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Continuous learning as a threat to care.

Purpose: To highlight the risk that the use of adaptive machine learning systems may result in clinicians spending more time on computers and less time caring for patients. Methods: Philosophical and ethical reflection on issues identified in the literature on human-computer interaction. Results: Ad...

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Published in:Health & Technology Vol. 16; no. 3; pp. 381 - 386
Main Authors: Sparrow, Robert, Daus, Zachary, Howard, Mark, Hatherley, Joshua, Kwan, Patrick
Format: Journal Article
Published: Springer Nature May2026
Online Access:View this record in EBSCOhost
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      dt: May2026
      vid: 16
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      pub: Springer Nature
      place: New York, New York
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        193278006
        189407073
        10.1007/s12553-025-01030-1
        193278006
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        atl: Continuous learning as a threat to care.
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        au:
          Sparrow, Robert
          Daus, Zachary
          Howard, Mark
          Hatherley, Joshua
          Kwan, Patrick
        affil: https://ror.org/02bfwt286 Philosophy Program, Faculty of Arts, Monash University, Clayton, 3800, Melbourne, VIC, Australia
      sug:
        subj:
          Health Personnel Psychosocial Factors
          Machine Learning
          Learning Methods
          Computers and Computerization
          Patient Care Ethical Issues
          Time Factors
          Ethics, Professional
          User-Computer Interface
          Referral and Consultation
          Education, Continuing
          Physician-Patient Relations
          Artificial Intelligence
          Attitude of Health Personnel
      ab: Purpose: To highlight the risk that the use of adaptive machine learning systems may result in clinicians spending more time on computers and less time caring for patients. Methods: Philosophical and ethical reflection on issues identified in the literature on human-computer interaction. Results: Adaptive machine learning systems learn from data generated by their use. Managing the process by which these systems improve is likely to require that clinicians pay more attention to them than they do to other software. The call for "explainable AI" will create a new "hermeneutic burden" for clinicians when it comes to the use of these systems. Conclusions: Adaptive machine learning systems are likely to exacerbate problems associated with computers becoming the "third party in the room" during clinical consultations. A key challenge is to ensure that the application of adaptive learning enhances, rather than damages, the relationship between clinician and patient.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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