{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:6ZFAZ3TVSDDYHPLIRZTRCGXBMI","short_pith_number":"pith:6ZFAZ3TV","schema_version":"1.0","canonical_sha256":"f64a0cee7590c783bd688e67111ae16228d57d473726e74517445bbf55304916","source":{"kind":"arxiv","id":"1706.06838","version":3},"attestation_state":"computed","paper":{"title":"A giant with feet of clay: on the validity of the data that feed machine learning in medicine","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Davide Ciucci, Federico Cabitza, Raffaele Rasoini","submitted_at":"2017-06-21T11:45:44Z","abstract_excerpt":"This paper considers the use of Machine Learning (ML) in medicine by focusing on the main problem that this computational approach has been aimed at solving or at least minimizing: uncertainty. To this aim, we point out how uncertainty is so ingrained in medicine that it biases also the representation of clinical phenomena, that is the very input of ML models, thus undermining the clinical significance of their output. Recognizing this can motivate both medical doctors, in taking more responsibility in the development and use of these decision aids, and the researchers, in pursuing different w"},"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":"1706.06838","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2017-06-21T11:45:44Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"05d87b56b679986d4eabbe467821db0642982852eca3b9fef1473401ac616234","abstract_canon_sha256":"f1719dbb374c96ce5dc0bd204d5ca896a916ad6248a2a9f80260f4a953f2e7b1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:08.996025Z","signature_b64":"fC0Qs0tj8b2XVQZsEDFY3pttitx5OT6P0xpXDheeEZMc/47ik5J3OBBkbUD9n4dhDZDwdz/Au1hhXrbqvOmIDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f64a0cee7590c783bd688e67111ae16228d57d473726e74517445bbf55304916","last_reissued_at":"2026-05-18T00:16:08.995267Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:08.995267Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A giant with feet of clay: on the validity of the data that feed machine learning in medicine","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Davide Ciucci, Federico Cabitza, Raffaele Rasoini","submitted_at":"2017-06-21T11:45:44Z","abstract_excerpt":"This paper considers the use of Machine Learning (ML) in medicine by focusing on the main problem that this computational approach has been aimed at solving or at least minimizing: uncertainty. To this aim, we point out how uncertainty is so ingrained in medicine that it biases also the representation of clinical phenomena, that is the very input of ML models, thus undermining the clinical significance of their output. Recognizing this can motivate both medical doctors, in taking more responsibility in the development and use of these decision aids, and the researchers, in pursuing different w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.06838","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":""},"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":"1706.06838","created_at":"2026-05-18T00:16:08.995407+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.06838v3","created_at":"2026-05-18T00:16:08.995407+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.06838","created_at":"2026-05-18T00:16:08.995407+00:00"},{"alias_kind":"pith_short_12","alias_value":"6ZFAZ3TVSDDY","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_16","alias_value":"6ZFAZ3TVSDDYHPLI","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_8","alias_value":"6ZFAZ3TV","created_at":"2026-05-18T12:31:03.183658+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/6ZFAZ3TVSDDYHPLIRZTRCGXBMI","json":"https://pith.science/pith/6ZFAZ3TVSDDYHPLIRZTRCGXBMI.json","graph_json":"https://pith.science/api/pith-number/6ZFAZ3TVSDDYHPLIRZTRCGXBMI/graph.json","events_json":"https://pith.science/api/pith-number/6ZFAZ3TVSDDYHPLIRZTRCGXBMI/events.json","paper":"https://pith.science/paper/6ZFAZ3TV"},"agent_actions":{"view_html":"https://pith.science/pith/6ZFAZ3TVSDDYHPLIRZTRCGXBMI","download_json":"https://pith.science/pith/6ZFAZ3TVSDDYHPLIRZTRCGXBMI.json","view_paper":"https://pith.science/paper/6ZFAZ3TV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.06838&json=true","fetch_graph":"https://pith.science/api/pith-number/6ZFAZ3TVSDDYHPLIRZTRCGXBMI/graph.json","fetch_events":"https://pith.science/api/pith-number/6ZFAZ3TVSDDYHPLIRZTRCGXBMI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6ZFAZ3TVSDDYHPLIRZTRCGXBMI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6ZFAZ3TVSDDYHPLIRZTRCGXBMI/action/storage_attestation","attest_author":"https://pith.science/pith/6ZFAZ3TVSDDYHPLIRZTRCGXBMI/action/author_attestation","sign_citation":"https://pith.science/pith/6ZFAZ3TVSDDYHPLIRZTRCGXBMI/action/citation_signature","submit_replication":"https://pith.science/pith/6ZFAZ3TVSDDYHPLIRZTRCGXBMI/action/replication_record"}},"created_at":"2026-05-18T00:16:08.995407+00:00","updated_at":"2026-05-18T00:16:08.995407+00:00"}