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

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 566 · see citing works

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 482 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants

Alirezaie, N., Kernohan, K. D., Hartley, T., Majewski, J., & Hocking, T. D. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.08.005 · Cited by 271 · see citing works

Congenital disorders of glycosylation (CDG): Quo vadis?

Péanne, R., de Lonlay, P., Foulquier, F., Kornak, U., Lefeber, D. J., Morava, É., Pérez, B., Séta, N., Thiel, C., SCHAFTINGEN, E. V., et al. (2017) · European Journal of Medical Genetics · doi:10.1016/j.ejmg.2017.10.012 · Cited by 265 · see citing works

Prediction of glycosylation sites using random forests

Hamby, S. E., & Hirst, J. D. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-500 · Cited by 236 · see citing works

Perspectives on Glycosylation and Its Congenital Disorders

Ng, B. G., & Freeze, H. H. (2018) · Trends in Genetics · doi:10.1016/j.tig.2018.03.002 · Cited by 232 · see citing works

The Application of Unsupervised Clustering Methods to Alzheimer’s Disease

Alashwal, H., Halaby, M. E., Crouse, J. J., ABDALLA, A. S., & Moustafa, A. A. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00031 · Cited by 220 · see citing works

Neurology of inherited glycosylation disorders

Freeze, H. H., Eklund, E. A., Ng, B. G., & Patterson, M. C. (2012) · The Lancet Neurology · doi:10.1016/s1474-4422(12)70040-6 · Cited by 202 · see citing works

The Challenge of Rare Diseases

Stoller, J. K. (2018) · CHEST Journal · doi:10.1016/j.chest.2017.12.018 · Cited by 198 · see citing works

Genome-wide association identifies diverse causes of common variable immunodeficiency

Orange, J. S., Glessner, J., Resnick, E. S., Sullivan, K. E., Lucas, M., Ferry, B. L., Kim, C. E., Hou, C., Wang, F., Chiavacci, R. M., et al. (2011) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2011.02.039 · Cited by 190 · see citing works

A clinician friendly data warehouse oriented toward narrative reports: Dr. Warehouse

Garcelon, N., Neuraz, A., Salomon, R., Faour, H., Benoît, V., Delapalme, A., Münnich, A., Burgun, A., & Rance, B. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.02.019 · Cited by 170 · see citing works

What is new in CDG?

Jaeken, J., & Péanne, R. (2017) · Journal of Inherited Metabolic Disease · doi:10.1007/s10545-017-0050-6 · Cited by 170 · see citing works

Glycosylation site prediction using ensembles of Support Vector Machine classifiers

Caragea, C., Sinapov, J., Silvescu, A., Dobbs, D. L., & Honavar, V. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-438 · Cited by 168 · see citing works

From scientific discovery to treatments for rare diseases – the view from the National Center for Advancing Translational Sciences – Office of Rare Diseases Research

Kaufmann, P., Pariser, A., & Austin, C. P. (2018) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-018-0936-x · Cited by 160 · see citing works

Looking beyond the hype: applied AI and machine learning in translational medicine

Toh, T. S., Dondelinger, F., & Wang, D. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.08.027 · Cited by 159 · see citing works

Estimation of Survival Distributions of Treatment Policies in Two‐Stage Randomization Designs in Clinical Trials

Lunceford, J. K., Davidian, M., & Tsiatis, A. A. (2002) · Biometrics · doi:10.1111/j.0006-341x.2002.00048.x · Cited by 145 · see citing works

Phen-Gen: combining phenotype and genotype to analyze rare disorders

Javed, A., Agrawal, S., & Ng, P. C. (2014) · Nature Methods · doi:10.1038/nmeth.3046 · Cited by 143 · see citing works

Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study

Ronicke, S., Hirsch, M. C., Türk, E., Larionov, K., Tientcheu, D., & Wagner, A. D. (2019) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-019-1040-6 · Cited by 134 · see citing works

Recommendations for the design of small population clinical trials

Day, S. J., Jonker, A. H., Lau, L. P. L., Hilgers, R.-D., Irony, I., Larsson, K., Roes, K. C. B., & Stallard, N. (2018) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-018-0931-2 · Cited by 133 · see citing works

The challenge of CDG diagnosis

Francisco, R., Marques‐da‐Silva, D., Brasil, S., Pascoal, C., Ferreira, V. D. R., Morava, É., & Jaeken, J. (2018) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2018.11.003 · Cited by 132 · see citing works

Challenges in Research and Health Technology Assessment of Rare Disease Technologies: Report of the ISPOR Rare Disease Special Interest Group

Nestler-Parr, S., Korchagina, D., Toumi, M., Pashos, C. L., Blanchette, C., Molsen, E., Morel, T., Simoens, S., Kaló, Z., Gatermann, R., et al. (2018) · Value in Health · doi:10.1016/j.jval.2018.03.004 · Cited by 131 · see citing works

MultiPLIER: A Transfer Learning Framework for Transcriptomics Reveals Systemic Features of Rare Disease

Taroni, J., Grayson, P. C., Hu, Q., Eddy, S. F., Kretzler, M., Merkel, P. A., & Greene, C. S. (2019) · Cell Systems · doi:10.1016/j.cels.2019.04.003 · Cited by 120 · see citing works

Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas

van IJzendoorn, D. G. P., Szuhai, K., Bruijn, I. H. B., Kostine, M., Kuijjer, M. L., & Bovée, J. V. M. G. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006826 · Cited by 111 · see citing works

Xrare: a machine learning method jointly modeling phenotypes and genetic evidence for rare disease diagnosis

Li, Q., Zhao, K., Bustamante, C. D., Ma, X., & Wong, W. H. (2019) · Genetics in Medicine · doi:10.1038/s41436-019-0439-8 · Cited by 106 · see citing works

Maintaining order: COG complex controls Golgi trafficking, processing, and sorting

Blackburn, J. B., D'Souza, Z. C., & Lupashin, V. V. (2019) · FEBS Letters · doi:10.1002/1873-3468.13570 · Cited by 106 · see citing works

