Cargando…
Examining explainable clinical decision support systems with think aloud protocols
Machine learning tools are increasingly used to improve the quality of care and the soundness of a treatment plan. Explainable AI (XAI) helps users in understanding the inner mechanisms of opaque machine learning models and is a driver of trust and adoption. Explanation methods for black-box models...
Autores principales: | Anjara, Sabrina G., Janik, Adrianna, Dunford-Stenger, Amy, Mc Kenzie, Kenneth, Collazo-Lorduy, Ana, Torrente, Maria, Costabello, Luca, Provencio, Mariano |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10501571/ https://www.ncbi.nlm.nih.gov/pubmed/37708135 http://dx.doi.org/10.1371/journal.pone.0291443 |
Ejemplares similares
-
Impact of Think-Aloud on Eye-Tracking: A Comparison of Concurrent and Retrospective Think-Aloud for Research on Decision-Making in the Game Environment
por: Prokop, Michal, et al.
Publicado: (2020) -
Examination of the suitability of collecting in event cognitive processes using Think Aloud protocol in golf
por: Whitehead, Amy E., et al.
Publicado: (2015) -
Data, AI and us : think aloud
por: Lovis, Christian
Publicado: (2023) -
Clinical decision-making and adaptive expertise in residency: a think-aloud study
por: Gamborg, Maria Louise, et al.
Publicado: (2023) -
Examining response process validity of script concordance testing: a think-aloud approach
por: Wan, Michael Siu Hong, et al.
Publicado: (2020)