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

The DOI matches its Crossref record; 99 of the 102 references it cites were found in a registry or an index.

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

Open access

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

DOI 10.3390/genes10120978PMC PMC6947640

Open this paper in CoChat — free account Open PDF

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.3390/genes10120978

Cites

99 of 99 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Deep learning
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Biopython: freely available python tools for computational molecular biology and bioinformatics
Cock, P. J. A., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B., et al. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp163 · Cited by 6,080
Predicting splicing from primary sequence with deep learning
Jaganathan, K., Kyriazopoulou Panagiotopoulou, S., McRae, J. F., Darbandi, S. F., Knowles, D., Li, Y. I., Kosmicki, J. A., Arbelaez, J., Cui, W., Schwartz, G. B., et al. (2019) · Cell · doi:10.1016/j.cell.2018.12.015 · Cited by 2,853
Identifying facial phenotypes of genetic disorders using deep learning
Gurovich, Y., Hanani, Y., Bar, O., Nadav, G., Fleischer, N., Gelbman, D., Basel-Salmon, L., Krawitz, P. M., Kamphausen, S. B., Zenker, M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0279-0 · Cited by 739
Identifying Mendelian disease genes with the Variant Effect Scoring Tool
Carter, H. K., Douville, C. B., Stenson, P. D., Cooper, D. N., & Karchin, R. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-s3-s3 · Cited by 593
Predicting the clinical impact of human mutation with deep neural networks
Sundaram, L., Gao, H., Padigepati, S. R., McRae, J. F., Li, Y., Kosmicki, J. A., Fritzilas, N., Hakenberg, J., Dutta, A., Shon, J. C., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0167-z · Cited by 505
Typical and atypical pathology in primary progressive aphasia variants
Spinelli, E. G., Mandelli, M. L., Miller, Z. A., Santos‐Santos, M. A., Wilson, S. M., Agosta, F., Grinberg, L. T., Huang, E. J., Trojanowski, J. Q., Meyer, M., et al. (2017) · Annals of Neurology · doi:10.1002/ana.24885 · Cited by 409
AI in Health: State of the Art, Challenges, and Future Directions
Wang, F., & Preininger, A. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677908 · Cited by 398
Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization
Pasa, F., Golkov, V., Pfeiffer, F., Cremers, D., & Pfeiffer, D. (2019) · Scientific Reports · doi:10.1038/s41598-019-42557-4 · Cited by 372

Show all 102 references

Research works printed but not linked: 2

Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer. Information Science and Statistics.
Conejar, 2014, A Medical Decision Support System (DSS) for Ubiquitous Healthcare Diagnosis System, Int. J. Softw. Eng. Its Appl., 8, 8

