Artificial intelligence, machine learning, and deep learning in rhinology: a systematic review.

Antonio Mario BulfamanteFrancesco FerellaAustin Michael Miller Cecilia Rosso Carlotta Pipolo Emanuela Fuccillo Giovanni Felisati Alberto Maria Saibene

Published in: European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery (2022)

AI has vast potential in rhinology, but an inherent lack of accessible code sources does not allow for sharing results and advancing research without reconstructing models from scratch. While data pools do not necessarily represent a problem for model construction, presently available tools appear limited in allowing employment of raw clinical data, thus demanding immense interpretive work prior to the analytic process.

Keyphrases

artificial intelligence
big data
deep learning
machine learning
electronic health record
convolutional neural network
drinking water
social media
healthcare
mental illness
risk assessment
high density