{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YMLFDUUA7W6VQW43XZOSM4RRQX","short_pith_number":"pith:YMLFDUUA","schema_version":"1.0","canonical_sha256":"c31651d280fdbd585b9bbe5d26723185d3bce1250658ac90c2f136d9a3dcd66c","source":{"kind":"arxiv","id":"2405.00728","version":1},"attestation_state":"computed","paper":{"title":"Evaluating the Application of ChatGPT in Outpatient Triage Guidance: A Comparative Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.CL","authors_text":"Dan Pu, Di Liu, Dou Liu, Guangwu Qian, Kang Li, Rong Yin, Xiandi Wang, Xiaomei Tan, Ying Han","submitted_at":"2024-04-27T04:12:02Z","abstract_excerpt":"The integration of Artificial Intelligence (AI) in healthcare presents a transformative potential for enhancing operational efficiency and health outcomes. Large Language Models (LLMs), such as ChatGPT, have shown their capabilities in supporting medical decision-making. Embedding LLMs in medical systems is becoming a promising trend in healthcare development. The potential of ChatGPT to address the triage problem in emergency departments has been examined, while few studies have explored its application in outpatient departments. With a focus on streamlining workflows and enhancing efficiency"},"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":"2405.00728","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-04-27T04:12:02Z","cross_cats_sorted":["cs.AI","cs.HC"],"title_canon_sha256":"f78c7721f18a33d9ec5c875e16b8037d5b53c9e660efbd6e29fd7f655305535e","abstract_canon_sha256":"e875247ddbb1d529a13c2be190aa3e6bf8a4eea8a139552889fae172490de05a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:14:25.445406Z","signature_b64":"Quuz6wHZdO3GMda0H8qN3meyD8HcRMk+ZKvswjx6rctEY3cLl98Alo66amDAHx7pIVOvprFHpWafuhEzsUtJDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c31651d280fdbd585b9bbe5d26723185d3bce1250658ac90c2f136d9a3dcd66c","last_reissued_at":"2026-07-05T08:14:25.445002Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:14:25.445002Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating the Application of ChatGPT in Outpatient Triage Guidance: A Comparative Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.CL","authors_text":"Dan Pu, Di Liu, Dou Liu, Guangwu Qian, Kang Li, Rong Yin, Xiandi Wang, Xiaomei Tan, Ying Han","submitted_at":"2024-04-27T04:12:02Z","abstract_excerpt":"The integration of Artificial Intelligence (AI) in healthcare presents a transformative potential for enhancing operational efficiency and health outcomes. Large Language Models (LLMs), such as ChatGPT, have shown their capabilities in supporting medical decision-making. Embedding LLMs in medical systems is becoming a promising trend in healthcare development. The potential of ChatGPT to address the triage problem in emergency departments has been examined, while few studies have explored its application in outpatient departments. With a focus on streamlining workflows and enhancing efficiency"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.00728","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/2405.00728/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":"2405.00728","created_at":"2026-07-05T08:14:25.445058+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.00728v1","created_at":"2026-07-05T08:14:25.445058+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.00728","created_at":"2026-07-05T08:14:25.445058+00:00"},{"alias_kind":"pith_short_12","alias_value":"YMLFDUUA7W6V","created_at":"2026-07-05T08:14:25.445058+00:00"},{"alias_kind":"pith_short_16","alias_value":"YMLFDUUA7W6VQW43","created_at":"2026-07-05T08:14:25.445058+00:00"},{"alias_kind":"pith_short_8","alias_value":"YMLFDUUA","created_at":"2026-07-05T08:14:25.445058+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.13902","citing_title":"PIORS: Personalized Intelligent Outpatient Reception based on Large Language Model with Multi-Agents Medical Scenario Simulation","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YMLFDUUA7W6VQW43XZOSM4RRQX","json":"https://pith.science/pith/YMLFDUUA7W6VQW43XZOSM4RRQX.json","graph_json":"https://pith.science/api/pith-number/YMLFDUUA7W6VQW43XZOSM4RRQX/graph.json","events_json":"https://pith.science/api/pith-number/YMLFDUUA7W6VQW43XZOSM4RRQX/events.json","paper":"https://pith.science/paper/YMLFDUUA"},"agent_actions":{"view_html":"https://pith.science/pith/YMLFDUUA7W6VQW43XZOSM4RRQX","download_json":"https://pith.science/pith/YMLFDUUA7W6VQW43XZOSM4RRQX.json","view_paper":"https://pith.science/paper/YMLFDUUA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.00728&json=true","fetch_graph":"https://pith.science/api/pith-number/YMLFDUUA7W6VQW43XZOSM4RRQX/graph.json","fetch_events":"https://pith.science/api/pith-number/YMLFDUUA7W6VQW43XZOSM4RRQX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YMLFDUUA7W6VQW43XZOSM4RRQX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YMLFDUUA7W6VQW43XZOSM4RRQX/action/storage_attestation","attest_author":"https://pith.science/pith/YMLFDUUA7W6VQW43XZOSM4RRQX/action/author_attestation","sign_citation":"https://pith.science/pith/YMLFDUUA7W6VQW43XZOSM4RRQX/action/citation_signature","submit_replication":"https://pith.science/pith/YMLFDUUA7W6VQW43XZOSM4RRQX/action/replication_record"}},"created_at":"2026-07-05T08:14:25.445058+00:00","updated_at":"2026-07-05T08:14:25.445058+00:00"}