Sorted by citations

Items 101–162 of 162

TRANSFORMATIVE APPLICATIONS OF AI IN HEALTHCARE AND PHARMACEUTICAL INNOVATION
JASMINE, P., IBRAHIM, A., Siddiq, S., DHANURSHRA, A., NIRMALA, M., & BASHA, S. S. (2025) · International Journal of Applied Pharmaceutics · doi:10.22159/ijap.2025v17i3.52719 · Cited by 2
Evaluating AI Research Quality in Myasthenia Gravis: A Longitudinal Study Using the CLARITY Framework (2020–2024)
Marconi, L., Pirovano, E., & Cabitza, F. (2026) · Journal of Medical Systems · doi:10.1007/s10916-025-02335-4 · Cited by 2
Rare disease patients’ needs: an up-to-date analysis and future directions
Kamusheva, M., & Milushewa, P. (2021) · Pharmacia · doi:10.3897/pharmacia.68.e73240 · Cited by 2
The Future of AI in Revolutionizing Support for Disabled Persons
Malviya, R., & Rajput, S. (2025) · SpringerBriefs in modern perspectives on disability research. · doi:10.1007/978-981-96-6069-8_7 · Cited by 2
Whither Health Research
Patrinos, D., McDougall, R., Beauvais, M. J. S., & Knoppers, B. M. (2023) · The International Journal of Children s Rights · doi:10.1163/15718182-31040005 · Cited by 2
Artificial Intelligence in Medicine and Understanding its Potential for Newer Applications
Pathak, A., Athavale, H., Pathak, T. B., & Athavale, S. A. (2021) · International Journal of Current Research and Review · doi:10.31782/ijcrr.2021.131620 · Cited by 2
Potential and role of artificial intelligence in current medical healthcare
Hung, C.-M., Shi, H., Lee, P., Chang, C., Rau, K., Lee, H.-M., Tseng, C.-H., Pei, S., Tsai, K.-J., & Chiu, C. (2022) · WArtificial Intelligence in Cancer · doi:10.35713/aic.v3.i1.1 · Cited by 2
The Future of Artificial Intelligence and Artificial Intelligence in Primary Care
Cheema, A. M. (2025) · Primary Care Clinics in Office Practice · doi:10.1016/j.pop.2025.07.011 · Cited by 1
Nadir Hastalıklara Erişim Portalı ORPHANET ve OrphaCode: Yeni Nesil Hastalık Kodlama
Yücesan, E., Düzenli, Ö. F., & Özbek, U. (2025) · Acta Medica Nicomedia · doi:10.53446/actamednicomedia.1651794 · Cited by 1
DeepSeek R1 excels in diagnosing previously misdiagnosed cases
Hou, C., Zhang, H., Zhao, P., Lu, J., Geng, J., Li, H., Sun, X., He, T., Zhang, H., Tang, Y., et al. (2025) · Array · doi:10.1016/j.array.2025.100559 · Cited by 1
Using Artificial Intelligence Systems in Modern Neurobehavioral Research
Apukhtin, K. V., Zolotova, A. E., Leunenko, A. O., Perfilova, V. N., & Kalueff, A. V. (2025) · Journal of Evolutionary Biochemistry and Physiology · doi:10.1134/s0022093025060080 · Cited by 1
Artificial Intelligence in Oral Health
Lee, J. (2022) · MDPI eBooks · doi:10.3390/books978-3-0365-5143-2 · Cited by 1
Artificial Intelligence in Antibiotic Discovery Against Superbugs
de, D. C. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.58345 · Cited by 1
Rare diseases: What rheumatologists need to know?
do Nascimento, R. R. N. R., Piotto, D. G. P., Freire, E. A. M., Neves, F. D. S., Sztajnbok, F., Bica, B. E. R. G., Pinheiro, F. A. G., Kozu, K. T., Pereira, I. A., Azevedo, V. F., et al. (2024) · Revista Brasileira de Reumatologia · doi:10.1186/s42358-024-00407-6 · Cited by 1
A Comparison of Transfer Learning Metaphyseal Sign Diagnostic Models for Kashin-Beck Disease Based on X-rays of Children’s Hands
Li, G., Zhang, H., Chen, P., Song, Y., Zhang, Y., & Li, C. (2025) · Cureus · doi:10.7759/cureus.78235 · Cited by 1
Enhanced Genetic Disorder Detection using LLM
Arulmurugan, A., Eswar Sudhan, M., Shravan, A., & Mazik, R. I. M. E. (2025) · International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) · doi:10.1109/icssas66150.2025.11081107 · Cited by 1
Inteligência artificial e doenças raras: avanços recentes e implicações clínicas
