{
  "id": "AIBD-0068",
  "title": "Imagene AI infers EGFR mutation from an H&E slide, letting a woman with leptomeningeal lung cancer start osimertinib two days after biopsy and avoid neuraxis radiotherapy",
  "summary": "Imagene AI predicted EGFR from an H&E slide in <48 h; rapid PCR confirmed; osimertinib began 2 days post-biopsy, no neuraxis radiotherapy.",
  "date_observed": "2022-07-01",
  "date_basis": "unassessed",
  "date_precision": "unassessed",
  "benefit_status": null,
  "source_collection": null,
  "source_entry": null,
  "account": "In July 2022 a healthy woman in her early 40s was admitted to Tel Aviv Sourasky Medical Center, Israel. She had a month of worsening headaches. Imaging showed a 2.6 cm left apical lung adenocarcinoma with leptomeningeal spread; biopsy tissue was scarce. To guide gene testing without exhausting tissue, the team ran Imagene's deep-learning algorithm on the H&E slide. Within 48 hours of biopsy it inferred EGFR mutation positivity. A rapid Idylla PCR assay confirmed the L858R exon 21 mutation; osimertinib started two days after biopsy. The authors say that was one week earlier than routine practice and avoided whole-neuraxis radiotherapy. Magnetic resonance imaging (MRI) at two months showed the enhancement gone.",
  "category": "benefit created",
  "unexpected": false,
  "purpose": "Imagene's algorithm is built to infer genomic alteration status from routine pathology slides in order to direct molecular testing; this benefit is its intended function.",
  "system": "Imagene AI (H&E-based molecular inference)",
  "model_version": null,
  "developer": "Imagene",
  "deployer": "Tel Aviv Sourasky Medical Center",
  "location": "Tel Aviv, Israel",
  "domain": "Healthcare - oncology / molecular pathology",
  "segment": "AI-guided biomarker testing in non-small-cell lung cancer",
  "benefit_type": "Improved Physical Health",
  "taxonomy_note": null,
  "registry_ids": {},
  "ratings": [
    {
      "group": "personal",
      "depth": 3,
      "reason": "Targeted therapy for leptomeningeal disease started a week earlier and whole-neuraxis radiotherapy was avoided, with MRI improvement at two months. Survival benefit is not reported."
    },
    {
      "group": "organizational",
      "depth": 1,
      "reason": "The slide-based inference let the team choose a single rapid PCR test instead of waiting for sequencing on limited tissue."
    }
  ],
  "ai_role": {
    "status": "assessed",
    "statement": "The AI inference directed which rapid test to run; treatment began only after PCR confirmation. The AI therefore changed the sequence and speed of testing rather than the final diagnosis. Authors are Tel Aviv Sourasky / Tel Aviv University clinicians declaring no competing interests. A second patient in the report (February 2023) followed the same path."
  },
  "evidence": [
    {
      "kind": "case report",
      "citation": "Waissengrin B, Garasimov A, Bainhoren O, Merimsky O, Shamai S, Erental A, Wolf I, Hershkovitz D (2023). Artificial intelligence (AI) molecular analysis tool assists in rapid treatment decision in lung cancer: a case report. Journal of Clinical Pathology 76(11):790-792.",
      "title": "Artificial intelligence (AI) molecular analysis tool assists in rapid treatment decision in lung cancer: a case report",
      "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/",
      "date": null,
      "written_by": "deployer",
      "quote": "The algorithmic inference identified positivity for EGFR mutation."
