{ "id": "AIBD-0049", "title": "Exomiser prioritization contributed to rare-disease diagnoses in the 100,000 Genomes Project pilot", "summary": "Phenotype-driven ML prioritization helped diagnose 25% of 2,183 probands in the NHS pilot; a quarter of diagnoses were immediately actionable.", "date_observed": "2021-11-11", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The 100,000 Genomes Project rare-disease diagnosis pilot appeared in the New England Journal of Medicine, 11 November 2021. It applied Exomiser's phenotype-based prioritization to genomes from 2,183 families in the English National Health Service (NHS). Genetic diagnoses were found in 25% of probands, rising to 35% for monogenic disorders. 92% of the 490 diagnoses came from applied gene panels or Exomiser's top five candidates. Clinicians reported 134 of 533 genetic diagnoses, 25%, were of immediate clinical actionability, including medication changes, surveillance recommendations, clinical trial eligibility and reproductive planning.", "category": "general benefit", "unexpected": false, "purpose": "Score and rank candidate causative variants against patient phenotypes to make genome-scale rare-disease diagnosis tractable for clinical teams.", "system": "Exomiser phenotype-driven variant prioritization (machine-learning-assisted scoring and ranking)", "model_version": null, "developer": "Not named in the cited sources; the pilot's phenotype-based prioritization analysis was led by Damian Smedley's team at Queen Mary University of London and Genomics England.", "deployer": "Genomics England and NHS Genomic Medicine Centers", "location": "England, United Kingdom", "domain": "Clinical genomics / rare disease diagnosis", "segment": "Families with undiagnosed rare disease in a national health system", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "Clinicians reported that 134 of 533 genetic diagnoses (25%) were of immediate clinical actionability, including medication changes and surveillance. That is a cohort figure and no individually attributable changed outcome is cited." }, { "group": "organizational", "depth": null, "reason": "Exomiser's top five candidates together with applied panels accounted for 92% of the 490 diagnoses. That is an aggregate reduction in manual interpretation burden across the pilot rather than a discrete organizational event." }, { "group": "societal", "depth": null, "reason": "A 25% diagnostic yield across 2,183 families is reported for a national health-system pilot rather than population-wide deployment." } ], "ai_role": { "status": "assessed", "statement": "Exomiser scored and ranked candidate variants against each proband's phenotype. The pilot reports that diagnoses were detected in 77% of top-ranked candidate variants and that panels and Exomiser's top five were complementary. Clinical confirmation and reporting were performed by NHS diagnostic laboratories and clinicians, not by the software. Neither cited source says who developed Exomiser. The pilot's prioritization analysis was led by Damian Smedley's team at Queen Mary University of London and Genomics England, who are among the paper's authors. An independent evaluation is cited alongside it." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "100,000 Genomes Project Pilot Investigators; Smedley D, Smith KR, Martin A, et al. (2021). 100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care - Preliminary Report. N Engl J Med.", "title": "100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care - Preliminary Report", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7613219/", "date": "2021-11-11", "written_by": "deployer", "quote": "With the use of phenotype-based prioritization with the Exomiser to score and rank the most likely causative variants, diagnoses were detected in 77% of the top-ranked candidate variants." }, { "kind": "peer-reviewed study", "citation": "Cooperstein IB, Marwaha S, Ward A, et al. (2025). An optimized variant prioritization process for rare disease diagnostics: recommendations for Exomiser and Genomiser. Genome Med.", "title": "An optimized variant prioritization process for rare disease diagnostics", "url": "https://doi.org/10.1186/s13073-025-01546-1", "date": "2025-10-21", "written_by": "independent", "quote": "the percentage of coding diagnostic variants ranked within the top 10 candidates increased from 49.7% to 85.5%" } ], "replication": { "produced_by": [ "Genome sequencing with family data across a national pilot", "Structured phenotype terms of adequate quality", "Exomiser ranking used alongside applied gene panels, with clinical review before reporting" ], "must_hold": [ "Genome or exome sequencing with family data where available", "Structured HPO phenotype terms of adequate quality", "Clinical review of ranked candidates before reporting" ], "echoes": [ { "date": "2025-10-21", "setting": "Undiagnosed Diseases Network, 386 diagnosed probands, University of Utah and Stanford", "model_version": "Exomiser/Genomiser", "outcome": "recurred", "source": "Cooperstein IB, et al. Genome Med 2025. doi 10.1186/s13073-025-01546-1" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "535", "title": "COVID-19 Detection and Prognostication Models Allegedly Flagged for Methodological Flaws and Underlying Biases", "url": "https://incidentdatabase.ai/cite/535", "severity": null, "group": "societal", "basis": "Both are machine-learning diagnostic decision-support models