Document-level attention-based BiLSTM-CRF incorporating disease dictionary for disease named entity recognition

Xu, K., Yang, Z., Kang, P., Wang, Q., & Liu, W. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.04.002 · Cited by 104 · see citing works

Analysis of the human diseasome using phenotype similarity between common, genetic and infectious diseases

Hoehndorf, R., Schofield, P. N., & Gkoutos, G. V. (2015) · Scientific Reports · doi:10.1038/srep10888 · Cited by 100 · see citing works

CDG Therapies: From Bench to Bedside

Brasil, S., Pascoal, C., Francisco, R., Marques‐da‐Silva, D., Andreotti, G., Videira, P. A., Morava, É., Jaeken, J., & Ferreira, V. D. R. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19051304 · Cited by 100 · see citing works

Predicting disease-causing variant combinations

Papadimitriou, S., Gazzo, A. M., Versbraegen, N., Nachtegael, C., Aerts, J. A., Moreau, Y., Dooren, S. V., Nowé, A., Smits, G., & Lenaerts, T. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1815601116 · Cited by 100 · see citing works

Identification of deleterious synonymous variants in human genomes

Buske, O. J., Manickaraj, A. K., Mital, S. R., Ray, P. N., & Brudno, M. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt308 · Cited by 94 · see citing works

The International Rare Diseases Research Consortium: Policies and Guidelines to maximize impact

Lochmüller, H., Torrent-Farnell, J., Cam, Y. L., Jonker, A. H., Lau, L. P. L., Baynam, G. S., Kaufmann, P., Dawkins, H. J. S., Lasko, P. F., Austin, C. P., et al. (2017) · European Journal of Human Genetics · doi:10.1038/s41431-017-0008-z · Cited by 93 · see citing works

Recognition of the Cornelia de Lange syndrome phenotype with facial dysmorphology novel analysis

Basel‐Vanagaite, L., Wolf, L., Orin, M. J., Larizza, L., Gervasini, C., Krantz, I. D., & Deardoff, M. A. (2015) · Clinical Genetics · doi:10.1111/cge.12716 · Cited by 89 · see citing works

Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays

Smith, K., Piccinini, F., Balassa, T., Koós, K., Danka, T., Azizpour, H., & Horváth, P. (2018) · Cell Systems · doi:10.1016/j.cels.2018.06.001 · Cited by 87 · see citing works

PEDIA: prioritization of exome data by image analysis

Hsieh, T., Mensah, M. A., Pantel, J. T., Aguilar, D., Bar, O., Bayat, A., Becerra-Solano, L. E., Bentzen, H. B., Biskup, S., Borisov, O., et al. (2019) · Genetics in Medicine · doi:10.1038/s41436-019-0566-2 · Cited by 82 · see citing works

DeepPVP: phenotype-based prioritization of causative variants using deep learning

Boudellioua, I., Kulmanov, M., Schofield, P. N., Gkoutos, G. V., & Hoehndorf, R. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2633-8 · Cited by 79 · see citing works

A pharmaco-metabolomics approach in a clinical trial of ALS: Identification of predictive markers of progression

Blasco, H., Patin, F., Descat, A., Garçon, G., Corcia, P., Gelé, P., Lenglet, T., Bede, P., Meininger, V., Devos, D., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0198116 · Cited by 74 · see citing works

Natural history and genotype‐phenotype correlations in 72 individuals with <i>SATB2</i> ‐associated syndrome

Zarate, Y. A., Smith‐Hicks, C. L., Greene, C., Abbott, M., Siu, V. M., Calhoun, A. R. U. L., Pandya, A., Li, C., Sellars, E. A., Kaylor, J., et al. (2018) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.38630 · Cited by 71 · see citing works

Mechanism of glucocerebrosidase activation and dysfunction in Gaucher disease unraveled by molecular dynamics and deep learning

Romero, R., Ramanathan, A., Yuen, T., Bhowmik, D., Mathew, M., Munshi, L. B., Javaid, S., Bloch, M., Lizneva, D., Rahimova, A., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1818411116 · Cited by 70 · see citing works

Involvement of Patient Organisations in Research and Development of Orphan Drugs for Rare Diseases in Europe

Mavris, M., & Cam, Y. L. (2012) · Molecular Syndromology · doi:10.1159/000342758 · Cited by 66 · see citing works

Stacked ensemble combined with fuzzy matching for biomedical named entity recognition of diseases

Bhasuran, B., Murugesan, G., Abdulkadhar, S., & Natarajan, J. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.09.009 · Cited by 63 · see citing works

Finding patients using similarity measures in a rare diseases-oriented clinical data warehouse: Dr. Warehouse and the needle in the needle stack

Garcelon, N., Neuraz, A., Benoît, V., Salomon, R., Kracker, S., Suárez, F., Bahi‐Buisson, N., Hadj-Rabia, S., Fischer, A., Münnich, A., et al. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.07.016 · Cited by 58 · see citing works

Phenotype-driven gene prioritization for rare diseases using graph convolution on heterogeneous networks

Rao, A., Saipradeep, V. G., Joseph, T., Kotte, S., Sivadasan, N., & Srinivasan, R. (2018) · BMC Medical Genomics · doi:10.1186/s12920-018-0372-8 · Cited by 57 · see citing works

DNA methylation profiling reliably distinguishes pulmonary enteric adenocarcinoma from metastatic colorectal cancer

Jurmeister, P. S., Schöler, A., Arnold, A., Klauschen, F., Lenze, D., Hummel, M., Schweizer, L., Bläker, H., Pfitzner, B. M., Mamlouk, S., et al. (2019) · Modern Pathology · doi:10.1038/s41379-019-0207-y · Cited by 56 · see citing works

Congenital Disorders of Glycosylation: Glycosylation Defects in Man and Biological Models for Their Study

Marquardt, T., & Freeze, H. H. (2001) · Biological Chemistry · doi:10.1515/bc.2001.024 · Cited by 54 · see citing works

isGPT: An optimized model to identify sub-Golgi protein types using SVM and Random Forest based feature selection

