{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5YR7DEGCZAXTOOA555F7RI6IR6","short_pith_number":"pith:5YR7DEGC","schema_version":"1.0","canonical_sha256":"ee23f190c2c82f37381def4bf8a3c88fb051d7b75322de75f1b1460508f6aafc","source":{"kind":"arxiv","id":"2410.19008","version":1},"attestation_state":"computed","paper":{"title":"Teach Multimodal LLMs to Comprehend Electrocardiographic Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Ping Zhang, Ruoqi Liu, Xiang Yue, Yuelin Bai","submitted_at":"2024-10-21T20:26:41Z","abstract_excerpt":"The electrocardiogram (ECG) is an essential non-invasive diagnostic tool for assessing cardiac conditions. Existing automatic interpretation methods suffer from limited generalizability, focusing on a narrow range of cardiac conditions, and typically depend on raw physiological signals, which may not be readily available in resource-limited settings where only printed or digital ECG images are accessible. Recent advancements in multimodal large language models (MLLMs) present promising opportunities for addressing these challenges. However, the application of MLLMs to ECG image interpretation "},"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":"2410.19008","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-10-21T20:26:41Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"d3caf8cd445405e4116405193fd62dbc3c0a4a7fa3750c98641c0e33a56f9587","abstract_canon_sha256":"de454a48a0685f93904b37b984377b23825191bc9c96a5fbabed638661170004"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:25:50.050820Z","signature_b64":"w6xdtpP3+4pdO5wbx74z0KJArTx55N8yMKcKS6KYscamLSR1OzerJsPYQay1tKaNyjM/gOcNLM8oxVSJ30JvAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ee23f190c2c82f37381def4bf8a3c88fb051d7b75322de75f1b1460508f6aafc","last_reissued_at":"2026-07-05T09:25:50.050335Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:25:50.050335Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Teach Multimodal LLMs to Comprehend Electrocardiographic Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Ping Zhang, Ruoqi Liu, Xiang Yue, Yuelin Bai","submitted_at":"2024-10-21T20:26:41Z","abstract_excerpt":"The electrocardiogram (ECG) is an essential non-invasive diagnostic tool for assessing cardiac conditions. Existing automatic interpretation methods suffer from limited generalizability, focusing on a narrow range of cardiac conditions, and typically depend on raw physiological signals, which may not be readily available in resource-limited settings where only printed or digital ECG images are accessible. Recent advancements in multimodal large language models (MLLMs) present promising opportunities for addressing these challenges. However, the application of MLLMs to ECG image interpretation "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.19008","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/2410.19008/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":"2410.19008","created_at":"2026-07-05T09:25:50.050393+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.19008v1","created_at":"2026-07-05T09:25:50.050393+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.19008","created_at":"2026-07-05T09:25:50.050393+00:00"},{"alias_kind":"pith_short_12","alias_value":"5YR7DEGCZAXT","created_at":"2026-07-05T09:25:50.050393+00:00"},{"alias_kind":"pith_short_16","alias_value":"5YR7DEGCZAXTOOA5","created_at":"2026-07-05T09:25:50.050393+00:00"},{"alias_kind":"pith_short_8","alias_value":"5YR7DEGC","created_at":"2026-07-05T09:25:50.050393+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.16441","citing_title":"DeepArrhythmia: Segment-Contextualized ECG Arrhythmia Classification via Selective Evidence Acquisition","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17308","citing_title":"Reasoning Before Diagnosis: Physician-Inspired Structured Thinking for ECG Classification","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2603.03331","citing_title":"PulseLM: A Foundation Dataset and Benchmark for PPG-Text Learning","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17295","citing_title":"LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5YR7DEGCZAXTOOA555F7RI6IR6","json":"https://pith.science/pith/5YR7DEGCZAXTOOA555F7RI6IR6.json","graph_json":"https://pith.science/api/pith-number/5YR7DEGCZAXTOOA555F7RI6IR6/graph.json","events_json":"https://pith.science/api/pith-number/5YR7DEGCZAXTOOA555F7RI6IR6/events.json","paper":"https://pith.science/paper/5YR7DEGC"},"agent_actions":{"view_html":"https://pith.science/pith/5YR7DEGCZAXTOOA555F7RI6IR6","download_json":"https://pith.science/pith/5YR7DEGCZAXTOOA555F7RI6IR6.json","view_paper":"https://pith.science/paper/5YR7DEGC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.19008&json=true","fetch_graph":"https://pith.science/api/pith-number/5YR7DEGCZAXTOOA555F7RI6IR6/graph.json","fetch_events":"https://pith.science/api/pith-number/5YR7DEGCZAXTOOA555F7RI6IR6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5YR7DEGCZAXTOOA555F7RI6IR6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5YR7DEGCZAXTOOA555F7RI6IR6/action/storage_attestation","attest_author":"https://pith.science/pith/5YR7DEGCZAXTOOA555F7RI6IR6/action/author_attestation","sign_citation":"https://pith.science/pith/5YR7DEGCZAXTOOA555F7RI6IR6/action/citation_signature","submit_replication":"https://pith.science/pith/5YR7DEGCZAXTOOA555F7RI6IR6/action/replication_record"}},"created_at":"2026-07-05T09:25:50.050393+00:00","updated_at":"2026-07-05T09:25:50.050393+00:00"}