{
  "abstract": "The rise of big data in health care and health research, artificial intelligence methods, and computational simulation capabilities is driving the development of so-called in silico approaches in medical research. These approaches offer exciting prospects for evaluating medical interventions and supporting clinical decision-making, with the goal of personalizing medicine. Falling within the field of non-interventional research, in silico studies are not subject to most of the regulatory requirements that apply to research involving human subjects. However, this reduction in administrative burdens raises questions regarding the transparency of research and the reliability of results. This article presents in silico approaches, analyzes how they fit within the French and European regulatory frameworks for clinical research, and then discusses the limitations of the current regulatory framework. It argues that the lack of a framework ensuring transparency and governance in research constitutes an obstacle to the rigorous evaluation of in silico studies and to their use as a basis for decision-making. Finally, it makes recommendations aimed at promoting the responsible development of in silico approaches, balancing scientific innovation, methodological quality, and public trust.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Isabelle Drouet"
    },
    {
      "name": "Claire Bahans"
    },
    {
      "name": "Antoine Chambaz"
    },
    {
      "name": "Yohann Foucher"
    },
    {
      "name": "Noémie Dubruel"
    },
    {
      "name": "Sébastien Bailly"
    },
    {
      "name": "Philippe Saint-Pierre"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8rdy0q3b4475c.bib",
  "cited_by": 0,
  "doi": null,
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  "id": "8rdy0q3b4475c",
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  "landing_url": "https://hal.science/hal-05828798v1",
  "language": "fr",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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  "openalex": null,
  "pdf_url": "https://hal.science/hal-05828798/document",
  "pmcid": null,
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  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8rdy0q3b4475c.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:00Z",
      "native_id": "hal-05828798",
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      "url": "https://hal.science/hal-05828798v1"
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  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:0.sdv",
      "subfield": "general"
    },
    {
      "field": "mathematics",
      "origin": "hal:0.stat",
      "subfield": "statistics"
    },
    {
      "field": "medicine",
      "origin": "hal:1.sdv.spee",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "In silico approaches in medical research",
  "title_text": "In silico approaches in medical research",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:00Z",
  "url": "https://research.draconitic.com/paper/8rdy0q3b4475c",
  "venue": "HAL",
  "versions": []
}