Publicação

Artificial intelligence (AI) in rare diseases: Is the future brighter?

Ver documento

Detalhes bibliográficos
Resumo:The amount of data collected and managed in (bio)medicine is ever-increasing. Thus, there is a need to rapidly and efficiently collect, analyze, and characterize all this information. Artificial intelligence (AI), with an emphasis on deep learning, holds great promise in this area and is already being successfully applied to basic research, diagnosis, drug discovery, and clinical trials. Rare diseases (RDs), which are severely underrepresented in basic and clinical research, can particularly benefit from AI technologies. Of the more than 7000 RDs described worldwide, only 5% have a treatment. The ability of AI technologies to integrate and analyze data from different sources (e.g., multi-omics, patient registries, and so on) can be used to overcome RDs’ challenges (e.g., low diagnostic rates, reduced number of patients, geographical dispersion, and so on). Ultimately, RDs’ AI-mediated knowledge could significantly boost therapy development. Presently, there are AI approaches being used in RDs and this review aims to collect and summarize these advances. A section dedicated to congenital disorders of glycosylation (CDG), a particular group of orphan RDs that can serve as a potential study model for other common diseases and RDs, has also been included.
Autores principais:Brasil, Sandra
Outros Autores:Pascoal, Carlota; Francisco, Rita; Ferreira, Vanessa dos Reis; Videira, Paula A.; Valadão, Gonçalo
Assunto:Artificial intelligence Big data Congenital disorders of glycosylation Diagnosis Drug repurposing Machine learning Personalized medicine Rare diseases Genetics Genetics(clinical) SDG 3 - Good Health and Well-being
Ano:2019
País:Portugal
Tipo de documento:recensão
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL

Registos relacionados

Registos relacionados