RTEX: A novel framework for ranking, tagging, and explanatory diagnostic captioning of radiography exams | Zendy

Vasiliki Kougia | Zendy; John Pavlopoulos | Zendy; Panagiotis Papapetrou | Zendy; Max Gordon | Zendy

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RTEX: A novel framework for ranking, tagging, and explanatory diagnostic captioning of radiography exams

Author(s) -

Vasiliki Kougia,

John Pavlopoulos,

Panagiotis Papapetrou,

Max Gordon

Publication year - 2021

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocab046

Subject(s) - closed captioning , ranking (information retrieval) , computer science , natural language processing , workflow , learning to rank , artificial intelligence , correctness , information retrieval , abnormality , component (thermodynamics) , machine learning , medicine , image (mathematics) , physics , database , psychiatry , thermodynamics , programming language

The study sought to assist practitioners in identifying and prioritizing radiography exams that are more likely to contain abnormalities, and provide them with a diagnosis in order to manage heavy workload more efficiently (eg, during a pandemic) or avoid mistakes due to tiredness.

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