Rahman, M. S., Rahman, M. K., Kaykobad, M., & Rahman, M. S. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.11.003 · Cited by 53 · see citing works

Stroke-Like Episodes and Cerebellar Syndrome in Phosphomannomutase Deficiency (PMM2-CDG): Evidence for Hypoglycosylation-Driven Channelopathy

Izquierdo-Serra, M., Martínez‐Monseny, A. F., López, L., Carrillo-García, J., Edo, A., Ortigoza‐Escobar, J. D., García, Ó., Candela, R. C., Carrasco-Marina, M. L., Gutiérrez-Solana, L., et al. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19020619 · Cited by 53 · see citing works

CDG and immune response: From bedside to bench and back

Pascoal, C., Francisco, R., Ferro, T., Ferreira, V. D. R., Jaeken, J., & Videira, P. A. (2019) · Journal of Inherited Metabolic Disease · doi:10.1002/jimd.12126 · Cited by 52 · see citing works

A checklist for clinical trials in rare disease: obstacles and anticipatory actions—lessons learned from the FOR-DMD trial

Crow, R. A., Morehart, P. J., McDermott, M. P., Tawil, R., Martens, W. B., Herr, B. E., McColl, E., Wilkinson, J. S., Kirschner, J., King, W. M., et al. (2018) · Trials · doi:10.1186/s13063-018-2645-0 · Cited by 51 · see citing works

The Discovery of a LEMD2-Associated Nuclear Envelopathy with Early Progeroid Appearance Suggests Advanced Applications for AI-Driven Facial Phenotyping

Marbach, F., Rustad, C. F., Rieß, A., Đukić, D., Hsieh, T., Jobani, I., Prescott, T., Bevot, A., Erger, F., Houge, G., et al. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.02.021 · Cited by 50 · see citing works

Utilization of Electronic Medical Records and Biomedical Literature to Support the Diagnosis of Rare Diseases Using Data Fusion and Collaborative Filtering Approaches

Shen, F., Liu, S., Wang, Y., Wen, A., Wang, L., & Liu, H. (2018) · JMIR Medical Informatics · doi:10.2196/11301 · Cited by 49 · see citing works

RDAD: A Machine Learning System to Support Phenotype-Based Rare Disease Diagnosis

Jia, J., Wang, R., An, Z., Guo, Y., Ni, X., & Shi, T. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00587 · Cited by 49 · see citing works

Next generation phenotyping in Emanuel and Pallister‐Killian syndrome using computer‐aided facial dysmorphology analysis of 2D photos

Liehr, T., Acquarola, N., Pyle, K., St‐Pierre, S., Rinholm, M., Bar, O., Wilhelm, K., & Schreyer, I. (2017) · Clinical Genetics · doi:10.1111/cge.13087 · Cited by 48 · see citing works

In silico clinical trials for pediatric orphan diseases

Carlier, A., Vasilevich, A. S., Maréchal, M., de Boer, J., & Geris, L. (2018) · Scientific Reports · doi:10.1038/s41598-018-20737-y · Cited by 45 · see citing works

Linked Registries: Connecting Rare Diseases Patient Registries through a Semantic Web Layer

Sernadela, P., González-Castro, L., Carta, C., van der Horst, E., Lopes, P. H. D. M. B., Kaliyaperumal, R., Thompson, M., Thompson, R., Queralt-Rosiñach, N., Martín, E. L., et al. (2017) · BioMed Research International · doi:10.1155/2017/8327980 · Cited by 45 · see citing works

Towards automated clinical coding

Catling, F. J. R., Spithourakis, G., & Riedel, S. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.09.021 · Cited by 42 · see citing works

Personalized Medicine: What’s in it for Rare Diseases?

Halfmann, S. S. G., Mählmann, L., Leyens, L., Reumann, M., & Brand, A. M. (2017) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-67144-4_22 · Cited by 40 · see citing works

The Analysis of Variants in the General Population Reveals That PMM2 Is Extremely Tolerant to Missense Mutations and That Diagnosis of PMM2-CDG Can Benefit from the Identification of Modifiers

Citro, V., Cimmaruta, C., Monticelli, M., Riccio, G., Mele, B. H., Cubellis, M. V., & Andreotti, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19082218 · Cited by 40 · see citing works

Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis

Subirats, L., Reguera, N., Hernández, A. M. B., Gómez‐Zúñiga, B., Minguillón, J., & Armayones, M. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15091877 · Cited by 37 · see citing works

More than just sugars: Conserved oligomeric Golgi complex deficiency causes glycosylation‐independent cellular defects

Blackburn, J. B., Kudlyk, T. A., Pokrovskaya, I. D., & Lupashin, V. V. (2018) · Traffic · doi:10.1111/tra.12564 · Cited by 37 · see citing works

Deep Learning Model to Assess Cancer Risk on the Basis of a Breast MR Image Alone

Portnoi, T., Yala, A., Schuster, T., Barzilay, R., Dontchos, B. N., Lamb, L. R., & Lehman, C. D. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20813 · Cited by 37 · see citing works

Validation of a new multiple osteochondromas classification through Switching Neural Networks

Mordenti, M., Ferrari, E., Pedrini, E., Fabbri, N., Campanacci, L., Muselli, M., & Sangiorgi, L. (2013) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.35819 · Cited by 37 · see citing works

A Computable Phenotype Improves Cohort Ascertainment in a Pediatric Pulmonary Hypertension Registry

Geva, A., Gronsbell, J. L., Cai, T., Cai, T. A., Murphy, S. N., Lyons, J. C., Heinz, M. M., Natter, M. D., Patibandla, N., Bickel, J. P., et al. (2017) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2017.05.037 · Cited by 36 · see citing works

DeepNEU: cellular reprogramming comes of age – a machine learning platform with application to rare diseases research

Danter, W. R. (2019) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-018-0983-3 · Cited by 34 · see citing works

The Application of Biomarkers in Clinical Trials for Motor Neuron Disease

Ganesalingam, J., & Bowser, R. P. (2010) · Biomarkers in Medicine · doi:10.2217/bmm.09.71 · Cited by 33 · see citing works

