{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FCE2IST7MIQGQATF4WRCPS2TNU","short_pith_number":"pith:FCE2IST7","schema_version":"1.0","canonical_sha256":"2889a44a7f6220680265e5a227cb536d11989a9dbf57b3779887ae9bcdfdc475","source":{"kind":"arxiv","id":"2412.02621","version":1},"attestation_state":"computed","paper":{"title":"Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Fuchun Sun, Haoran Sun, Jiahong Dong, Jun Yan, Kai Sun, Lei Liu, Lei Yang, Ling Wang, Na Guo, Shuo Jin, Siyan Xue, Siyuan Wang, Tian Zhao, Xinzhou Wang, Yu Luo","submitted_at":"2024-12-03T17:50:19Z","abstract_excerpt":"Recent advancements in deep learning have significantly revolutionized the field of clinical diagnosis and treatment, offering novel approaches to improve diagnostic precision and treatment efficacy across diverse clinical domains, thus driving the pursuit of precision medicine. The growing availability of multi-organ and multimodal datasets has accelerated the development of large-scale Medical Multimodal Foundation Models (MMFMs). These models, known for their strong generalization capabilities and rich representational power, are increasingly being adapted to address a wide range of clinica"},"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":"2412.02621","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-12-03T17:50:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d7476dc8426f98543b1de01c29dffc0054215980e16199e412c554cad70b60e0","abstract_canon_sha256":"498065318ea008754b0f24657f0830575b14cea8923b07411cffcf3e5b952e6c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:43:52.599737Z","signature_b64":"u1bCFNs9YcHiD2U3Tncvxsasre56arUk+s4QZ4A027rZ0sZliDcSXLeCbSmRz/yI498obURr20X8jHKeMfC+CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2889a44a7f6220680265e5a227cb536d11989a9dbf57b3779887ae9bcdfdc475","last_reissued_at":"2026-07-05T09:43:52.599237Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:43:52.599237Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Fuchun Sun, Haoran Sun, Jiahong Dong, Jun Yan, Kai Sun, Lei Liu, Lei Yang, Ling Wang, Na Guo, Shuo Jin, Siyan Xue, Siyuan Wang, Tian Zhao, Xinzhou Wang, Yu Luo","submitted_at":"2024-12-03T17:50:19Z","abstract_excerpt":"Recent advancements in deep learning have significantly revolutionized the field of clinical diagnosis and treatment, offering novel approaches to improve diagnostic precision and treatment efficacy across diverse clinical domains, thus driving the pursuit of precision medicine. The growing availability of multi-organ and multimodal datasets has accelerated the development of large-scale Medical Multimodal Foundation Models (MMFMs). These models, known for their strong generalization capabilities and rich representational power, are increasingly being adapted to address a wide range of clinica"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.02621","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/2412.02621/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":"2412.02621","created_at":"2026-07-05T09:43:52.599288+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.02621v1","created_at":"2026-07-05T09:43:52.599288+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.02621","created_at":"2026-07-05T09:43:52.599288+00:00"},{"alias_kind":"pith_short_12","alias_value":"FCE2IST7MIQG","created_at":"2026-07-05T09:43:52.599288+00:00"},{"alias_kind":"pith_short_16","alias_value":"FCE2IST7MIQGQATF","created_at":"2026-07-05T09:43:52.599288+00:00"},{"alias_kind":"pith_short_8","alias_value":"FCE2IST7","created_at":"2026-07-05T09:43:52.599288+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26894","citing_title":"Modeling Local, Global, and Cross-Modal Context in Multimodal 3D MRI","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FCE2IST7MIQGQATF4WRCPS2TNU","json":"https://pith.science/pith/FCE2IST7MIQGQATF4WRCPS2TNU.json","graph_json":"https://pith.science/api/pith-number/FCE2IST7MIQGQATF4WRCPS2TNU/graph.json","events_json":"https://pith.science/api/pith-number/FCE2IST7MIQGQATF4WRCPS2TNU/events.json","paper":"https://pith.science/paper/FCE2IST7"},"agent_actions":{"view_html":"https://pith.science/pith/FCE2IST7MIQGQATF4WRCPS2TNU","download_json":"https://pith.science/pith/FCE2IST7MIQGQATF4WRCPS2TNU.json","view_paper":"https://pith.science/paper/FCE2IST7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.02621&json=true","fetch_graph":"https://pith.science/api/pith-number/FCE2IST7MIQGQATF4WRCPS2TNU/graph.json","fetch_events":"https://pith.science/api/pith-number/FCE2IST7MIQGQATF4WRCPS2TNU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FCE2IST7MIQGQATF4WRCPS2TNU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FCE2IST7MIQGQATF4WRCPS2TNU/action/storage_attestation","attest_author":"https://pith.science/pith/FCE2IST7MIQGQATF4WRCPS2TNU/action/author_attestation","sign_citation":"https://pith.science/pith/FCE2IST7MIQGQATF4WRCPS2TNU/action/citation_signature","submit_replication":"https://pith.science/pith/FCE2IST7MIQGQATF4WRCPS2TNU/action/replication_record"}},"created_at":"2026-07-05T09:43:52.599288+00:00","updated_at":"2026-07-05T09:43:52.599288+00:00"}