Fair by chance? On the use of algorithms in therapeutic decisions
NL-context Nieuwe AI-methoden voor therapieallocatie moeten patiënten gelijke kansen geven, in plaats van hen te selecteren op basis van starre drempelwaarden.
Samenvatting
Fair by chance? On the use of algorithms in therapeutic decisions. Predictive tools made possible by advances in machine learning techniques may help clinicians make more accurate decisions about who should be allocated costly therapies, such as immunotherapy, which only work on a relatively low proportion of patients. In this article, I argue that a fair decision procedure must recognise each patients’ chance of responding well. To do so, the procedure should not apply a fixed threshold to probability scores. Rather, each patient should be given a chance of being allocated the therapy matching her probability score. An important consequence of this conclusion is that the fair use of algorithmic scores may not guarantee that the therapy is allocated to those who will respond well to the highest possible degree.
Waarom dit ertoe doet
Deze technologische ontwikkeling kan de manier waarop AI in de zorg wordt ingezet fundamenteel veranderen.
Context (AI-duiding)
Klik op “Toon context” om AI-duiding op te halen.
Scores
De mate waarin dit signaal de Nederlandse gezondheidszorg kan beïnvloeden (1 = minimaal, 5 = transformatief).
Hoe snel actie of aandacht nodig is (1 = kan wachten, 5 = onmiddellijke aandacht vereist).
De mate van onzekerheid over de uitkomst of timing (1 = zeer voorspelbaar, 5 = zeer onzeker).
Tags
Bronnen
- Fair by chance? On the use of algorithms in therapeutic decisionsJournal of Medical Ethics —
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