{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CEJYNLXFYCTZBZC5U6T3AHFP2N","short_pith_number":"pith:CEJYNLXF","schema_version":"1.0","canonical_sha256":"111386aee5c0a790e45da7a7b01cafd34497a63a868211f8ebcdf888824398d5","source":{"kind":"arxiv","id":"2409.00097","version":3},"attestation_state":"computed","paper":{"title":"Large Language Models for Disease Diagnosis: A Scoping Review","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chunpu Xu, Daochen Zha, Dongming Cai, Genevieve B. Melton, Jeremy Yeung, Jiashuo Wang, Kaishuai Xu, Liqiao Xia, Mian Zhang, Mingquan Lin, Rui Zhang, Shuang Zhou, Sirui Ding, Yawen Guo, Yi Fang, Zaifu Zhan, Zidu Xu","submitted_at":"2024-08-27T02:06:45Z","abstract_excerpt":"Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the increasing attention in this field, a holistic view is still lacking. Many critical aspects remain unclear, such as the diseases and clinical data to which LLMs have been applied, the LLM techniques employed, and the evaluation methods used. In this article, we perform a comprehensive review of LLM-based methods for disease di"},"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":"2409.00097","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-08-27T02:06:45Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6edfe30565b372428642b09d03bcdfc9d3b0f0cde43381b07f80f393de873900","abstract_canon_sha256":"5e832c3c5a2b5d534a2c5e9f32c0b9dc17917a187764a1736243ddacd54cff7e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:03.758501Z","signature_b64":"Ni7NWMDR3n2s2+D8EXUOgybGDHRSbX136v1sP5mIftsZx4gSWB7VEUvkKDlJN4cDjCbCg8xejsScxJiW7LzFAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"111386aee5c0a790e45da7a7b01cafd34497a63a868211f8ebcdf888824398d5","last_reissued_at":"2026-07-05T11:25:03.757890Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:03.757890Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large Language Models for Disease Diagnosis: A Scoping Review","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chunpu Xu, Daochen Zha, Dongming Cai, Genevieve B. Melton, Jeremy Yeung, Jiashuo Wang, Kaishuai Xu, Liqiao Xia, Mian Zhang, Mingquan Lin, Rui Zhang, Shuang Zhou, Sirui Ding, Yawen Guo, Yi Fang, Zaifu Zhan, Zidu Xu","submitted_at":"2024-08-27T02:06:45Z","abstract_excerpt":"Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the increasing attention in this field, a holistic view is still lacking. Many critical aspects remain unclear, such as the diseases and clinical data to which LLMs have been applied, the LLM techniques employed, and the evaluation methods used. In this article, we perform a comprehensive review of LLM-based methods for disease di"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.00097","kind":"arxiv","version":3},"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/2409.00097/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":"2409.00097","created_at":"2026-07-05T11:25:03.757973+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.00097v3","created_at":"2026-07-05T11:25:03.757973+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.00097","created_at":"2026-07-05T11:25:03.757973+00:00"},{"alias_kind":"pith_short_12","alias_value":"CEJYNLXFYCTZ","created_at":"2026-07-05T11:25:03.757973+00:00"},{"alias_kind":"pith_short_16","alias_value":"CEJYNLXFYCTZBZC5","created_at":"2026-07-05T11:25:03.757973+00:00"},{"alias_kind":"pith_short_8","alias_value":"CEJYNLXF","created_at":"2026-07-05T11:25:03.757973+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.11740","citing_title":"UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA","ref_index":183,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CEJYNLXFYCTZBZC5U6T3AHFP2N","json":"https://pith.science/pith/CEJYNLXFYCTZBZC5U6T3AHFP2N.json","graph_json":"https://pith.science/api/pith-number/CEJYNLXFYCTZBZC5U6T3AHFP2N/graph.json","events_json":"https://pith.science/api/pith-number/CEJYNLXFYCTZBZC5U6T3AHFP2N/events.json","paper":"https://pith.science/paper/CEJYNLXF"},"agent_actions":{"view_html":"https://pith.science/pith/CEJYNLXFYCTZBZC5U6T3AHFP2N","download_json":"https://pith.science/pith/CEJYNLXFYCTZBZC5U6T3AHFP2N.json","view_paper":"https://pith.science/paper/CEJYNLXF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.00097&json=true","fetch_graph":"https://pith.science/api/pith-number/CEJYNLXFYCTZBZC5U6T3AHFP2N/graph.json","fetch_events":"https://pith.science/api/pith-number/CEJYNLXFYCTZBZC5U6T3AHFP2N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CEJYNLXFYCTZBZC5U6T3AHFP2N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CEJYNLXFYCTZBZC5U6T3AHFP2N/action/storage_attestation","attest_author":"https://pith.science/pith/CEJYNLXFYCTZBZC5U6T3AHFP2N/action/author_attestation","sign_citation":"https://pith.science/pith/CEJYNLXFYCTZBZC5U6T3AHFP2N/action/citation_signature","submit_replication":"https://pith.science/pith/CEJYNLXFYCTZBZC5U6T3AHFP2N/action/replication_record"}},"created_at":"2026-07-05T11:25:03.757973+00:00","updated_at":"2026-07-05T11:25:03.757973+00:00"}