{ "id": "AIBD-0063", "title": "Lunit INSIGHT CXR flags incidental nodules on chest radiographs taken for other reasons; 13 patients at Yongin Severance Hospital had resectable lung cancer", "summary": "AI on routine chest X-rays flagged nodules in 13 patients not suspected of lung cancer; all had resectable cancer, 7 stage I, and surgery.", "date_observed": "2020-03-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "At Yongin Severance Hospital, Yongin, South Korea, the deep-learning software Lunit INSIGHT CXR (versions 2 and 3, Lunit Inc.) was applied to chest radiographs. Kwak and colleagues reported in PLOS ONE on 10 March 2023 on 75 patients with resectable lung cancer between March 2020 and February 2022. Of these, 13 (17.3%) had cancers found incidentally because the software flagged a nodule on a radiograph taken for an unrelated reason. One was a 60-year-old man, a current smoker, imaged before cholecystectomy, whose left upper lobe adenocarcinoma was stage IA1. He saw a pulmonologist three days later, after the radiologist's report. The paper reports eight of the 13 saw a pulmonologist the same day, before the radiologist's official report. Its Table 1 is not fully consistent with this figure. All 13 had resection and seven were stage I needing no further treatment.", "category": "benefit created", "unexpected": false, "purpose": "Lunit INSIGHT CXR is built to detect and localize abnormalities, including nodules, on chest radiographs. Incidental detection of lung cancer is its intended function.", "system": "Lunit INSIGHT CXR", "model_version": "versions 2 and 3", "developer": "Lunit Inc.", "deployer": "Yongin Severance Hospital, Yonsei University College of Medicine", "location": "Yongin, South Korea", "domain": "Healthcare - thoracic radiology / lung cancer", "segment": "AI applied to routine hospital chest radiographs", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 3, "reason": "Thirteen patients had early, resectable lung cancer diagnosed and operated on. Seven were stage I and needed no further treatment. A survival benefit is plausible but not demonstrated in the paper." }, { "group": "organizational", "depth": 1, "reason": "Eight patients were seen by a pulmonologist on the day of the radiograph, before the formal report, shortening the diagnostic pathway." } ], "ai_role": { "status": "assessed", "statement": "The software flagged all 13 nodules and its results were visible to ordering clinicians on PACS immediately after the radiograph. The authors assume from medical-record timestamps that the AI results aided the same-day consultations in 8 of 13 patients. They acknowledge that whether clinicians acted on the AI result or on the radiologist's report cannot be settled from the study. Radiologists also read the films. In 12 of 13 cases the radiologist's report described an abnormality at the site. There the contribution is earlier attention to findings nobody was looking for. In one (patient 12) the report read “No active lung disease”, so the AI flagged a cancer that the formal report missed." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Kwak SH, Kim EK, Kim MH, Lee EH, Shin HJ (2023). Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs. PLOS ONE 18(3):e0281690.", "title": "Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10004566/", "date": "2023-03-10", "written_by": "deployer", "quote": "Eight patients (61.5%) consulted a pulmonologist promptly on the same day when the chest radiograph was taken and before they received the radiologist's official report." }, { "kind": "peer-reviewed study", "citation": "Kwak SH et al. (2023). PLOS ONE 18(3):e0281690 (second quotation).", "title": "Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10004566/", "date": "2023-03-10", "written_by": "deployer", "quote": "Seven patients (53.8%) were in stage I, which meant no additional treatment was required after surgery." } ], "replication": { "produced_by": [ "Routine AI analysis of chest radiographs with results visible to ordering clinicians in real time", "A prompt pulmonology referral pathway for AI-flagged nodules" ], "must_hold": [ "Clinicians review AI flags on films ordered for non-pulmonary reasons", "CT follow-up and tissue diagnosis for flagged nodules" ], "echoes": [] }, "paired_incident": { "database": "AI Incident Database", "identifier": "81", "title": "Researchers find evidence of racial, gender, and socioeconomic bias in chest X-ray classifiers", "url": "https://incidentdatabase.ai/cite/81", "severity": null, "group": "personal", "basis": "Same system class and task, AI classification of routine chest radiographs: the classifiers studied showed gender, racial and socioeconomic bias that underdiagnoses patients, the group Lunit's incidental nodule flags benefited." }, "headline": "Lunit INSIGHT CXR finds 13 resectable lung cancers incidentally", "metric": "13 (17.3%) had cancers found incidentally because the software flagged a nodule", "comparator": null, "later": { "status": "unknown", "date": null, "note": "No later report on these 13 patients or on the Yongin Severance deployment appears in the cited paper.", "quote": null, "url": null }, "value": null, "developer_country": "South Korea", "deployer_country": "South Korea", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 81 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/81", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Account and AI-role statement corrected against the paper's Table 1 and limitations: the example patient (patient 2) is a current smoker who saw a pulmonologist three days after the radiologist's report, the same-day consultations are the authors' timestamp-based assumption rather than a demonstrated AI effect, and one of the 13 radiologist reports (patient 12) read “No active lung disease”.", "source": "Kwak SH, Kim EK, Kim MH, Lee EH, Shin HJ (2023). PLOS ONE 18(3):e0281690, Table 1 and Discussion (limitations), https://pmc.ncbi.nlm.nih.gov/articles/PMC10004566/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.lunit.io/en/contact; https://ko.wikipedia.org/wiki/%EC%9A%A9%EC%9D%B8%EC%84%B8%EB%B8%8C%EB%9E%80%EC%8A%A4%EB%B3%91%EC%9B%90", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: the deployer's peer-reviewed study. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0063", "catalog_version": "1.0.0", "source_fingerprint": "3d322e6fbf2835ba613f9621741daa31fd7c7800a98629b503274e8e027bf96d", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0063-source-0001", "AIBD-0063-source-0002" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Lunit Inc.", "deployer": "Yongin Severance Hospital, Yonsei University College of Medicine" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The software flagged all 13 nodules and its results were visible to ordering clinicians on PACS immediately after the radiograph. The authors assume from medical-record timestamps that the AI results aided the same-day consultations in 8 of 13 patients. They acknowledge that whether clinicians acted on the AI result or on the radiologist's report cannot be settled from the study. Radiologists also read the films. In 12 of 13 cases the radiologist's report described an abnormality at the site. There the contribution is earlier attention to findings nobody was looking for. In one (patient 12) the report read “No active lung disease”, so the AI flagged a cancer that the formal report missed." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Lunit INSIGHT CXR flagged all 13 nodules and made the results available to clinicians immediately after the chest X-rays. In one patient, it flagged a nodule that proved cancerous where the radiologist had reported no active lung disease.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The software flagged all 13 nodules and its results were visible to ordering clinicians on PACS immediately after the radiograph. The authors assume from medical-record timestamps that the AI results aided the same-day consultations in 8 of 13 patients. They acknowledge that whether clinicians acted on the AI result or on the radiologist's report cannot be settled from the study. Radiologists also read the films. In 12 of 13 cases the radiologist's report described an abnormality at the site. There the contribution is earlier attention to findings nobody was looking for. In one (patient 12) the report read “No active lung disease”, so the AI flagged a cancer that the formal report missed." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "Thirteen patients had early, resectable lung cancer diagnosed and operated on. Seven were stage I and needed no further treatment. A survival benefit is plausible but not demonstrated in the paper." }, { "group": "organizational", "depth": 1, "reason": "Eight patients were seen by a pulmonologist on the day of the radiograph, before the formal report, shortening the diagnostic pathway." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "low", "evidence_word": "claimed", "claim_status": "The deployer's account", "basis": "Sources: the deployer's peer-reviewed study. The cited evidence includes no independent source and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "Thirteen patients had early, resectable lung cancer diagnosed and operated on. Seven were stage I and needed no further treatment. A survival benefit is plausible but not demonstrated in the paper." }, { "group": "organizational", "depth": 1, "reason": "Eight patients were seen by a pulmonologist on the day of the radiograph, before the formal report, shortening the diagnostic pathway." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "Thirteen patients had early, resectable lung cancer diagnosed and operated on. Seven were stage I and needed no further treatment. A survival benefit is plausible but not demonstrated in the paper." }, { "group": "organizational", "depth": 1, "reason": "Eight patients were seen by a pulmonologist on the day of the radiograph, before the formal report, shortening the diagnostic pathway." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "value_present", "value": { "kind": "structured", "data": { "answer": "Same system class and task, AI classification of routine chest radiographs: the classifiers studied showed gender, racial and socioeconomic bias that underdiagnoses patients, the group Lunit's incidental nodule flags benefited.", "links": [ { "database": "AI Incident Database", "identifier": "81", "title": "Researchers find evidence of racial, gender, and socioeconomic bias in chest X-ray classifiers", "url": "https://incidentdatabase.ai/cite/81", "group": "personal", "basis": "Same system class and task, AI classification of routine chest radiographs: the classifiers studied showed gender, racial and socioeconomic bias that underdiagnoses patients, the group Lunit's incidental nodule flags benefited.", "relation": "comparable_harm" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Existing paired_incident classification; no new source review.", "record_fields": [ "paired_incident" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "13 (17.3%) had cancers found incidentally because the software flagged a nodule", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "The authors inferred from timestamps that AI aided