Rare Disease Registries Classification and Characterization: A Data Mining Approach

Santoro, M. C., Coi, A., Paola, M. L. D., Bianucci, A. M., Gainotti, S., Mollo, E., Taruscio, D., Vittozzi, L., & Bianchi, F. (2015) · Public Health Genomics · doi:10.1159/000369993 · Cited by 32 · see citing works

Characteristics of clinical trials in rare vs. common diseases: A register-based Latvian study

Logviss, K., Krievins, D. K., & Purvina, S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0194494 · Cited by 31 · see citing works

Mucopolysaccharidosis type II detection by Naïve Bayes Classifier: An example of patient classification for a rare disease using electronic medical records from the Canadian Primary Care Sentinel Surveillance Network

Ehsani‐Moghaddam, B., Queenan, J. A., MacKenzie, J. J., & Birtwhistle, R. (2018) · PLoS ONE · doi:10.1371/journal.pone.0209018 · Cited by 30 · see citing works

A Computational Framework for Genome-wide Characterization of the Human Disease Landscape

Lee, Y.-S., Krishnan, A., Oughtred, R., Rust, J., Chang, C., Ryu, J., Kristensen, V. N., Dolinski, K. J., Theesfeld, C. L., & Troyanskaya, O. G. (2019) · Cell Systems · doi:10.1016/j.cels.2018.12.010 · Cited by 28 · see citing works

Detecting Acromegaly: Screening for Disease with a Morphable Model

Miller, E. G., Lu, Q., Paisley, A. N., Trainer, P. J., Blanz, V., Dedden, K., & Miller, R. E. (2006) · Lecture notes in computer science · doi:10.1007/11866763_61 · Cited by 28 · see citing works

Industrializing rare disease therapy discovery and development

Ekins, S. (2017) · Nature Biotechnology · doi:10.1038/nbt.3787 · Cited by 28 · see citing works

Design of Biomedical Robots for Phenotype Prediction Problems

deAndrés‐Galiana, E. J., Fernández‐Martínez, J. L., & Sonis, S. T. (2016) · Journal of Computational Biology · doi:10.1089/cmb.2016.0008 · Cited by 27 · see citing works

Adaptive control of epileptiform excitability in an in vitro model of limbic seizures

Panuccio, G., Guez, A., Vincent, R., Avoli, M., & Pineau, J. (2013) · Experimental Neurology · doi:10.1016/j.expneurol.2013.01.002 · Cited by 27 · see citing works

HHT diagnosis by Mid-infrared spectroscopy and artificial neural network analysis

Lux, A., Müller, R., Tulk, M., Olivieri, C., Zarrabeitia, R., Salonikios, T., & Wirnitzer, B. (2013) · Orphanet Journal of Rare Diseases · doi:10.1186/1750-1172-8-94 · Cited by 26 · see citing works

Rare disease knowledge enrichment through a data-driven approach

Shen, F., Zhao, Y., Wang, L., Rastegar-Mojarad, M., Wang, Y., Liu, S., & Liu, H. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0752-9 · Cited by 26 · see citing works

From gestalt to gene: early predictive dysmorphic features of PMM2-CDG

Martínez‐Monseny, A. F., Cuadras, D., Bolasell, M., Muchart, J., Arjona, C., Borregán, M., Algrabli, A., Montero, R., Artuch, R., Velázquez-Fragua, R., et al. (2018) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2018-105588 · Cited by 25 · see citing works

eDiVA—Classification and prioritization of pathogenic variants for clinical diagnostics

Bosio, M., Drechsel, O., Rahman, R., Muyas, F., Rabionet, R., Bezdan, D., Domènech, L., Hor, H., Schott, J., Munell, F., et al. (2019) · Human Mutation · doi:10.1002/humu.23772 · Cited by 24 · see citing works

In Silico Prediction of the Effects of Mutations in the Human Mevalonate Kinase Gene: Towards a Predictive Framework for Mevalonate Kinase Deficiency

Browne, C., & Timson, D. J. (2015) · Annals of Human Genetics · doi:10.1111/ahg.12126 · Cited by 23 · see citing works

Deep learning approach for survival prediction for patients with synovial sarcoma

Han, I., Kim, J. H., Park, H., Kim, H., & Seo, S. W. (2018) · Tumor Biology · doi:10.1177/1010428318799264 · Cited by 22 · see citing works

Sensitivity analysis of gene ranking methods in phenotype prediction

deAndrés‐Galiana, E. J., Fernández‐Martínez, J. L., & Sonis, S. T. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.10.012 · Cited by 22 · see citing works

Constructing a database for the relations between CNV and human genetic diseases via systematic text mining

Yang, X., Song, Z., Wu, C., Wang, W., Li, G., Zhang, W., Wu, L., & Lü, K. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2526-2 · Cited by 21 · see citing works

Mining the transcriptome for rare disease therapies: a comparison of the efficiencies of two data mining approaches and a targeted cell-based drug screen

Mears, A. J., Schock, S., Hadwen, J., Putos, S., Dyment, D. A., Boycott, K. M., & MacKenzie, A. (2017) · npj Genomic Medicine · doi:10.1038/s41525-017-0018-3 · Cited by 20 · see citing works

Orphan drug development: the increasing role of clinical pharmacology

AHMED, M. A., Okour, M., Brundage, R. C., & Kartha, R. V. (2019) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-019-09646-3 · Cited by 19 · see citing works

Detecting Acromegaly: Screening for Disease with a Morphable Model

Miller, E. G., Lu, Q., Paisley, A. N., Trainer, P. J., Blanz, V., Dedden, K., Miller, R. E., Larsen, R., Nielsen, M., & Jon Sporring, A. (2006) · Cited by 17 · see citing works

Inferring characteristic phenotypes via class association rule mining in the bone dysplasia domain

Paul, R., Groza, T., Hunter, J., & Zankl, A. (2013) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2013.12.001 · Cited by 14 · see citing works

Improving Young Stroke Prediction by Learning with Active Data Augmenter in a Large-Scale Electronic Medical Claims Database

