Academic Journal
Προβολή σε EDS

Rethinking scale in AI-driven genomic medicine – the role of small biobanks

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Rethinking scale in AI-driven genomic medicine – the role of small biobanks
Συγγραφείς: Grech, Laura, Pace, Nikolai Paul
Στοιχεία εκδότη: Frontiers Media S.A.
Έτος έκδοσης: 2026
Συλλογή: University of Malta: OAR@UM / L-Università ta' Malta
Θεματικοί όροι: Biobanks -- Moral and ethical aspects, Artificial intelligence -- Medical applications, Medical genetics -- Data processing, Human genome -- Health aspects, Genomic medicine
Περιγραφή: Artificial intelligence is rapidly advancing genomic medicine, but its clinical trustworthiness cannot be secured by larger datasets alone. This perspective argues that small biobanks, including national, regional, hospital-linked and disease-focused collections provide essential stress tests for AI-driven genomic medicine because they expose failures in population calibration, rare-variant interpretation, phenotype realism, privacy protection, and governance. Rather than serving primarily as substrates for training general-purpose models, small biobanks are most valuable as environments for external validation, local calibration, interpretability, federated analysis, and accountable deployment. Their local representativeness, clinical linkage, and governance structures can help determine whether AI predictions remain valid and clinically useful outside the large datasets on which they were developed. Trustworthy AI-powered genomic medicine will therefore depend not only on larger models and larger datasets, but also on smaller, well-governed biobanks that force those models to prove their validity in real-world settings. ; peer-reviewed
Τύπος εγγράφου: article in journal/newspaper
Γλώσσα: English
Relation: https://www.um.edu.mt/library/oar/handle/123456789/148788
DOI: 10.3389/fgene.2026.1903325
Διαθεσιμότητα: https://www.um.edu.mt/library/oar/handle/123456789/148788
https://doi.org/10.3389/fgene.2026.1903325
Rights: info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.
Αριθμός Καταχώρησης: edsbas.CADAD0C0
Βάση Δεδομένων: BASE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://www.um.edu.mt/library/oar/handle/123456789/148788#
    Name: EDS - BASE (ns324271)
    Category: fullText
    Text: View record from BASE
Header DbId: edsbas
DbLabel: BASE
An: edsbas.CADAD0C0
RelevancyScore: 1011
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1010.63568115234
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Rethinking scale in AI-driven genomic medicine – the role of small biobanks
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Grech%2C+Laura%22">Grech, Laura</searchLink><br /><searchLink fieldCode="AR" term="%22Pace%2C+Nikolai+Paul%22">Pace, Nikolai Paul</searchLink>
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: Frontiers Media S.A.
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2026
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: University of Malta: OAR@UM / L-Università ta' Malta
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Biobanks+--+Moral+and+ethical+aspects%22">Biobanks -- Moral and ethical aspects</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence+--+Medical+applications%22">Artificial intelligence -- Medical applications</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+genetics+--+Data+processing%22">Medical genetics -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Human+genome+--+Health+aspects%22">Human genome -- Health aspects</searchLink><br /><searchLink fieldCode="DE" term="%22Genomic+medicine%22">Genomic medicine</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Artificial intelligence is rapidly advancing genomic medicine, but its clinical trustworthiness cannot be secured by larger datasets alone. This perspective argues that small biobanks, including national, regional, hospital-linked and disease-focused collections provide essential stress tests for AI-driven genomic medicine because they expose failures in population calibration, rare-variant interpretation, phenotype realism, privacy protection, and governance. Rather than serving primarily as substrates for training general-purpose models, small biobanks are most valuable as environments for external validation, local calibration, interpretability, federated analysis, and accountable deployment. Their local representativeness, clinical linkage, and governance structures can help determine whether AI predictions remain valid and clinically useful outside the large datasets on which they were developed. Trustworthy AI-powered genomic medicine will therefore depend not only on larger models and larger datasets, but also on smaller, well-governed biobanks that force those models to prove their validity in real-world settings. ; peer-reviewed
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: article in journal/newspaper
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: NoteTitleSource
  Label: Relation
  Group: SrcInfo
  Data: https://www.um.edu.mt/library/oar/handle/123456789/148788
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.3389/fgene.2026.1903325
– Name: URL
  Label: Availability
  Group: URL
  Data: https://www.um.edu.mt/library/oar/handle/123456789/148788<br />https://doi.org/10.3389/fgene.2026.1903325
– Name: Copyright
  Label: Rights
  Group: Cpyrght
  Data: info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsbas.CADAD0C0
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.CADAD0C0
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3389/fgene.2026.1903325
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: Biobanks -- Moral and ethical aspects
        Type: general
      – SubjectFull: Artificial intelligence -- Medical applications
        Type: general
      – SubjectFull: Medical genetics -- Data processing
        Type: general
      – SubjectFull: Human genome -- Health aspects
        Type: general
      – SubjectFull: Genomic medicine
        Type: general
    Titles:
      – TitleFull: Rethinking scale in AI-driven genomic medicine – the role of small biobanks
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Grech, Laura
      – PersonEntity:
          Name:
            NameFull: Pace, Nikolai Paul
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-locals
              Value: edsbas
            – Type: issn-locals
              Value: edsbas.oa
ResultId 1