View in EDS HTML Full Text PDF Full Text

All in the Name of Artificial Intelligence: A Commentary on Linardon (2025).

Saved in:
Bibliographic Details
Title: All in the Name of Artificial Intelligence: A Commentary on Linardon (2025).
Authors: Burger, Pia, Ghosh, Sreejita
Source: International Journal of Eating Disorders. Jul2025, Vol. 58 Issue 7, p1191-1195. 5p.
Subjects: Treatment of eating disorders, Self-efficacy, Artificial intelligence, Natural language processing, Decision making in clinical medicine, Workflow, Trust, Physician practice patterns, Attitudes of medical personnel, Patient-professional relations, Automation, Patients' attitudes
Abstract: Artificial Intelligence (AI) is being rapidly integrated into healthcare, but Linardon et al. reveal a troubling gap between what AI actually is, its capabilities, and the patients' and clinicians' perceptions of it—equating AI solely with large language models. In this commentary, we discuss concerns over AI's black‐box nature, its potential to perpetuate existing biases, and the blind trust some people place in its decisions, despite evidence that quantitative models outperform large language models in clinical decision‐making tasks. While AI holds promise in eating disorder care, its integration requires a nuanced understanding of its capabilities, limitations, and the critical distinction between AI for administrative automation, clinical decision‐making, and direct‐to‐patient AI. Poorly designed AI alerts risk becoming just another ignorable nuisance, while patient‐facing AI could either empower individuals or drown them in notifications and misinformation. Before we anoint AI as healthcare's savior, it requires validation for accuracy, reliability, fairness, real‐world usability, and its actual measurable impact on clinicians and patients. The real challenge is not whether AI will change healthcare but ensuring it does so responsibly—by integrating it thoughtfully into workflows, such that it is supporting rather than replacing clinical judgment, and maintaining accountability when things go wrong. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Eating Disorders is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: pbh
DbLabel: Psychology and Behavioral Sciences Collection
An: 187574249
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: All in the Name of Artificial Intelligence: A Commentary on Linardon (2025).
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Burger%2C+Pia%22">Burger, Pia</searchLink><br /><searchLink fieldCode="AR" term="%22Ghosh%2C+Sreejita%22">Ghosh, Sreejita</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Eating+Disorders%22">International Journal of Eating Disorders</searchLink>. Jul2025, Vol. 58 Issue 7, p1191-1195. 5p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Treatment+of+eating+disorders%22">Treatment of eating disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Self-efficacy%22">Self-efficacy</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making+in+clinical+medicine%22">Decision making in clinical medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Workflow%22">Workflow</searchLink><br /><searchLink fieldCode="DE" term="%22Trust%22">Trust</searchLink><br /><searchLink fieldCode="DE" term="%22Physician+practice+patterns%22">Physician practice patterns</searchLink><br /><searchLink fieldCode="DE" term="%22Attitudes+of+medical+personnel%22">Attitudes of medical personnel</searchLink><br /><searchLink fieldCode="DE" term="%22Patient-professional+relations%22">Patient-professional relations</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Patients'+attitudes%22">Patients' attitudes</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Artificial Intelligence (AI) is being rapidly integrated into healthcare, but Linardon et al. reveal a troubling gap between what AI actually is, its capabilities, and the patients' and clinicians' perceptions of it—equating AI solely with large language models. In this commentary, we discuss concerns over AI's black‐box nature, its potential to perpetuate existing biases, and the blind trust some people place in its decisions, despite evidence that quantitative models outperform large language models in clinical decision‐making tasks. While AI holds promise in eating disorder care, its integration requires a nuanced understanding of its capabilities, limitations, and the critical distinction between AI for administrative automation, clinical decision‐making, and direct‐to‐patient AI. Poorly designed AI alerts risk becoming just another ignorable nuisance, while patient‐facing AI could either empower individuals or drown them in notifications and misinformation. Before we anoint AI as healthcare's savior, it requires validation for accuracy, reliability, fairness, real‐world usability, and its actual measurable impact on clinicians and patients. The real challenge is not whether AI will change healthcare but ensuring it does so responsibly—by integrating it thoughtfully into workflows, such that it is supporting rather than replacing clinical judgment, and maintaining accountability when things go wrong. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Eating Disorders is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=187574249
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/eat.24446
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 5
        StartPage: 1191
    Subjects:
      – SubjectFull: Treatment of eating disorders
        Type: general
      – SubjectFull: Self-efficacy
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Decision making in clinical medicine
        Type: general
      – SubjectFull: Workflow
        Type: general
      – SubjectFull: Trust
        Type: general
      – SubjectFull: Physician practice patterns
        Type: general
      – SubjectFull: Attitudes of medical personnel
        Type: general
      – SubjectFull: Patient-professional relations
        Type: general
      – SubjectFull: Automation
        Type: general
      – SubjectFull: Patients' attitudes
        Type: general
    Titles:
      – TitleFull: All in the Name of Artificial Intelligence: A Commentary on Linardon (2025).
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Burger, Pia
      – PersonEntity:
          Name:
            NameFull: Ghosh, Sreejita
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Text: Jul2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 02763478
          Numbering:
            – Type: volume
              Value: 58
            – Type: issue
              Value: 7
          Titles:
            – TitleFull: International Journal of Eating Disorders
              Type: main
ResultId 1