Hung, C.-Y., Lin, C., & Lee, C.-C. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513479 · Cited by 13 · see citing works

Patient’s Experience in Pediatric Primary Immunodeficiency Disorders: Computerized Classification of Questionnaires

Mücke, U., Klemann, C., Baumann, U., Meyer‐Bahlburg, A., Kortum, X., Klawonn, F., Lechner, W. M., & Grigull, L. (2017) · Frontiers in Immunology · doi:10.3389/fimmu.2017.00384 · Cited by 11 · see citing works

CSAX: Characterizing Systematic Anomalies in eXpression Data

Noto, K., Majidi, S., Edlow, A. G., Wick, H. C., Bianchi, D. W., & Slonim, D. K. (2015) · Journal of Computational Biology · doi:10.1089/cmb.2014.0155 · Cited by 10 · see citing works

Impact of structural prior knowledge in SNV prediction: Towards causal variant finding in rare disease

Dehiya, V., Thomas, J., & Sael, L. (2018) · PLoS ONE · doi:10.1371/journal.pone.0204101 · Cited by 6 · see citing works

Application of density estimation algorithms in analyzing co-morbidities of migraine

Yang, M.-H., Yang, F., & Oyang, Y. (2013) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-013-0028-8 · Cited by 5 · see citing works

Important Considerations in the Initial Clinical Evaluation of the Dysmorphic Neonate

Smpokou, P., Lanpher, B. C., & Rosenbaum, K. N. (2015) · Advances in Neonatal Care · doi:10.1097/anc.0000000000000216 · Cited by 2 · see citing works

Cerebrospinal fluid metabolomics in West Syndrome: central role of the serine metabolic pathway

Lagrue, E., Hounoum, B. M., Rullier, C., Andres, C. R., Emond, P., Bocca, C., Castelnau, P., & Blasco, H. (2018) · Journal of Translational Science · doi:10.15761/jts.1000223 · Cited by 1 · see citing works

Congenital Adrenal Hyperplasia—Current Insights in Pathophysiology, Diagnostics, and Management

Claahsen - van der Grinten, H. L., Speiser, P. W., Ahmed, S. F., Arlt, W., Auchus, R. J., Falhammar, H., Flück, C. E., Guasti, L., Huebner, A., Kortmann, B. B. M., et al. (2022) · Endocrine Reviews · doi:10.1210/endrev/bnab016 · Cited by 556 · see citing works

Artificial intelligence in developing countries: The impact of generative artificial intelligence (AI) technologies for development

Mannuru, N. R., Shahriar, S., Teel, Z. A., Wang, T., Lund, B. D., Tijani, S., Pohboon, C. O., Agbaji, D. A., Alhassan, J., Galley, J., et al. (2023) · Information Development · doi:10.1177/02666669231200628 · Cited by 392 · see citing works

Drug Repurposing for Rare Diseases

Roessler, H. I., Knoers, N. V. A. M., van Haelst, M. M., & van Haaften, G. W. (2021) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2021.01.003 · Cited by 273 · see citing works

The use of machine learning in rare diseases: a scoping review

Schaefer, J., Lehne, M., Schepers, J., Prasser, F., & Thun, S. (2020) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-020-01424-6 · Cited by 213 · see citing works

Artificial intelligence in rare disease diagnosis and treatment

Wojtara, M. S., Rana, E., Rahman, T., Khanna, P., & Singh, H. (2023) · Clinical and Translational Science · doi:10.1111/cts.13619 · Cited by 174 · see citing works

Diagnosis support systems for rare diseases: a scoping review

Faviez, C., Chen, X., Garcelon, N., Neuraz, A., Knebelmann, B., Salomon, R., Lyonnet, S., Saunier, S., & Burgun, A. (2020) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-020-01374-z · Cited by 107 · see citing works

Establishing a second-generation artificial intelligence-based system for improving diagnosis, treatment, and monitoring of patients with rare diseases

Hurvitz, N., Azmanov, H., Kesler, A., & Ilan, Y. (2021) · European Journal of Human Genetics · doi:10.1038/s41431-021-00928-4 · Cited by 106 · see citing works

Opportunities and Challenges for Machine Learning in Rare Diseases

Decherchi, S., Pedrini, E., Mordenti, M., Cavalli, A., & Sangiorgi, L. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.747612 · Cited by 93 · see citing works

Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European Hospitals

Klumpp, M., Hintze, M., Immonen, M., Ródenas, F., Pilati, F., Aparicio-Martínez, F., Çelebi, D., Liebig, T., Jirstrand, M., Urbann, O., et al. (2021) · Healthcare · doi:10.3390/healthcare9080961 · Cited by 88 · see citing works

Clinicians’ Views on Using Artificial Intelligence in Healthcare: Opportunities, Challenges, and Beyond

Alanazi, A. (2023) · Cureus · doi:10.7759/cureus.45255 · Cited by 87 · see citing works

The Impact of Artificial Intelligence on Optimizing Diagnosis and Treatment Plans for Rare Genetic Disorders

Abdallah, S., Sharifa, M., Almadhoun, M. K. I. K., Khawar, M. M., Shaikh, U., Balabel, K. M., Saleh, I., Manzoor, A., Mandal, A. K., Ekomwereren, O., et al. (2023) · Cureus · doi:10.7759/cureus.46860 · Cited by 87 · see citing works

Drug targets for COVID-19 therapeutics: Ongoing global efforts

Saxena, A. (2020) · Journal of Biosciences · doi:10.1007/s12038-020-00067-w · Cited by 83 · see citing works

Knowledge, Perceptions and Attitude of Researchers Towards Using ChatGPT in Research

Abdelhafiz, A. S., Ali, A. M. M., Maaly, A. M., Ziady, H. H., Sultan, E. A., & Mahgoub, M. A. (2024) · Journal of Medical Systems · doi:10.1007/s10916-024-02044-4 · Cited by 76 · see citing works

Application of artificial intelligence in medical education: A review of benefits, challenges, and solutions

