Vol. 25 No. 44 (2026): Artificial Intelligence and health
Originalartikler

Personalized predictions: Predictive Artificial Intelligence (AI) in clinical practice

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  • Iben Mundbjerg Gjødsbøl,
  • Mette Nordahl Svendsen
Iben Mundbjerg Gjødsbøl
Centre for Medical Science and Technology Studies, Department of Public Health, University of Copenhagen
Mette Nordahl Svendsen
Centre for Medical Science and Technology Studies, Department of Public Health, University of Copenhagen
DOI: https://doi.org/10.7146/tfss.v25i44.156294

Published 2026-06-26

Keywords

  • sundhedsprognoser,
  • Kunstig intelligens,
  • algoritmer,
  • effektivisering,
  • klinisk beslutningstagning

How to Cite

Gjødsbøl, I. M., & Svendsen, M. N. (2026). Personalized predictions: Predictive Artificial Intelligence (AI) in clinical practice. Tidsskrift for Forskning I Sygdom Og Samfund - Journal of Research in Sickness and Society, 25(44), 76–99. https://doi.org/10.7146/tfss.v25i44.156294

Copyright (c) 2026 Iben Mundbjerg Gjødsbøl, Mette Nordahl Svendsen

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

In politics, healthcare, and biomedical research, there are high expectations that predictive algorithms developed with artificial intelligence (AI) will individualize and optimize prevention, diagnosis, and treatment. The hope is that algorithms capable of integrating and processing vast amounts of multi-modal, patient-specific data can assist healthcare professionals in predicting future events such as disease, complications, and death for individual patients. In other words, predictive algorithms are presented as modern-day oracles, offering ‘personal prognoses’ with unprecedented precision. In 2023 the CARDIAIHD algorithm was implemented as a research experiment in the Electronic Health Record system, Sundhedsplatformen, in Denmark’s Capital Region and Region Zealand. The algorithm predicts the risk of death—or the survival prognosis—for patients hospitalized with acute ischemic heart disease. Drawing upon ongoing ethnographic fieldwork among cardiologists treating heart patients, we show how prognosis is situated in both time and place, and how the prognosis generated by the CARDIAIHD algorithm constitutes one of several possible futures in clinical practice. We argue that predictive algorithms—such as the CARDIAIHD-algorithm—not only displace clinicians’ tasks of integrating and interpreting data, but also demand that they engage in new forms of interpretive labor. Moreover, clinicians may face challenges in discerning how algorithmic predictions can be meaningfully applied in clinical decision-making.

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