relied on across health systems; the COVID detection and prognostication models proved unfit for clinical use, harming COVID patients and their healthcare providers." }, "headline": "Exomiser helps diagnose 25% of 2,183 probands in the NHS pilot", "metric": "Genetic diagnoses were found in 25% of probands, rising to 35% for monogenic disorders", "comparator": { "against": "the virtual panel-based pipeline (applied gene panels) alone", "before": "A total of 322 (66%) of the 490 diagnoses that were based on SNVs or indels from the genomes were made with the virtual panel–based pipeline", "after": "diagnoses were detected in 77% of the top-ranked candidate variants, in 86% of the top three candidates, and in 88% of the top five candidates", "basis": "measured", "quote": "With the use of phenotype-based prioritization with the Exomiser to score and rank the most likely causative variants, diagnoses were detected in 77% of the top-ranked candidate variants, in 86% of the top three candidates, and in 88% of the top five candidates (Fig. 2D). Use of the Exomiser and applied panels was complementary — 92% of the 490 diagnoses were made with the applied panels or the Exomizer top five candidates (last blue bar in Fig. 2D).", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7613219/" }, "later": null, "value": null, "developer_country": null, "deployer_country": "United Kingdom", "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 535 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/535", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Corrected the observation and evidence dates to the paper's publication date of 11 November 2021, restored the “Preliminary Report” subtitle to the evidence title, moved the record to general benefit without an event with all three depths unset because everything it reports is aggregate, and stopped naming Exomiser's developer, because neither cited source says who built the tool.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7613219/", "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 deployer added from the organization's own site or a registry.", "source": "https://www.genomicsengland.co.uk/contact-us", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7613219/", "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": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent peer-reviewed study and the deployer's peer-reviewed study. The cited evidence includes no finding by a regulator or court.", "profile": { "case_id": "AIBD-0049", "catalog_version": "1.0.0", "source_fingerprint": "a1b860f3f4af8025dd5dd72250432aae62ef19eadc064e85d7a3d6dbb464a664", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0049-source-0001", "AIBD-0049-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": "Not named in the cited sources; the pilot's phenotype-based prioritization analysis was led by Damian Smedley's team at Queen Mary University of London and Genomics England.", "deployer": "Genomics England and NHS Genomic Medicine Centers" } }, "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": "Exomiser scored and ranked candidate variants against each proband's phenotype. 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The pilot's prioritization analysis was led by Damian Smedley's team at Queen Mary University of London and Genomics England, who are among the paper's authors. 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diagnosis pilot appeared in the New England Journal of Medicine, 11 November 2021. It applied Exomiser's phenotype-based prioritization to genomes from 2,183 families in the English National Health Service (NHS). Genetic diagnoses were found in 25% of probands, rising to 35% for monogenic disorders. 92% of the 490 diagnoses came from applied gene panels or Exomiser's top five candidates. Clinicians reported 134 of 533 genetic diagnoses, 25%, were of immediate clinical actionability, including medication changes, surveillance recommendations, clinical trial eligibility and reproductive planning.", "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", "reason": "Clinicians reported that 134 of 533 genetic diagnoses (25%) were of immediate clinical actionability, including medication changes and surveillance. That is a cohort figure and no individually attributable changed outcome is cited." }, { "group": "organizational", "reason": "Exomiser's top five candidates together with applied panels accounted for 92% of the 490 diagnoses. That is an aggregate reduction in manual interpretation burden across the pilot rather than a discrete organizational event." }, { "group": "societal", "reason": "A 25% diagnostic yield across 2,183 families is reported for a national health-system pilot rather than population-wide deployment." } ] } }, "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": "Score and rank candidate causative variants against patient phenotypes to make genome-scale rare-disease diagnosis tractable for clinical teams." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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It applied Exomiser's phenotype-based prioritization to genomes from 2,183 families in the English National Health Service (NHS). Genetic diagnoses were found in 25% of probands, rising to 35% for monogenic disorders. 92% of the 490 diagnoses came from applied gene panels or Exomiser's top five candidates. 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