Doctors vs. algorithms: physicians, too, struggle to learn from evidence that contradicts AI suggestions

Vista sencilla de ítem
dc.contributor.authorViñas Gómez, Aranzazu
dc.contributor.authorBlanco Bregón, Fernando
dc.contributor.authorMatute, Helena
dc.date.accessioned2026-08-04T08:33:30Z
dc.date.available2026-08-04T08:33:30Z
dc.date.issued2026-07-09
dc.date.updated2026-08-04T08:33:30Z
dc.description.abstractDespite their widespread adoption, Artificial Intelligence-based Patient Classification Systems sometimes rely on incorrect, outdated, or incomplete data, which can lead to inaccurate outputs. Nevertheless, health professionals are expected to override these errors, at least when they have access to critical information. To test this, we conducted two experiments in which professional physicians interacted with an Artificial Intelligence system that incorrectly classified fictitious patients as either highly or lowly sensitive to a treatment. The physicians administered the treatment to a series of fictitious patients and received feedback that was useful for learning that the patient classification was incorrect and that all patients were equally sensitive to the treatment. We ran two experiments: in Experiment 1, the medicine showed medium effectiveness for both types of patients, while in Experiment 2, the treatment was completely ineffective for both types of patients. The results showed that, in the two experiments, physicians generally trusted the AI-based patient classification and struggled to learn from the evidence. Furthermore, in Experiment 2, they failed to realize that the treatment was ineffective. Our findings have important implications for healthcare professionals and patients, underscoring the need to critically evaluate Patient Classification Systems.en
dc.description.sponsorshipSupport for this research was provided by Grant PID2021-126320NB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF A way of making Europe, as well asGrant IT1696-22 funded by the Basque Government. A.V. was supported by Fellowship FPU20/01009 funded by MICIUen
dc.identifier.citationVinas, A., Blanco, F., & Matute, H. (2026). Doctors vs. algorithms: physicians, too, struggle to learn from evidence that contradicts AI suggestions. PLOS Digital Health, 5(7). https://doi.org/10.1371/JOURNAL.PDIG.0001490
dc.identifier.doi10.1371/JOURNAL.PDIG.0001490
dc.identifier.eissn2767-3170
dc.identifier.urihttps://hdl.handle.net/20.500.14454/6462
dc.language.isoeng
dc.publisherPublic Library of Science
dc.rights© 2026 Vinas et al.
dc.titleDoctors vs. algorithms: physicians, too, struggle to learn from evidence that contradicts AI suggestionsen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue7
oaire.citation.titlePLOS Digital Health
oaire.citation.volume5
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
vinas_doctors_2026.pdf
Tamaño:
846.33 KB
Formato:
Adobe Portable Document Format
Descargar
Colecciones
Artículos