{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MVO57M5YUS4JLJCAAQQR55XW62","short_pith_number":"pith:MVO57M5Y","schema_version":"1.0","canonical_sha256":"655ddfb3b8a4b895a44004211ef6f6f688630504cd50b2856229c9dae6e44c4b","source":{"kind":"arxiv","id":"2409.06351","version":1},"attestation_state":"computed","paper":{"title":"MAGDA: Multi-agent guideline-driven diagnostic assistance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"David Bani-Harouni, Matthias Keicher, Nassir Navab","submitted_at":"2024-09-10T09:10:30Z","abstract_excerpt":"In emergency departments, rural hospitals, or clinics in less developed regions, clinicians often lack fast image analysis by trained radiologists, which can have a detrimental effect on patients' healthcare. Large Language Models (LLMs) have the potential to alleviate some pressure from these clinicians by providing insights that can help them in their decision-making. While these LLMs achieve high test results on medical exams showcasing their great theoretical medical knowledge, they tend not to follow medical guidelines. In this work, we introduce a new approach for zero-shot guideline-dri"},"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":"2409.06351","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-09-10T09:10:30Z","cross_cats_sorted":[],"title_canon_sha256":"eb6cbbe33cf7fe43a170f611c22cf158b5e485edd320d3c37fcdd285d70dfb10","abstract_canon_sha256":"c1b42480a527670683f68b8ac3fee1cadb4310e14a02cf5659cfe1d4acf45c3d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:05:23.061268Z","signature_b64":"arxO/0HQmmSxJXoo7PtRES9SXJj3dw+5LaKdDsh5uynh2qgp0gr78i+nbQVe64+EN0V3+MVQwhiDOoPoJUxsAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"655ddfb3b8a4b895a44004211ef6f6f688630504cd50b2856229c9dae6e44c4b","last_reissued_at":"2026-07-05T09:05:23.060860Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:05:23.060860Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MAGDA: Multi-agent guideline-driven diagnostic assistance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"David Bani-Harouni, Matthias Keicher, Nassir Navab","submitted_at":"2024-09-10T09:10:30Z","abstract_excerpt":"In emergency departments, rural hospitals, or clinics in less developed regions, clinicians often lack fast image analysis by trained radiologists, which can have a detrimental effect on patients' healthcare. Large Language Models (LLMs) have the potential to alleviate some pressure from these clinicians by providing insights that can help them in their decision-making. While these LLMs achieve high test results on medical exams showcasing their great theoretical medical knowledge, they tend not to follow medical guidelines. In this work, we introduce a new approach for zero-shot guideline-dri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.06351","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/2409.06351/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":"2409.06351","created_at":"2026-07-05T09:05:23.060917+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.06351v1","created_at":"2026-07-05T09:05:23.060917+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.06351","created_at":"2026-07-05T09:05:23.060917+00:00"},{"alias_kind":"pith_short_12","alias_value":"MVO57M5YUS4J","created_at":"2026-07-05T09:05:23.060917+00:00"},{"alias_kind":"pith_short_16","alias_value":"MVO57M5YUS4JLJCA","created_at":"2026-07-05T09:05:23.060917+00:00"},{"alias_kind":"pith_short_8","alias_value":"MVO57M5Y","created_at":"2026-07-05T09:05:23.060917+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MVO57M5YUS4JLJCAAQQR55XW62","json":"https://pith.science/pith/MVO57M5YUS4JLJCAAQQR55XW62.json","graph_json":"https://pith.science/api/pith-number/MVO57M5YUS4JLJCAAQQR55XW62/graph.json","events_json":"https://pith.science/api/pith-number/MVO57M5YUS4JLJCAAQQR55XW62/events.json","paper":"https://pith.science/paper/MVO57M5Y"},"agent_actions":{"view_html":"https://pith.science/pith/MVO57M5YUS4JLJCAAQQR55XW62","download_json":"https://pith.science/pith/MVO57M5YUS4JLJCAAQQR55XW62.json","view_paper":"https://pith.science/paper/MVO57M5Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.06351&json=true","fetch_graph":"https://pith.science/api/pith-number/MVO57M5YUS4JLJCAAQQR55XW62/graph.json","fetch_events":"https://pith.science/api/pith-number/MVO57M5YUS4JLJCAAQQR55XW62/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MVO57M5YUS4JLJCAAQQR55XW62/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MVO57M5YUS4JLJCAAQQR55XW62/action/storage_attestation","attest_author":"https://pith.science/pith/MVO57M5YUS4JLJCAAQQR55XW62/action/author_attestation","sign_citation":"https://pith.science/pith/MVO57M5YUS4JLJCAAQQR55XW62/action/citation_signature","submit_replication":"https://pith.science/pith/MVO57M5YUS4JLJCAAQQR55XW62/action/replication_record"}},"created_at":"2026-07-05T09:05:23.060917+00:00","updated_at":"2026-07-05T09:05:23.060917+00:00"}