Zarei, M., Eftekhari Mamaghani, H., Abbasi, A., & Hosseini, M.-S. (2024) · Medicina Clínica Práctica · doi:10.1016/j.mcpsp.2023.100422 · Cited by 70 · see citing works

The Value of Smartwatches in the Health Care Sector for Monitoring, Nudging, and Predicting: Viewpoint on 25 Years of Research

Köhler, C., Bartschke, A., Fürstenau, D., Schaaf, T., & Salgado, E. (2024) · Journal of Medical Internet Research · doi:10.2196/58936 · Cited by 68 · see citing works

A systematic review on machine learning approaches in the diagnosis and prognosis of rare genetic diseases

Román-Naranjo, P., Parra‐Perez, A. M., & Lopez-Escamez, J. A. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104429 · Cited by 63 · see citing works

Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities

Hasani, N., Farhadi, F., Morris, M. A., Nikpanah, M., Rahmim, A., Xu, Y., Pariser, A., Collins, M. T., Summers, R. M., Jones, E. C., et al. (2021) · PET Clinics · doi:10.1016/j.cpet.2021.09.009 · Cited by 61 · see citing works

The case for open science: rare diseases

Rubinstein, Y., Robinson, P. N., Gahl, W. A., Avillach, P., Baynam, G. S., Cederroth, H., Goodwin, R. M., Groft, S. C., HANSSON, M. G., Harris, N. L., et al. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooaa030 · Cited by 58 · see citing works

The EuRRECa Project as a Model for Data Access and Governance Policies for Rare Disease Registries That Collect Clinical Outcomes

Ali, S., Bryce, J., Tan, L. E., Hiort, O., Pereira, A. M., van den Akker, E. L. T., Appelman‐Dijkstra, N. M., Bertherat, J. Y., Cools, M., Dekkers, O. M., et al. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17238743 · Cited by 54 · see citing works

Artificial intelligence in drug repurposing for rare diseases: a mini-review

Cortial, L., Montero, V., Tourlet, S., del Bano, J., & Blin, O. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1404338 · Cited by 52 · see citing works

A Comprehensive Analysis of Artificial Intelligence Techniques for the Prediction and Prognosis of Genetic Disorders Using Various Gene Disorders

Chaplot, N., Pandey, D., Kumar, Y., & Sisodia, P. S. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09904-1 · Cited by 51 · see citing works

Artificial intelligence-driven phenotyping of zebrafish psychoactive drug responses

Bozhko, D. V., Myrov, V. O., Kolchanova, S. M., Polovian, A. I., Galumov, G. K., Demin, K. A., Zabegalov, K. N., Strekalova, T., de Abreu, M. S., Petersen, E. V., et al. (2021) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2021.110405 · Cited by 50 · see citing works

Artificial Intelligence in Epigenetic Studies: Shedding Light on Rare Diseases

Brasil, S., Neves, C. J., Rijoff, T., Falcão, M., Valadão, G., Videira, P. A., & Ferreira, V. D. R. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.648012 · Cited by 47 · see citing works

Successes and challenges of artificial intelligence in cardiology

Vandenberk, B., Chew, D. S., Prasana, D., Gupta, S., & Exner, D. V. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1201392 · Cited by 46 · see citing works

Artificial intelligence in healthcare: a mastery

Mukherjee, J., Sharma, R., Dutta, P., & Bhunia, B. (2023) · Biotechnology and Genetic Engineering Reviews · doi:10.1080/02648725.2023.2196476 · Cited by 43 · see citing works

Applying artificial intelligence to rare diseases: a literature review highlighting lessons from Fabry disease

Germain, D. P., Gruson, D., Malcles, M., & Garcelon, N. (2025) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-025-03655-x · Cited by 38 · see citing works

Challenges in the Classification of Cardiac Arrhythmias and Ischemia Using End-to-End Deep Learning and the Electrocardiogram: A Systematic Review

Diaz, E. O., Mauricio, D., Maculan, N., & Godoy, G. V. U. (2026) · Diagnostics · doi:10.3390/diagnostics16010161 · Cited by 36 · see citing works

Integration of artificial intelligence and multi-omics in kidney diseases

Zhou, S., Zhong, X., & Duan, L. (2022) · Fundamental Research · doi:10.1016/j.fmre.2022.01.037 · Cited by 35 · see citing works

Advancing optical nanosensors with artificial intelligence: A powerful tool to identify disease-specific biomarkers in multi-omics profiling

Taha, B. A., Abdulrahm, Z. M., Addie, A. J., Haider, A. J., Alkawaz, A. N., Yaqoob, I. A. M., & Arsad, N. (2025) · Talanta · doi:10.1016/j.talanta.2025.127693 · Cited by 34 · see citing works

Rare diseases: a comprehensive literature review and future directions

Chaudhary, A., & Kumar, V. (2025) · Journal of Rare Diseases · doi:10.1007/s44162-025-00099-6 · Cited by 33 · see citing works

Beyond the Screen: The Impact of Generative Artificial Intelligence (AI) on Patient Learning and the Patient-Physician Relationship

Traylor, D., Kern, K. V., Anderson, E. E., & Henderson, R. (2025) · Cureus · doi:10.7759/cureus.76825 · Cited by 30 · see citing works

Identification of risk features for complication in Gaucher’s disease patients: a machine learning analysis of the Spanish registry of Gaucher disease

Andrade‐Campos, M., de Frutos, L. L., Cebolla, J. J., Serrano‐Gonzalo, I., Medrano-Engay, B., Roca‐Espiau, M., Gomez-Barrera, B., Pérez-Heredia, J., Íñiguez, D., & Giraldo, P. (2020) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-020-01520-7 · Cited by 28 · see citing works

Leveraging Artificial Intelligence to Achieve Sustainable Public Healthcare Services in Saudi Arabia: A Systematic Literature Review of Critical Success Factors

Kumar, R., Singh, A. K., Kassar, A. S. A., Humaida, M. I., Joshi, S., & Sharma, M. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.059152 · Cited by 27 · see citing works

ProtoMed: Prototypical networks with auxiliary regularization for few-shot medical image classification

Ouahab, A., & Ahmed, O. B. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105337 · Cited by 24 · see citing works

Systematic Review: Drug Repositioning for Congenital Disorders of Glycosylation (CDG)

Brasil, S., Allocca, M., Magrinho, S. C. M., Santos, I., Raposo, M., Francisco, R., Pascoal, C., Martins, T., Videira, P. A., Pereira, F., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23158725 · Cited by 22 · see citing works

The Role of Virtual Triage in Improving Clinician Experience and Satisfaction: A Narrative Review

Gellert, G. A., Rasławska-Socha, J., Marcjasz, N., Price, T. J., Heyduk, A., Młodawska, A., Kuszczyński, K., Jędruch, A., & Orzechowski, P. D. (2023) · Telemedicine Reports · doi:10.1089/tmr.2023.0020 · Cited by 22 · see citing works

Artificial Intelligence: A New Frontier in Rare Disease Early Diagnosis

Nishat, S. M. H., Tanweer, A. S., Alshamsi, B. A., Shaheen, M. H., Tanveer, A. S., Nishat, A., Harbat, Y. A., Alaboud, A., Almazrouei, M. A., & Ali-Mohamed, R. A. (2025) · Cureus · doi:10.7759/cureus.79487 · Cited by 21 · see citing works

Human and Machine Intelligence Together Drive Drug Repurposing in Rare Diseases

Challa, A. P., Zaleski, N. M., Jerome, R. N., Lavieri, R. R., Shirey-Rice, J. K., Barnado, A., Lindsell, C. J., Aronoff, D. M., Crofford, L. J., Harris, R. C., et al. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.707836 · Cited by 21 · see citing works

A Community-Based Participatory Framework to Co-Develop Patient Education Materials (PEMs) for Rare Diseases: A Model Transferable across Diseases

Falcão, M., Allocca, M., Rodrigues, A. S., Granjo, P., Francisco, R., Pascoal, C., Rossi, M. G., Marques‐da‐Silva, D., Magrinho, S. C. M., Jaeken, J., et al. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20020968 · Cited by 21 · see citing works

Federated learning for rare disease detection: a survey

Wang, J., & Ma, F. (2023) · Rare Disease and Orphan Drugs Journal · doi:10.20517/rdodj.2023.16 · Cited by 21 · see citing works

The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution

Özçelik, F., Dündar, M. S., Yildirim, A. B., HENEHAN, G. T. M., Vicente, O., Sánchez-Alcázar, J. A., Gökçe, N., Yildirim, D. T., Bingol, N. N., Plaseska‐Karanfilska, D., et al. (2024) · Functional & Integrative Genomics · doi:10.1007/s10142-024-01417-9 · Cited by 21 · see citing works

Scoping review and classification of deep learning in medical genetics

Hanchard, S. E. L., Dwyer, M. C., Liu, S., Hu, P., Tekendo‐Ngongang, C., Waikel, R. L., Duong, D., & Solomon, B. D. (2022) · Genetics in Medicine · doi:10.1016/j.gim.2022.04.025 · Cited by 20 · see citing works

Explainable Artificial Intelligence–A New Step towards the Trust in Medical Diagnosis with AI Frameworks: A Review

Deshpande, N. M., Gite, S., Pradhan, B., & Assiri, M. E. (2022) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2022.021225 · Cited by 20 · see citing works

Artificial Intelligence and Machine Learning for Inborn Errors of Immunity: Current State and Future Promise

Martinson, A. K., Chin, A. T., Butte, M. J., & Rider, N. L. (2024) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2024.08.012 · Cited by 20 · see citing works

The Use of Artificial Intelligence for Clinical Coding Automation: A Bibliometric Analysis

Ramalho, A. L. C., Souza, J., & Freitas, A. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-53036-5_30 · Cited by 19 · see citing works

Diagnostic and therapeutic approach of artificial intelligence in neuro-oncological diseases

Venkatesan, D., Elangovan, A., Babu, H. W. S., Pasha, M. T., Abraham, K. S., Satheeshkumar, J., Sivaprakash, P., Ayyadurai, N., Gopalakrishnan, A. V., Narayanasamy, A., et al. (2022) · Biosensors and Bioelectronics X · doi:10.1016/j.biosx.2022.100188 · Cited by 19 · see citing works

Natural history of rare diseases using natural language processing of narrative unstructured electronic health records: The example of Dravet syndrome

Barco, T. L., Garcelon, N., Neuraz, A., & Nabbout, R. (2023) · Epilepsia · doi:10.1111/epi.17855 · Cited by 19 · see citing works

A primer on artificial intelligence for the paediatric cardiologist

Gearhart, A. S., Gaffar, S., & Chang, A. C. (2020) · Cardiology in the Young · doi:10.1017/s1047951120001493 · Cited by 19 · see citing works

AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction

Elhanashi, A., Saponara, S., Zheng, Q., Almutairi, N., Singh, Y., Kuanar, S. P., Ali, F., Ünal, O., & Faghani, S. (2025) · Journal of Imaging · doi:10.3390/jimaging11050141 · Cited by 18 · see citing works

New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches

Setty, S. T., Scott‐Boyer, M., Cuppens, T., & Droit, A. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23126792 · Cited by 18 · see citing works

Advances in RNA therapeutics: Classes, innovations and clinical applications

Shahid, U. (2025) · The Journal of Precision Medicine Health and Disease · doi:10.1016/j.premed.2025.100016 · Cited by 17 · see citing works

Ethical and Practical Considerations of Artificial Intelligence in Pediatric Medicine: A Systematic Review

Abusamra, H. N. J., Ali, S. H. M., Elhussien, W. A. K., Mirghani, A. M. A., Ahmed, A. A. A., & Ibrahim, M. (2025) · Cureus · doi:10.7759/cureus.79024 · Cited by 17 · see citing works

Clinical Pharmacology in Drug Development for Rare Diseases in Neurology: Contributions and Opportunities

