{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3T75MTQM3LBR4H5FFEY6X224BP","short_pith_number":"pith:3T75MTQM","schema_version":"1.0","canonical_sha256":"dcffd64e0cdac31e1fa52931ebeb5c0bd084b0f1eb71b87c273aabf30f3267ea","source":{"kind":"arxiv","id":"2408.15266","version":1},"attestation_state":"computed","paper":{"title":"People over trust AI-generated medical responses and view them to be as valid as doctors, despite low accuracy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CY"],"primary_cat":"cs.HC","authors_text":"Chethan Sarabu, Guillermo A. Cecchi, Pat Pataranutaporn, Pattie Maes, Shruthi Shekar","submitted_at":"2024-08-11T23:41:28Z","abstract_excerpt":"This paper presents a comprehensive analysis of how AI-generated medical responses are perceived and evaluated by non-experts. A total of 300 participants gave evaluations for medical responses that were either written by a medical doctor on an online healthcare platform, or generated by a large language model and labeled by physicians as having high or low accuracy. Results showed that participants could not effectively distinguish between AI-generated and Doctors' responses and demonstrated a preference for AI-generated responses, rating High Accuracy AI-generated responses as significantly "},"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":"2408.15266","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2024-08-11T23:41:28Z","cross_cats_sorted":["cs.AI","cs.CY"],"title_canon_sha256":"0b23aa3a2834be0ece2b2f929cccebbd5d38203486764d7c62ecfa9dfdb99785","abstract_canon_sha256":"6ab2718f9e1beecfc2d4d37bc2b280b9210b3f8c159b27a9abb286b9c83de507"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:00:09.324341Z","signature_b64":"WAqXWxJvObdIILRLQubZUxQ+U+xh8CPqMaoGHVOR3fE+DIEI1+6uyfvbHplCeRTfGGi+qSLuxYpjHN54+aiuDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dcffd64e0cdac31e1fa52931ebeb5c0bd084b0f1eb71b87c273aabf30f3267ea","last_reissued_at":"2026-07-05T09:00:09.323853Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:00:09.323853Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"People over trust AI-generated medical responses and view them to be as valid as doctors, despite low accuracy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CY"],"primary_cat":"cs.HC","authors_text":"Chethan Sarabu, Guillermo A. Cecchi, Pat Pataranutaporn, Pattie Maes, Shruthi Shekar","submitted_at":"2024-08-11T23:41:28Z","abstract_excerpt":"This paper presents a comprehensive analysis of how AI-generated medical responses are perceived and evaluated by non-experts. A total of 300 participants gave evaluations for medical responses that were either written by a medical doctor on an online healthcare platform, or generated by a large language model and labeled by physicians as having high or low accuracy. Results showed that participants could not effectively distinguish between AI-generated and Doctors' responses and demonstrated a preference for AI-generated responses, rating High Accuracy AI-generated responses as significantly "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.15266","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/2408.15266/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":"2408.15266","created_at":"2026-07-05T09:00:09.323911+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.15266v1","created_at":"2026-07-05T09:00:09.323911+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.15266","created_at":"2026-07-05T09:00:09.323911+00:00"},{"alias_kind":"pith_short_12","alias_value":"3T75MTQM3LBR","created_at":"2026-07-05T09:00:09.323911+00:00"},{"alias_kind":"pith_short_16","alias_value":"3T75MTQM3LBR4H5F","created_at":"2026-07-05T09:00:09.323911+00:00"},{"alias_kind":"pith_short_8","alias_value":"3T75MTQM","created_at":"2026-07-05T09:00:09.323911+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.08415","citing_title":"Political Plasticity: An Analysis of Ideological Adaptability in Large Language Models","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2604.25415","citing_title":"One-shot emergency psychiatric triage across 15 frontier AI chatbots","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3T75MTQM3LBR4H5FFEY6X224BP","json":"https://pith.science/pith/3T75MTQM3LBR4H5FFEY6X224BP.json","graph_json":"https://pith.science/api/pith-number/3T75MTQM3LBR4H5FFEY6X224BP/graph.json","events_json":"https://pith.science/api/pith-number/3T75MTQM3LBR4H5FFEY6X224BP/events.json","paper":"https://pith.science/paper/3T75MTQM"},"agent_actions":{"view_html":"https://pith.science/pith/3T75MTQM3LBR4H5FFEY6X224BP","download_json":"https://pith.science/pith/3T75MTQM3LBR4H5FFEY6X224BP.json","view_paper":"https://pith.science/paper/3T75MTQM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.15266&json=true","fetch_graph":"https://pith.science/api/pith-number/3T75MTQM3LBR4H5FFEY6X224BP/graph.json","fetch_events":"https://pith.science/api/pith-number/3T75MTQM3LBR4H5FFEY6X224BP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3T75MTQM3LBR4H5FFEY6X224BP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3T75MTQM3LBR4H5FFEY6X224BP/action/storage_attestation","attest_author":"https://pith.science/pith/3T75MTQM3LBR4H5FFEY6X224BP/action/author_attestation","sign_citation":"https://pith.science/pith/3T75MTQM3LBR4H5FFEY6X224BP/action/citation_signature","submit_replication":"https://pith.science/pith/3T75MTQM3LBR4H5FFEY6X224BP/action/replication_record"}},"created_at":"2026-07-05T09:00:09.323911+00:00","updated_at":"2026-07-05T09:00:09.323911+00:00"}