{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:WJZHWUEHLD2R22WVK7JBGZXTR2","short_pith_number":"pith:WJZHWUEH","schema_version":"1.0","canonical_sha256":"b2727b508758f51d6ad557d21366f38eb5feb6bb6058458ac0cbae50a6436654","source":{"kind":"arxiv","id":"2505.08508","version":1},"attestation_state":"computed","paper":{"title":"TrialMatchAI: An End-to-End AI-powered Clinical Trial Recommendation System to Streamline Patient-to-Trial Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-bio.QM"],"primary_cat":"cs.AI","authors_text":"Alexis Groppi, Eivind Hovig, Gerrit Meijer, Johanna Galvis, Lana Meiqari, Macha Nikolski, Majd Abdallah, Maria Alexandra Rujano, Mariska Bierkens, Remond Fijneman, Rodrigo Dienstmann, Sigve Nakken, Slim Karkar, Steve Canham","submitted_at":"2025-05-13T12:39:06Z","abstract_excerpt":"Patient recruitment remains a major bottleneck in clinical trials, calling for scalable and automated solutions. We present TrialMatchAI, an AI-powered recommendation system that automates patient-to-trial matching by processing heterogeneous clinical data, including structured records and unstructured physician notes. Built on fine-tuned, open-source large language models (LLMs) within a retrieval-augmented generation framework, TrialMatchAI ensures transparency and reproducibility and maintains a lightweight deployment footprint suitable for clinical environments. The system normalizes biome"},"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":"2505.08508","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-05-13T12:39:06Z","cross_cats_sorted":["cs.LG","q-bio.QM"],"title_canon_sha256":"711262d5e78df022ba5db189faaa599d2e575cb58be2dc97ae6ee9735327a8d9","abstract_canon_sha256":"e110e155ace5265e391d1b065c8361bed9aa5cf8b92ec7834bc933e78b44918b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:02:31.638370Z","signature_b64":"OX5APbq7ebWYwnQdddrIqM54/AFcOsqp2fuiBybgLu8y1iHt1nsLeRl4mnmll5l4Nz2zEIUPigkgKm7U1Hu/CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b2727b508758f51d6ad557d21366f38eb5feb6bb6058458ac0cbae50a6436654","last_reissued_at":"2026-07-05T11:02:31.637812Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:02:31.637812Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TrialMatchAI: An End-to-End AI-powered Clinical Trial Recommendation System to Streamline Patient-to-Trial Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-bio.QM"],"primary_cat":"cs.AI","authors_text":"Alexis Groppi, Eivind Hovig, Gerrit Meijer, Johanna Galvis, Lana Meiqari, Macha Nikolski, Majd Abdallah, Maria Alexandra Rujano, Mariska Bierkens, Remond Fijneman, Rodrigo Dienstmann, Sigve Nakken, Slim Karkar, Steve Canham","submitted_at":"2025-05-13T12:39:06Z","abstract_excerpt":"Patient recruitment remains a major bottleneck in clinical trials, calling for scalable and automated solutions. We present TrialMatchAI, an AI-powered recommendation system that automates patient-to-trial matching by processing heterogeneous clinical data, including structured records and unstructured physician notes. Built on fine-tuned, open-source large language models (LLMs) within a retrieval-augmented generation framework, TrialMatchAI ensures transparency and reproducibility and maintains a lightweight deployment footprint suitable for clinical environments. The system normalizes biome"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.08508","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/2505.08508/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":"2505.08508","created_at":"2026-07-05T11:02:31.637873+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.08508v1","created_at":"2026-07-05T11:02:31.637873+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.08508","created_at":"2026-07-05T11:02:31.637873+00:00"},{"alias_kind":"pith_short_12","alias_value":"WJZHWUEHLD2R","created_at":"2026-07-05T11:02:31.637873+00:00"},{"alias_kind":"pith_short_16","alias_value":"WJZHWUEHLD2R22WV","created_at":"2026-07-05T11:02:31.637873+00:00"},{"alias_kind":"pith_short_8","alias_value":"WJZHWUEH","created_at":"2026-07-05T11:02:31.637873+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/WJZHWUEHLD2R22WVK7JBGZXTR2","json":"https://pith.science/pith/WJZHWUEHLD2R22WVK7JBGZXTR2.json","graph_json":"https://pith.science/api/pith-number/WJZHWUEHLD2R22WVK7JBGZXTR2/graph.json","events_json":"https://pith.science/api/pith-number/WJZHWUEHLD2R22WVK7JBGZXTR2/events.json","paper":"https://pith.science/paper/WJZHWUEH"},"agent_actions":{"view_html":"https://pith.science/pith/WJZHWUEHLD2R22WVK7JBGZXTR2","download_json":"https://pith.science/pith/WJZHWUEHLD2R22WVK7JBGZXTR2.json","view_paper":"https://pith.science/paper/WJZHWUEH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.08508&json=true","fetch_graph":"https://pith.science/api/pith-number/WJZHWUEHLD2R22WVK7JBGZXTR2/graph.json","fetch_events":"https://pith.science/api/pith-number/WJZHWUEHLD2R22WVK7JBGZXTR2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WJZHWUEHLD2R22WVK7JBGZXTR2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WJZHWUEHLD2R22WVK7JBGZXTR2/action/storage_attestation","attest_author":"https://pith.science/pith/WJZHWUEHLD2R22WVK7JBGZXTR2/action/author_attestation","sign_citation":"https://pith.science/pith/WJZHWUEHLD2R22WVK7JBGZXTR2/action/citation_signature","submit_replication":"https://pith.science/pith/WJZHWUEHLD2R22WVK7JBGZXTR2/action/replication_record"}},"created_at":"2026-07-05T11:02:31.637873+00:00","updated_at":"2026-07-05T11:02:31.637873+00:00"}