    },
    {
      "kind": "case report",
      "citation": "Waissengrin B et al. (2023). J Clin Pathol 76(11):790-792 (second quotation).",
      "title": "Artificial intelligence (AI) molecular analysis tool assists in rapid treatment decision in lung cancer: a case report",
      "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/",
      "date": null,
      "written_by": "deployer",
      "quote": "The incorporation of the AI solution not only enabled targeted treatment 1 week earlier than in the routine practice"
    },
    {
      "kind": "case report",
      "citation": "Waissengrin B et al. (2023). J Clin Pathol 76(11):790-792 (third quotation).",
      "title": "Artificial intelligence (AI) molecular analysis tool assists in rapid treatment decision in lung cancer: a case report",
      "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/",
      "date": null,
      "written_by": "deployer",
      "quote": "resulted in avoiding altogether whole neuroaxis radiotherapy"
    }
  ],
  "replication": {
    "produced_by": [
      "Scanning the diagnostic H&E slide and running the inference within 48 hours of biopsy",
      "A rapid single-gene PCR assay available for confirmation"
    ],
    "must_hold": [
      "PCR or sequencing confirmation before treatment",
      "A tumor with a targetable driver the model can infer"
    ],
    "echoes": [
      {
        "date": "2023-02-01",
        "setting": "Second woman in her early 40s with EGFR-mutant NSCLC at the same center, February 2023; same AI-guided path, MRI improvement at two months",
        "model_version": null,
        "outcome": "recurred",
        "source": "Waissengrin B et al. (2023) J Clin Pathol 76(11):790-792, https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/"
      }
    ]
  },
  "paired_incident": {
    "database": "AI Incident Database",
    "identifier": "225",
    "title": "IBM Watson for Oncology Criticized by Customers for Allegedly Unsafe and Inaccurate Cancer Treatment Recommendations",
    "url": "https://incidentdatabase.ai/cite/225",
    "severity": null,
    "group": "personal",
    "basis": "Same domain and task, AI decision support steering cancer treatment: Watson for Oncology gave allegedly unsafe and incorrect treatment recommendations, harming cancer patients and oncologists, the groups Imagene's EGFR inference served."
  },
  "headline": "Imagene AI infers EGFR from H&E; osimertinib 2 days post-biopsy",
  "metric": "one week earlier than routine practice and avoided whole-neuraxis radiotherapy",
  "comparator": {
    "against": "routine practice, in which comprehensive genomic profiling precedes targeted therapy",
    "before": "CGP is time-consuming, with the time for diagnosis reaching up to 4 weeks",
    "after": "targeted treatment 1 week earlier than in the routine practice; osimertinib given 2 days after the biopsy",
    "basis": "assumed",
    "quote": "The incorporation of the AI solution not only enabled targeted treatment 1 week earlier than in the routine practice but also resulted in avoiding altogether whole neuroaxis radiotherapy.",
    "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/"
  },
  "later": {
    "status": "persisted",
    "date": null,
    "note": "MRI two months after osimertinib began (exact date not given) showed disappearance of the leptomeningeal enhancement in this patient and in the second patient.",
    "quote": "Both patients' MRI scans after 2 months of TKI showed significant improvement with disappearance of the enhancement.",
    "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/"
  },
  "value": null,
  "developer_country": "Israel",
  "deployer_country": "Israel",
  "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 225 (medium confidence); severity not given by the source.",
      "source": "https://incidentdatabase.ai/cite/225",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Added what the benefit is measured against, what happened since from the cited sources.",
      "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/; https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/",
      "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 Organizational Efficiency 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://imagene-ai.com/privacy-policy/; https://en.wikipedia.org/wiki/Tel_Aviv_Sourasky_Medical_Center",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Prose pass: a rating reason, account, 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 case report. The cited evidence includes no independent source and no finding by a regulator or court.",
  "profile": {
    "case_id": "AIBD-0068",
    "catalog_version": "1.0.0",
    "source_fingerprint": "f95325356bf6185ad2551e47258c9cbba0b69a755721dbb9d46c0bafd1aac5bd",
    "collection_refs": [
      {
        "collection_id": "aibd",
        "version": "snapshot-8bf7cfec9cb185aa"
      },
      {
        "collection_id": "aibd",
        "version": "2026-10-25"
      }
    ],
    "source_ids": [
      "AIBD-0068-source-0001",
      "AIBD-0068-source-0002",
      "AIBD-0068-source-0003"
    ],
    "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": "Imagene",
            "deployer": "Tel Aviv Sourasky Medical Center"
          }
        },
        "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 AI inference directed which rapid test to run; treatment began only after PCR confirmation. The AI therefore changed the sequence and speed of testing rather than the final diagnosis. Authors are Tel Aviv Sourasky / Tel Aviv University clinicians declaring no competing interests. A second patient in the report (February 2023) followed the same path."
            }
          }
        },
        "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"
      },
      "ai_relationship": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "category": "benefit created",
            "ai_role": {
              "status": "assessed",
              "statement": "The AI inference directed which rapid test to run; treatment began only after PCR confirmation. The AI therefore changed the sequence and speed of testing rather than the final diagnosis. Authors are Tel Aviv Sourasky / Tel Aviv University clinicians declaring no competing interests. A second patient in the report (February 2023) followed the same path."
            }
          }
        },
        "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": "Targeted therapy for leptomeningeal disease started a week earlier and whole-neuraxis radiotherapy was avoided, with MRI improvement at two months. Survival benefit is not reported."