Ribeiro, P. J. T., Freire, C. D. A., Aragão, A. M. A., de Araújo, F. L. S., Pedrosa, G. D. A., Aguiar, A. R. N., de Oliveira, C. E. P., Bonfim, B. V. B., de Araújo, L. A., Silveira, M. R., et al. (2024) · Brazilian Journal of Health Review · doi:10.34119/bjhrv7n5-021 · Cited by 1
Science in the Age of AI
Noble, A., Beasley, P. A., Dayan, P., Leonelli, S., Nolan, A., Quinlan, P., Sellen, A., Setchi, R., & Vere, K. (2024) · doi:10.1098/rs-policy.2026-31 · Cited by 29
The Use of Natural Language Processing for Market Orientation on Rare Diseases
Hölscher, M., & Buchkremer, R. (2020) · Advances in business information systems and analytics book series · doi:10.4018/978-1-7998-4240-8.ch010
Artificial Intelligence in the Genetic Diagnosis of Rare Disease
James, K. N., Phadke, S. S., Wong, T. C., & Chowdhury, S. (2020) · Advances in Molecular Pathology · doi:10.1016/j.yamp.2020.07.013
Machine learning and big data in pediatric laboratory medicine
Haymond, S., Julian, R. K., Gill, E. L., & Master, S. R. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-817962-8.00018-4
Integrated Life Cycle Management for Rare and Orphan Products
Kolochavina, M. (2021) · Springer eBooks · doi:10.1007/978-3-030-78605-2_23
Updating preventive measures of children disability
Fisenko, А. P., Zelinskaya, D. I., Terletskaya, R. N., & Vinyarskaya, I. V. (2021) · Rossiyskiy Vestnik Perinatologii i Pediatrii (Russian Bulletin of Perinatology and Pediatrics) · doi:10.21508/1027-4065-2021-66-5-15-20
Analysis of Strategy for Proof of Efficacy for Drugs Designated as Particularly High Medical Necessities in Japan
SHIRAI, T., & NAGAI, N. (2022) · Rinsho yakuri/Japanese Journal of Clinical Pharmacology and Therapeutics · doi:10.3999/jscpt.53.3_39
Artificial intelligence in the cardiology clinic
Recto, M., & Chang, A. C. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90534-3.00038-x
Artificial intelligence in medical genetics
Waikel, R. L., Duong, D., & Solomon, B. D. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15688-5.00007-3
Current Status and Future Prospects of Diagnosis and Treatment for Rare Diseases
桂, 俊. (2024) · Advances in Clinical Medicine · doi:10.12677/acm.2024.1441076
EVENT STUDY ON DRUG APPROVALS - IMPACT OF ORPHAN DRUG APPROVALS ON THE SHAREHOLDER VALUE OF PHARMACEUTICAL COMPANIES
ROLF, J.-L., Marotta, G., & Zureck, A. (2024) · International Journal of Social Sciences and Management Review · doi:10.37602/ijssmr.2024.7302
Would Artificial Intelligence Improve the Quality of Care of Patients With Rare Diseases?
Abukhadijah, H. J., & Nashwan, A. J. (2024) · Global Journal on Quality and Safety in Healthcare · doi:10.36401/jqsh-24-x3
Interrogantes bioéticos en enfermedades poco frecuentes: derechos humanos, algoritmos diagnósticos e inteligencia artificial
Gubert, I. C., Zavala, G. S., Vergès, C., López, G. L., Scrigni, A. V., Sotomayor, M. A., Castro, L. R., de Martínez, C. A. C., Estupiñan, E. M. B., Ledesma, F. M., et al. (2024) · Revista Iberoamericana de Bioética · doi:10.14422/rib.i26.y2024.004
Operation and Other Considerations
Ye, J., & Nie, L. (2024) · ICSA book series in statistics · doi:10.1007/978-3-031-60843-8_15
An optimized deep learning model based on transperineal ultrasound images for precision diagnosis of female stress urinary incontinence
Chen, K., Chen, Q., Nan, N., Sun, L., Ma, M., & Yu, S. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1564446
Healthcare in the AI Age: Considerations from an Operational Excellence Perspective
Ribera, J. (2025) · Springer eBooks · doi:10.1007/978-981-96-1437-0_9
Bayesian sequential decision-making for rare disease clinical trials
Gao, Y., Bai, J., & Chen, F. (2025) · Technology and Health Care · doi:10.1177/09287329251344056
Advanced Machine Learning Paradigms for Multi Omic Gene Symptom Association in Rare Disease Diagnosis
Shashikala, K. S., Vadlamudi, S., S, T. V., Govind, C., Bharath, B., & Inchal, C. (2025) · IEEE International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET) · doi:10.1109/acroset66531.2025.11281009
Redefining rare disease care in India: the expanding role of genetic counselling and technology
Srivathsa, A., Krishna, L., Bhat, D., Nisha, J., & Prashant, A. (2025) · BMJ connections. · doi:10.1136/bmjccgg-2025-000037
Additional file 13 of Stakeholders’ views on drug development: the congenital disorders of glycosylation community perspective
Monticelli, M., Francisco, R., Brasil, S., Marques‐da‐Silva, D., Rijoff, T., Pascoal, C., Jaeken, J., Videira, P. A., & Ferreira, V. D. R. (2022) · Figshare · doi:10.6084/m9.figshare.20408670
Self-citation
Additional file 12 of Stakeholders’ views on drug development: the congenital disorders of glycosylation community perspective
Monticelli, M., Francisco, R., Brasil, S., Marques‐da‐Silva, D., Rijoff, T., Pascoal, C., Jaeken, J., Videira, P. A., & Ferreira, V. D. R. (2022) · Figshare · doi:10.6084/m9.figshare.20408667
Self-citation
Additional file 10 of Stakeholders’ views on drug development: the congenital disorders of glycosylation community perspective
Monticelli, M., Francisco, R., Brasil, S., Marques‐da‐Silva, D., Rijoff, T., Pascoal, C., Jaeken, J., Videira, P. A., & Ferreira, V. D. R. (2022) · Figshare · doi:10.6084/m9.figshare.20408661
Self-citation
Additional file 9 of Stakeholders’ views on drug development: the congenital disorders of glycosylation community perspective
Monticelli, M., Francisco, R., Brasil, S., Marques‐da‐Silva, D., Rijoff, T., Pascoal, C., Jaeken, J., Videira, P. A., & Ferreira, V. D. R. (2022) · Figshare · doi:10.6084/m9.figshare.20408697
Self-citation
Additional file 11 of Stakeholders’ views on drug development: the congenital disorders of glycosylation community perspective
Monticelli, M., Francisco, R., Brasil, S., Marques‐da‐Silva, D., Rijoff, T., Pascoal, C., Jaeken, J., Videira, P. A., & Ferreira, V. D. R. (2022) · Figshare · doi:10.6084/m9.figshare.20408664
Self-citation
Additional file 1 of GPAD: a natural language processing-based application to extract the gene-disease association discovery information from OMIM
Rahit, K. M. T. H., Avramović, V., Chong, J. X., & Tarailo‐Graovac, M. (2024) · Figshare · doi:10.6084/m9.figshare.25302906
Artificial intelligence performance in image-based biliary atresia identification: a systematic review and meta-analysis
Halimi, A., Msherghi, A. A. A., Nounou, M. V., Elhabbasi, B. M., Abdulrahman, B., Abouelella, A., Shanab, A., Zakraoui, R., Farrag, A. A., & Elhadi, M. (2026) · European Journal of Radiology · doi:10.1016/j.ejrad.2025.112651
Изкуствен интелект и системи за клинична подкрепа при вземане на решения в диагностицирането на редки болести
Dimitrov, K. Y. (2026) · Редки болести и лекарства сираци · doi:10.36865/2025.v16i3.231
Artificial intelligence for orphan and rare diseases: a promising frontier in healthcare
Shukla, A. K. (2025) · Journal of medical pharmaceutical and allied sciences · doi:10.55522/jmpas.v14i6.6990
Geo-Temporal Analysis of Social Media Conversations on Laron Syndrome: Insights from Dynamic Topic Modelling
Tumzghi, N., & Shehhi, A. A. (2025) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom60686.2025.11342536
Rare Skin Disease Classification using Augmented Social Media Reddit Data
Alhammadi, E., Tumzghi, N., & Shehhi, A. A. (2025) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom60686.2025.11343226
RTCDB: A Multi-Omics Database Supporting Research and Discovery in Rare Tumor Oncology
Su, Y., Yang, B., Geng, A., Chen, Y., Zhang, Z., & Cui, F. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356395
Maximum likelihood estimation and rare events
Marwala, T. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36322-1.00026-2
Standardized prospective multidisciplinary follow-up of patients with surgical congenital malformations: A model for continuous data driven improvement of health care
van Streun, S. P., Roorda, D., van der Steeg, A. F. W., Gorter, R. R., Derikx, J. P. M., van Heurn, E. L. W., & Oosterlaan, J. (2026) · Journal of Pediatric Surgery · doi:10.1016/j.jpedsurg.2026.163040
Association of Rare Disease Diagnosis and Its Treatment Using Artificial Intelligence – A Review
Kamboj, A., Singh, A. K., Kumar, S., & Garg, V. K. (2025) · Global AI Summit - International Conference on Artificial Intelligence and Emerging Technology (AI Summit) · doi:10.1109/aisummit66170.2025.11411235
A Machine Learning-based System for Predicting Monogenic Genetic Disorders
D’Souza, S. M., Faisal, M., Karur, K., Zehra, N., Vinnakota, S., & Eswar, P. M. (2026) · International Conference on Image Processing and Capsule Networks (ICIPCN) · doi:10.1109/icipcn67432.2026.11438309
A diagnosis support system to identify rare disease cases in large-scale clinical databases : application to ciliopathies
Faviez, C. (2022) · theses.fr (ABES) · doi:10.70675/508a4baczcf58z41c4z973aze562382c21ea
MediVault: An Auditable and Secure Federated Learning System for Privacy-Preserving Healthcare Collaboration
Li, J., Adeel, U., & Akram, M. S. (2026) · Algorithms · doi:10.3390/a19060427
Repurposing for Small Population Needs
(2026) · Frontiers research topics · doi:10.3389/978-2-8325-8110-0
Self-adaptive forward-forward network for anomaly detection and medical image analysis
Müller, J. P., Baugh, M., & Kainz, B. (2026) · Frontiers in Radiology · doi:10.3389/fradi.2026.1771850
From Variant Interpretation to Biomarker Translation: Multi‐omics Integration in Inherited Neuromuscular Diseases
Zhang, S., Lang, X., & Liu, L. (2026) · Human Mutation · doi:10.1155/humu/9236120
Receiving information and determining research priorities for Lennox-Gastaut syndrome: A community-focused study of caregivers’ experiences and preferences
Muller, R., Chiu, L., Votoupal, M., Wojnaroski, M., Rosenman, M. B., Patel, A. D., Carroll, M., Leavens, K., Dixon‐Salazar, T., & Lam, S. K. (2026) · Epilepsy Research · doi:10.1016/j.eplepsyres.2026.107890
AI Platforms for Explainable and Trustworthy Clinical Research
Kashif, A., Khan, M. Z., Khursheed, H., Khalid, H., Ahmed, Z., Abid, F., Sulman, A., Sultana, S., Afzal, A., & Khan, B. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8904-2.ch006
Applying Artificial Intelligence to a Complex Diagnostic Challenge: A CNS Germ Cell Tumor Presenting as Insidious Memory Loss—A Systematic Evaluation of Large Language Models’ Reasoning Abilities across Multi-Stage Complex Clinical Workups
高, 同. (2026) · Advances in Clinical Medicine · doi:10.12677/acm.2026.1682938
MMAC-Net: A Multi-Modal Multi-Label Attention-Based Deep Learning Approach for Automated ICD-9 Coding of Rare Disease Admissions from Electronic Health Records
Ashrafi, A. F., Alhajj, R., & Rokne, J. G. (2026) · Applied Sciences · doi:10.3390/app16188962
AI-driven precision medicine for lysosomal storage disorders: Innovations in diagnosis and therapeutics
Ahmad, R. M., Ahmad, B. M., Mohamed, F. E., Khan, M. J., Al-Jasmi, F. A., & Ali, B. R. (2026) · Genes & Diseases · doi:10.1016/j.gendis.2026.102520

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks