Cited by 162Artificial Intelligence in Healthcare and EducationGenomics and Rare DiseasesMachine Learning in Healthcare

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Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

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Brasil, Sandra, Pascoal, Carlota, Francisco, Rita, dos Reis Ferreira, Vanessa, A. Videira, Paula, Valadão, Gonçalo·2019·Genes

DOI 10.3390/genes10120978PMC PMC6947640

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Abstract

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.

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Authors: Brasil, S., Pascoal, C., Francisco, R., dos Reis Ferreira, V., A. Videira, P., & Valadão, G.
Affiliations: Universidade Nova de Lisboa (PT)
Portuguese Association for CDG, 2820-381 Lisboa, Portugal
Universidade Politécnica de Lisboa (PT)
ORCID: Brasil, Sandra
Pascoal, Carlota
Francisco, Rita
Valadão, Gonçalo
Venue: Genes
Publisher: MDPI AG
Published: 2019-11-27
Volume: 10
Issue: 12
Pages: 978
ISSN: 2073-4425 (ISSN-L)
DOI: 10.3390/genes10120978
PMC: PMC6947640
OpenAlex: W2989824398
Cited by: 160
References: 102
Citations by year: 2026: 17 · 2025: 41 · 2024: 28 · 2023: 22 · 2022: 21 · 2021: 20 · 2020: 11
FWCI: 0.64
Type: journal article
Language: en
Open access: gold
dx.doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Genes (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
Funders: Fundação para a Ciência e a Tecnologia (SFRH/BD/138647/2018, SFRH/BD/124326/2016)
Topics: Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords: artificial intelligence, rare diseases, deep learning, congenital disorders of glycosylation, multi-omics integration, patient registry, drug discovery, diagnosis, clinical trials, translational medicine
MeSH terms: Deep Learning, Clinical Trials as Topic, Humans, Rare Diseases, Drug Discovery, Precision Medicine

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