              },
              {
                "group": "organizational",
                "depth": 1,
                "reason": "The slide-based inference let the team choose a single rapid PCR test instead of waiting for sequencing on limited tissue."
              }
            ]
          }
        },
        "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 case report. 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": "Targeted therapy for leptomeningeal disease started a week earlier and whole-neuraxis radiotherapy was avoided, with MRI improvement at two months. Survival benefit is not reported."
              },
              {
                "group": "organizational",
                "depth": 1,
                "reason": "The slide-based inference let the team choose a single rapid PCR test instead of waiting for sequencing on limited tissue."
              }
            ]
          }
        },
        "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": "Targeted therapy for leptomeningeal disease started a week earlier and whole-neuraxis radiotherapy was avoided, with MRI improvement at two months. Survival benefit is not reported."
              },
              {
                "group": "organizational",
                "depth": 1,
                "reason": "The slide-based inference let the team choose a single rapid PCR test instead of waiting for sequencing on limited tissue."
              }
            ]
          }
        },
        "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 domain and task, AI decision support steering cancer treatment: Watson for Oncology gave allegedly unsafe and incorrect treatment recommendations, harming cancer patients and oncologists, the groups Imagene's EGFR inference served.",
            "links": [
              {
                "database": "AI Incident Database",
                "identifier": "225",
                "title": "IBM Watson for Oncology Criticized by Customers for Allegedly Unsafe and Inaccurate Cancer Treatment Recommendations",
                "url": "https://incidentdatabase.ai/cite/225",
                "group": "personal",
                "basis": "Same domain and task, AI decision support steering cancer treatment: Watson for Oncology gave allegedly unsafe and incorrect treatment recommendations, harming cancer patients and oncologists, the groups Imagene's EGFR inference served.",
                "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": "one week earlier than routine practice and avoided whole-neuraxis radiotherapy",
          "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",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "external_references[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "paired_incident": {
              "database": "AI Incident Database",
              "identifier": "225",
              "title": "IBM Watson for Oncology Criticized by Customers for Allegedly Unsafe and Inaccurate Cancer Treatment Recommendations",
              "url": "https://incidentdatabase.ai/cite/225",
              "group": "personal",
              "basis": "Same domain and task, AI decision support steering cancer treatment: Watson for Oncology gave allegedly unsafe and incorrect treatment recommendations, harming cancer patients and oncologists, the groups Imagene's EGFR inference served."
            }
          }
        },
        "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": "persisted",
              "note": "MRI two months after osimertinib began (exact date not given) showed disappearance of the leptomeningeal enhancement in this patient and in the second patient.",
              "quote": "Both patients' MRI scans after 2 months of TKI showed significant improvement with disappearance of the enhancement.",
              "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/"
            }
          }
        },
        "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": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "id": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "id": "AIBD-0068"
          }
        },
        "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": "In July 2022 a healthy woman in her early 40s was admitted to Tel Aviv Sourasky Medical Center, Israel. She had a month of worsening headaches. Imaging showed a 2.6 cm left apical lung adenocarcinoma with leptomeningeal spread; biopsy tissue was scarce. To guide gene testing without exhausting tissue, the team ran Imagene's deep-learning algorithm on the H&E slide. Within 48 hours of biopsy it inferred EGFR mutation positivity. A rapid Idylla PCR assay confirmed the L858R exon 21 mutation; osimertinib started two days after biopsy. The authors say that was one week earlier than routine practice and avoided whole-neuraxis radiotherapy. Magnetic resonance imaging (MRI) at two months showed the enhancement gone.",
            "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": "Targeted therapy for leptomeningeal disease started a week earlier and whole-neuraxis radiotherapy was avoided, with MRI improvement at two months. Survival benefit is not reported."
              },
              {
                "group": "organizational",
                "depth": 1,
                "reason": "The slide-based inference let the team choose a single rapid PCR test instead of waiting for sequencing on limited tissue."
