{
  "abstract": "Clinical genomic analysis requires experts to integrate evolving guidelines, databases, and computational evidence. As sequencing has become increasingly accessible, this complex and labor-intensive process has become a major bottleneck to the timely use of genomic information in clinical decision-making. To fill this gap, we developed ClinGeX, a large-language-model agent that composes 105 curated executable skills through task-specific retrieval and dependency-aware orchestration. On 223 temporally held-out benchmark cases, ClinGeX achieved 71.7\\% macro-averaged task-level concordance with expert reference classifications, compared with 45.7\\% for the strongest comparator. In a three-condition, counterbalanced human-AI collaboration study, 45 clinical genomic professionals assessed 135 clinically derived cases under routine (no AI assistance), DeepSeek-assisted and ClinGeX-assisted conditions. ClinGeX assistance increased raw case-level concordance across criterion-level variant analysis, genomic interpretation in rare disease and molecular oncology decision-making (51.1\\%, 57.8\\%, 64.4\\% versus 15.6\\%, 31.1\\%, 20.0\\% in comparator conditions) and reduced reported interpretation time in all scenarios. During an exploratory one-month real-world non-interventional assessment, 11 experienced professionals submitted feedback on 96 self-selected analysis episodes and reported frequent conclusion adoption and time savings. These findings support executable domain skills as a promising approach to augment specialist genomic interpretation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.40556",
  "author_total": 13,
  "authors": [
    {
      "name": "Qiaoyu Zheng"
    },
    {
      "name": "Weike Zhao"
    },
    {
      "name": "Yanjie Fan"
    },
    {
      "name": "Ye Cao"
    },
    {
      "name": "Mengqi Jiang"
    },
    {
      "name": "Xiao Zhou"
    },
    {
      "name": "Xiaoman Zhang"
    },
    {
      "name": "Pengcheng Qiu"
    },
    {
      "name": "Yongguo Yu"
    },
    {
      "name": "Yanfeng Wang"
    },
    {
      "name": "Ya Zhang"
    },
    {
      "name": "Chaoyi Wu"
    },
    {
      "name": "Weidi Xie"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a4aq61r0t001h.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.40556",
  "fulltext_url": null,
  "id": "a4aq61r0t001h",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.40556",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "q-bio.GN"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.40556",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a4aq61r0t001h.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:36Z",
      "native_id": "2610.40556",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.40556"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.GN",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Advancing Clinical Genomic Analysis with a Skill-Centred Agent",
  "title_text": "Advancing Clinical Genomic Analysis with a Skill-Centred Agent",
  "type": "preprint",
  "updated_at": "2026-11-01T12:20:56Z",
  "url": "https://research.draconitic.com/paper/a4aq61r0t001h",
  "venue": "arXiv",
  "versions": []
}