eight same-day consultations. They could not establish whether clinicians acted on the software’s result or the radiologist’s report. Radiologists described an abnormality at the same site in 12 of the 13 cases. Table 1 does not fully agree with the reported count of eight same-day consultations.", "editor_note": "" } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "paired_incident": { "database": "AI Incident Database", "identifier": "81", "title": "Researchers find evidence of racial, gender, and socioeconomic bias in chest X-ray classifiers", "url": "https://incidentdatabase.ai/cite/81", "group": "personal", "basis": "Same system class and task, AI classification of routine chest radiographs: the classifiers studied showed gender, racial and socioeconomic bias that underdiagnoses patients, the group Lunit's incidental nodule flags benefited." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "paired_incident" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "unknown", "note": "No later report on these 13 patients or on the Yongin Severance deployment appears in the cited paper." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "value_present", "value": { "kind": "text", "text": "Radiologists also interpreted the images, and clinicians decided on consultations and further care." }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0063" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Physical Health" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "At Yongin Severance Hospital, Yongin, South Korea, the deep-learning software Lunit INSIGHT CXR (versions 2 and 3, Lunit Inc.) was applied to chest radiographs. Kwak and colleagues reported in PLOS ONE on 10 March 2023 on 75 patients with resectable lung cancer between March 2020 and February 2022. Of these, 13 (17.3%) had cancers found incidentally because the software flagged a nodule on a radiograph taken for an unrelated reason. One was a 60-year-old man, a current smoker, imaged before cholecystectomy, whose left upper lobe adenocarcinoma was stage IA1. He saw a pulmonologist three days later, after the radiologist's report. The paper reports eight of the 13 saw a pulmonologist the same day, before the radiologist's official report. Its Table 1 is not fully consistent with this figure. All 13 had resection and seven were stage I needing no further treatment.", "benefit_type": "Improved Physical Health" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 3, "reason": "Thirteen patients had early, resectable lung cancer diagnosed and operated on. Seven were stage I and needed no further treatment. A survival benefit is plausible but not demonstrated in the paper." }, { "group": "organizational", "depth": 1, "reason": "Eight patients were seen by a pulmonologist on the day of the radiograph, before the formal report, shortening the diagnostic pathway." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Lunit INSIGHT CXR is built to detect and localize abnormalities, including nodules, on chest radiographs. Incidental detection of lung cancer is its intended function." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Yongin, South Korea", "developer_country": "South Korea", "deployer_country": "South Korea" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Lunit INSIGHT CXR is built to detect and localize abnormalities, including nodules, on chest radiographs. Incidental detection of lung cancer is its intended function." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2020-03-01", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 81 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/81", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Account and AI-role statement corrected against the paper's Table 1 and limitations: the example patient (patient 2) is a current smoker who saw a pulmonologist three days after the radiologist's report, the same-day consultations are the authors' timestamp-based assumption rather than a demonstrated AI effect, and one of the 13 radiologist reports (patient 12) read “No active lung disease”.", "source": "Kwak SH, Kim EK, Kim MH, Lee EH, Shin HJ (2023). PLOS ONE 18(3):e0281690, Table 1 and Discussion (limitations), https://pmc.ncbi.nlm.nih.gov/articles/PMC10004566/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.lunit.io/en/contact; https://ko.wikipedia.org/wiki/%EC%9A%A9%EC%9D%B8%EC%84%B8%EB%B8%8C%EB%9E%80%EC%8A%A4%EB%B3%91%EC%9B%90", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Routine AI analysis of chest radiographs with results visible to ordering clinicians in real time", "A prompt pulmonology referral pathway for AI-flagged nodules" ], "must_hold": [ "Clinicians review AI flags on films ordered for non-pulmonary reasons", "CT follow-up and tissue diagnosis for flagged nodules" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 81 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/81", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Account and AI-role statement corrected against the paper's Table 1 and limitations: the example patient (patient 2) is a current smoker who saw a pulmonologist three days after the radiologist's report, the same-day consultations are the authors' timestamp-based assumption rather than a demonstrated AI effect, and one of the 13 radiologist reports (patient 12) read “No active lung disease”.", "source": "Kwak SH, Kim EK, Kim MH, Lee EH, Shin HJ (2023). PLOS ONE 18(3):e0281690, Table 1 and Discussion (limitations), https://pmc.ncbi.nlm.nih.gov/articles/PMC10004566/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.lunit.io/en/contact; https://ko.wikipedia.org/wiki/%EC%9A%A9%EC%9D%B8%EC%84%B8%EB%B8%8C%EB%9E%80%EC%8A%A4%EB%B3%91%EC%9B%90", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Healthcare - thoracic radiology / lung cancer", "segment": "AI applied to routine hospital chest radiographs" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "peer-reviewed study", "citation": "Kwak SH, Kim EK, Kim MH, Lee EH, Shin HJ (2023). Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs. PLOS ONE 18(3):e0281690.", "title": "Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10004566/", "date": "2023-03-10", "written_by": "deployer", "quote": "Eight patients (61.5%) consulted a pulmonologist promptly on the same day when the chest radiograph was taken and before they received the radiologist's official report." }, { "kind": "peer-reviewed study", "citation": "Kwak SH et al. (2023). PLOS ONE 18(3):e0281690 (second quotation).", "title": "Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10004566/", "date": "2023-03-10", "written_by": "deployer", "quote": "Seven patients (53.8%) were in stage I, which meant no additional treatment was required after surgery." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0063-source-0001", "AIBD-0063-source-0002" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "peer-reviewed study", "citation": "Kwak SH, Kim EK, Kim MH, Lee EH, Shin HJ (2023). Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs. PLOS ONE 18(3):e0281690.", "title": "Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10004566/", "date": "2023-03-10", "written_by": "deployer", "quote": "Eight patients (61.5%) consulted a pulmonologist promptly on the same day when the chest radiograph was taken and before they received the radiologist's official report." }, { "kind": "peer-reviewed study", "citation": "Kwak SH et al. (2023). PLOS ONE 18(3):e0281690 (second quotation).", "title": "Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10004566/", "date": "2023-03-10", "written_by": "deployer", "quote": "Seven patients (53.8%) were in stage I, which meant no additional treatment was required after surgery." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "AI on routine chest X-rays flagged nodules in 13 patients not suspected of lung cancer; all had resectable cancer, 7 stage I, and surgery.", "account": "At Yongin Severance Hospital, Yongin, South Korea, the deep-learning software Lunit INSIGHT CXR (versions 2 and 3, Lunit Inc.) was applied to chest radiographs. Kwak and colleagues reported in PLOS ONE on 10 March 2023 on 75 patients with resectable lung cancer between March 2020 and February 2022. Of these, 13 (17.3%) had cancers found incidentally because the software flagged a nodule on a radiograph taken for an unrelated reason. One was a 60-year-old man, a current smoker, imaged before cholecystectomy, whose left upper lobe adenocarcinoma was stage IA1. He saw a pulmonologist three days later, after the radiologist's report. The paper reports eight of the 13 saw a pulmonologist the same day, before the radiologist's official report. Its Table 1 is not fully consistent with this figure. All 13 had resection and seven were stage I needing no further treatment." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The software flagged suspicious nodules on chest X-rays for clinicians to review." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "The studies used versions 2 and 3 of the system." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "versions 2 and 3" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Lunit INSIGHT CXR versions 2 and 3.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Lunit INSIGHT CXR" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Lunit Inc." } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Lunit INSIGHT CXR flags incidental nodules on chest radiographs taken for other reasons; 13 patients at Yongin Severance Hospital had resectable lung cancer", "headline": "Lunit INSIGHT CXR finds 13 resectable lung cancers incidentally" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "3d322e6fbf2835ba613f9621741daa31fd7c7800a98629b503274e8e027bf96d" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "At Yongin Severance Hospital, Yongin, South Korea, the deep-learning software Lunit INSIGHT CXR (versions 2 and 3, Lunit Inc.) was applied to chest radiographs. Kwak and colleagues reported in PLOS ONE on 10 March 2023 on 75 patients with resectable lung cancer between March 2020 and February 2022. Of these, 13 (17.3%) had cancers found incidentally because the software flagged a nodule on a radiograph taken for an unrelated reason. One was a 60-year-old man, a current smoker, imaged before cholecystectomy, whose left upper lobe adenocarcinoma was stage IA1. He saw a pulmonologist three days later, after the radiologist's report. The paper reports eight of the 13 saw a pulmonologist the same day, before the radiologist's official report. Its Table 1 is not fully consistent with this figure. All 13 had resection and seven were stage I needing no further treatment.", "replication": { "produced_by": [ "Routine AI analysis of chest radiographs with results visible to ordering clinicians in real time", "A prompt pulmonology referral pathway for AI-flagged nodules" ], "must_hold": [ "Clinicians review AI flags on films ordered for non-pulmonary reasons", "CT follow-up and tissue diagnosis for flagged nodules" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 34, "reviewed": 0, "record_mapped": 34, "not_assessed": 57, "complete": false, "by_state": { "value_present": 34, "not_reported": 0, "unresolved": 0, "not_assessed": 57, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } }