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/2504.13650/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":"2504.13650","created_at":"2026-07-05T10:50:59.799803+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.13650v1","created_at":"2026-07-05T10:50:59.799803+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.13650","created_at":"2026-07-05T10:50:59.799803+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y6JXV7JSRZWQ","created_at":"2026-07-05T10:50:59.799803+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y6JXV7JSRZWQMDLC","created_at":"2026-07-05T10:50:59.799803+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y6JXV7JS","created_at":"2026-07-05T10:50:59.799803+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2506.05831","citing_title":"HeartcareGPT: A Unified Multimodal ECG Suite for Dual Signal-Image Modeling and Understanding","ref_index":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Y6JXV7JSRZWQMDLCJGBTWS2XRD","json":"https://pith.science/pith/Y6JXV7JSRZWQMDLCJGBTWS2XRD.json","graph_json":"https://pith.science/api/pith-number/Y6JXV7JSRZWQMDLCJGBTWS2XRD/graph.json","events_json":"https://pith.science/api/pith-number/Y6JXV7JSRZWQMDLCJGBTWS2XRD/events.json","paper":"https://pith.science/paper/Y6JXV7JS"},"agent_actions":{"view_html":"https://pith.science/pith/Y6JXV7JSRZWQMDLCJGBTWS2XRD","download_json":"https://pith.science/pith/Y6JXV7JSRZWQMDLCJGBTWS2XRD.json","view_paper":"https://pith.science/paper/Y6JXV7JS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.13650&json=true","fetch_graph":"https://pith.science/api/pith-number/Y6JXV7JSRZWQMDLCJGBTWS2XRD/graph.json","fetch_events":"https://pith.science/api/pith-number/Y6JXV7JSRZWQMDLCJGBTWS2XRD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y6JXV7JSRZWQMDLCJGBTWS2XRD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y6JXV7JSRZWQMDLCJGBTWS2XRD/action/storage_attestation","attest_author":"https://pith.science/pith/Y6JXV7JSRZWQMDLCJGBTWS2XRD/action/author_attestation","sign_citation":"https://pith.science/pith/Y6JXV7JSRZWQMDLCJGBTWS2XRD/action/citation_signature","submit_replication":"https://pith.science/pith/Y6JXV7JSRZWQMDLCJGBTWS2XRD/action/replication_record"}},"created_at":"2026-07-05T10:50:59.799803+00:00","updated_at":"2026-07-05T10:50:59.799803+00:00"}