              }
            ]
          }
        },
        "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": "Imagene's algorithm is built to infer genomic alteration status from routine pathology slides in order to direct molecular testing; this benefit 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": "Tel Aviv, Israel",
            "developer_country": "Israel",
            "deployer_country": "Israel"
          }
        },
        "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": "Imagene's algorithm is built to infer genomic alteration status from routine pathology slides in order to direct molecular testing; this benefit 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": "2022-07-01",
            "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 225 (medium confidence); severity not given by the source.",
                "source": "https://incidentdatabase.ai/cite/225",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Added what the benefit is measured against, what happened since from the cited sources.",
                "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/; https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/",
                "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 Organizational Efficiency 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://imagene-ai.com/privacy-policy/; https://en.wikipedia.org/wiki/Tel_Aviv_Sourasky_Medical_Center",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Prose pass: a rating reason, account, 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": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "replication": {
              "produced_by": [
                "Scanning the diagnostic H&E slide and running the inference within 48 hours of biopsy",
                "A rapid single-gene PCR assay available for confirmation"
              ],
              "must_hold": [
                "PCR or sequencing confirmation before treatment",
                "A tumor with a targetable driver the model can infer"
              ],
              "echoes": [
                {
                  "date": "2023-02-01",
                  "setting": "Second woman in her early 40s with EGFR-mutant NSCLC at the same center, February 2023; same AI-guided path, MRI improvement at two months",
                  "outcome": "recurred",
                  "source": "Waissengrin B et al. (2023) J Clin Pathol 76(11):790-792, https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/"
                }
              ]
            }
          }
        },
        "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"
      },
      "replication.conditions": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "replication": {
              "produced_by": [
                "Scanning the diagnostic H&E slide and running the inference within 48 hours of biopsy",
                "A rapid single-gene PCR assay available for confirmation"
              ],
              "must_hold": [
                "PCR or sequencing confirmation before treatment",
                "A tumor with a targetable driver the model can infer"
              ],
              "echoes": [
                {
                  "date": "2023-02-01",
                  "setting": "Second woman in her early 40s with EGFR-mutant NSCLC at the same center, February 2023; same AI-guided path, MRI improvement at two months",
                  "outcome": "recurred",
                  "source": "Waissengrin B et al. (2023) J Clin Pathol 76(11):790-792, https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/"
                }
              ]
            }
          }
        },
        "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 225 (medium confidence); severity not given by the source.",
                "source": "https://incidentdatabase.ai/cite/225",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Added what the benefit is measured against, what happened since from the cited sources.",
                "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/; https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/",
                "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 Organizational Efficiency 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://imagene-ai.com/privacy-policy/; https://en.wikipedia.org/wiki/Tel_Aviv_Sourasky_Medical_Center",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Prose pass: a rating reason, account, 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 - oncology / molecular pathology",
            "segment": "AI-guided biomarker testing in non-small-cell lung cancer"
          }
        },
        "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": "case report",
                "citation": "Waissengrin B, Garasimov A, Bainhoren O, Merimsky O, Shamai S, Erental A, Wolf I, Hershkovitz D (2023). Artificial intelligence (AI) molecular analysis tool assists in rapid treatment decision in lung cancer: a case report. Journal of Clinical Pathology 76(11):790-792.",
                "title": "Artificial intelligence (AI) molecular analysis tool assists in rapid treatment decision in lung cancer: a case report",
                "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/",
                "written_by": "deployer",
                "quote": "The algorithmic inference identified positivity for EGFR mutation."
              },
              {
                "kind": "case report",
                "citation": "Waissengrin B et al. (2023). J Clin Pathol 76(11):790-792 (second quotation).",
                "title": "Artificial intelligence (AI) molecular analysis tool assists in rapid treatment decision in lung cancer: a case report",
                "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/",
                "written_by": "deployer",
                "quote": "The incorporation of the AI solution not only enabled targeted treatment 1 week earlier than in the routine practice"
              },
              {
                "kind": "case report",
                "citation": "Waissengrin B et al. (2023). J Clin Pathol 76(11):790-792 (third quotation).",
                "title": "Artificial intelligence (AI) molecular analysis tool assists in rapid treatment decision in lung cancer: a case report",
                "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/",
                "written_by": "deployer",
                "quote": "resulted in avoiding altogether whole neuroaxis radiotherapy"
              }
            ]
          }
        },
        "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-0068-source-0001",
            "AIBD-0068-source-0002",
            "AIBD-0068-source-0003"
          ],
          "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": "case report",
                "citation": "Waissengrin B, Garasimov A, Bainhoren O, Merimsky O, Shamai S, Erental A, Wolf I, Hershkovitz D (2023). Artificial intelligence (AI) molecular analysis tool assists in rapid treatment decision in lung cancer: a case report. Journal of Clinical Pathology 76(11):790-792.",
                "title": "Artificial intelligence (AI) molecular analysis tool assists in rapid treatment decision in lung cancer: a case report",
                "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/",
                "written_by": "deployer",
                "quote": "The algorithmic inference identified positivity for EGFR mutation."
              },
              {
                "kind": "case report",
                "citation": "Waissengrin B et al. (2023). J Clin Pathol 76(11):790-792 (second quotation).",
                "title": "Artificial intelligence (AI) molecular analysis tool assists in rapid treatment decision in lung cancer: a case report",
                "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/",
                "written_by": "deployer",
                "quote": "The incorporation of the AI solution not only enabled targeted treatment 1 week earlier than in the routine practice"
              },
              {
                "kind": "case report",
                "citation": "Waissengrin B et al. (2023). J Clin Pathol 76(11):790-792 (third quotation).",
                "title": "Artificial intelligence (AI) molecular analysis tool assists in rapid treatment decision in lung cancer: a case report",
                "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/",
                "written_by": "deployer",
                "quote": "resulted in avoiding altogether whole neuroaxis radiotherapy"
              }
            ]
          }
        },
        "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": "Imagene AI predicted EGFR from an H&E slide in <48 h; rapid PCR confirmed; osimertinib began 2 days post-biopsy, no neuraxis radiotherapy.",
            "account": "In July 2022 a healthy woman in her early 40s was admitted to Tel Aviv Sourasky Medical Center, Israel. She had a month of worsening headaches. Imaging showed a 2.6 cm left apical lung adenocarcinoma with leptomeningeal spread; biopsy tissue was scarce. To guide gene testing without exhausting tissue, the team ran Imagene's deep-learning algorithm on the H&E slide. Within 48 hours of biopsy it inferred EGFR mutation positivity. A rapid Idylla PCR assay confirmed the L858R exon 21 mutation; osimertinib started two days after biopsy. The authors say that was one week earlier than routine practice and avoided whole-neuraxis radiotherapy. Magnetic resonance imaging (MRI) at two months showed the enhancement gone."
          }
        },
        "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": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].deployment_configuration": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "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": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].product_name": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "system": "Imagene AI (H&E-based molecular inference)"
          }
        },
        "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": "Imagene"
          }
        },
        "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": "Imagene AI infers EGFR mutation from an H&E slide, letting a woman with leptomeningeal lung cancer start osimertinib two days after biopsy and avoid neuraxis radiotherapy",
            "headline": "Imagene AI infers EGFR from H&E; osimertinib 2 days post-biopsy"
          }
        },
        "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": [
            "f95325356bf6185ad2551e47258c9cbba0b69a755721dbb9d46c0bafd1aac5bd"
          ],
          "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": "In July 2022 a healthy woman in her early 40s was admitted to Tel Aviv Sourasky Medical Center, Israel. She had a month of worsening headaches. Imaging showed a 2.6 cm left apical lung adenocarcinoma with leptomeningeal spread; biopsy tissue was scarce. To guide gene testing without exhausting tissue, the team ran Imagene's deep-learning algorithm on the H&E slide. Within 48 hours of biopsy it inferred EGFR mutation positivity. A rapid Idylla PCR assay confirmed the L858R exon 21 mutation; osimertinib started two days after biopsy. The authors say that was one week earlier than routine practice and avoided whole-neuraxis radiotherapy. Magnetic resonance imaging (MRI) at two months showed the enhancement gone.",
            "replication": {
              "produced_by": [
                "Scanning the diagnostic H&E slide and running the inference within 48 hours of biopsy",
                "A rapid single-gene PCR assay available for confirmation"
              ],
              "must_hold": [
                "PCR or sequencing confirmation before treatment",
                "A tumor with a targetable driver the model can infer"
              ],
              "echoes": [
                {
                  "date": "2023-02-01",
                  "setting": "Second woman in her early 40s with EGFR-mutant NSCLC at the same center, February 2023; same AI-guided path, MRI improvement at two months",
                  "outcome": "recurred",
                  "source": "Waissengrin B et al. (2023) J Clin Pathol 76(11):790-792, https://pmc.ncbi.nlm.nih.gov/articles/PMC10646899/"
                }
              ]
            }
          }
        },
        "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": 31,
      "reviewed": 0,
      "record_mapped": 31,
      "not_assessed": 60,
      "complete": false,
      "by_state": {
        "value_present": 31,
        "not_reported": 0,
        "unresolved": 0,
        "not_assessed": 60,
        "not_applicable": 0,
        "withheld": 0,
        "source_unavailable": 0
      }
    }
  }
}