Abuasal, B., AHMED, M. A., Patel, P., Albusaysi, S., Sabarinath, S., Uppoor, R. S., & Mehta, M. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2501 · Cited by 17 · see citing works

Machine learning approaches for advanced detection of rare genetic disorders in whole-genome sequencing

Alzahrani, A. A., & Alharithi, F. S. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.08.056 · Cited by 16 · see citing works

Deep Learning-Based Microscopic Diagnosis of Odontogenic Keratocysts and Non-Keratocysts in Haematoxylin and Eosin-Stained Incisional Biopsies

Rao, R. S., Shivanna, D. B., Mahadevpur, K. S., Shivaramegowda, S. G., Prakash, S., Lakshminarayana, S., & Patil, S. (2021) · Diagnostics · doi:10.3390/diagnostics11122184 · Cited by 16 · see citing works

Artificial Intelligence in the Diagnosis of Pediatric Rare Diseases: From Real-World Data Toward a Personalized Medicine Approach

Ilić, N., & Sarajlija, A. (2025) · Journal of Personalized Medicine · doi:10.3390/jpm15090407 · Cited by 15 · see citing works

Omics and rare diseases: challenges, applications, and future perspectives

Braconi, D., Nadwa, H., Bernardini, G., & Santucci, A. (2025) · Expert Review of Proteomics · doi:10.1080/14789450.2025.2468300 · Cited by 15 · see citing works

The AI revolution: how multimodal intelligence will reshape the oncology ecosystem

Dellamonica, D., Ruau, D., Griffiths, B., Rossi, G., Li, B. T., Razavi, P., Maio, T. D., Gonzalez, T., Remorino, A., Reis-Filho, J. S., et al. (2025) · npj Artificial Intelligence · doi:10.1038/s44387-025-00044-4 · Cited by 14 · see citing works

Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment

Yuan, S., Guo, L., & Xu, F. (2025) · International Urology and Nephrology · doi:10.1007/s11255-025-04878-4 · Cited by 12 · see citing works

AΙ-Driven Drug Repurposing: Applications and Challenges

Keramida, P., Syrigos, N. K., Kouvela, M., Poulakou, G., Charpidou, A. G., & Fiste, O. (2025) · Medicines · doi:10.3390/medicines12040028 · Cited by 12 · see citing works

Applying Neural Networks on Biometric Datasets for Screening Speech and Language Deficiencies in Child Communication

Toki, E. I., Tatsis, G., Tatsis, V., Plachouras, K., Pange, J., & Tsoulos, I. G. (2023) · Mathematics · doi:10.3390/math11071643 · Cited by 12 · see citing works

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) · BMC Bioinformatics · doi:10.1186/s12859-024-05693-x · Cited by 11 · see citing works

Mapping the diagnostic odyssey of congenital disorders of glycosylation (CDG): insights from the community

Granjo, P., Pascoal, C., Gallego, D., Francisco, R., Jaeken, J., Moors, T. L., Edmondson, A. C., Kantautas, K. A., Serrano, M., Videira, P. A., et al. (2024) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-024-03389-2 · Cited by 11 · see citing works

The Artificial Intelligence-Assisted Diagnosis of Skeletal Dysplasias in Pediatric Patients: A Comparative Benchmark Study of Large Language Models and a Clinical Expert Group

Ilić, N., Marić, N., Cvetković, D., Bogosavljević, M., Bukara-Radujković, G., Krstić, J., Paunović, Z., Begović, N. N., Zarić, S. P., Todorovic, S., et al. (2025) · Genes · doi:10.3390/genes16070762 · Cited by 10 · see citing works

Can artificial intelligence save medical genetics?

Solomon, B. D. (2021) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.62538 · Cited by 10 · see citing works

Beyond sialylation: Exploring the multifaceted role of GNE in GNE myopathy

Pereira, B. L., Barbosa, M., Granjo, P., Lochmüller, H., & Videira, P. A. (2025) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2025.109075 · Cited by 9 · see citing works

RDDL: A systematic ensemble pipeline tool that streamlines balancing training schemes to reduce the effects of data imbalance in rare-disease-related deep-learning applications

Yang, T.-H., Liao, Z.-Y., Yu, Y.-H., & Hsia, M. (2023) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2023.107929 · Cited by 9 · see citing works

Artificial Intelligence in the Genetic Diagnosis of Rare Disease

James, K. N., Phadke, S. S., Wong, T. C., & Chowdhury, S. (2023) · Clinics in Laboratory Medicine · doi:10.1016/j.cll.2022.09.023 · Cited by 9 · see citing works

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) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-022-02460-0 · Cited by 9 · see citing works

Artificial Intelligence and Machine Learning in Tropical Disease Management

Ogwu, M. C., & Izah, S. C. (2025) · Health information science · doi:10.1007/978-3-031-82622-1_7 · Cited by 8 · see citing works

Artificial Intelligence in Genetics

Sanders, M. A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-12128-9.00006-1 · Cited by 7 · see citing works

Exploring the Intersection of AI, Healthcare, and Environmental Sustainability: Future Trends and Challenges

Kumar, A. A., Suryadevara, R., Sowmyya, T., & Chanal, G. B. (2024) · Approaches to global sustainability, markets, and governance · doi:10.1007/978-981-97-9555-0_2 · Cited by 7 · see citing works

Development of an integrated and comprehensive clinical trial process management system

Shen, L., Zhai, Y., Pan, A., Zhao, Q., Zhou, M., & Liu, J. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02158-8 · Cited by 7 · see citing works

Challenges and Future Opportunities in Rare Genetic Disorders: A Comprehensive Review

Madhu, N. R., Biswas, G., Paul, S., Adhikari, S., Sarkar, B., Rafeeq, M. M., & Umair, M. (2024) · Springer eBooks · doi:10.1007/978-981-99-9323-9_9 · Cited by 7 · see citing works

Artificial Intelligence in drug discovery and development: current landscape, challenges, and future perspectives

Seyhan, A. A., & Carini, C. (2026) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2026.105461 · Cited by 6 · see citing works

Navigating the uncommon: challenges in applying evidence-based medicine to rare diseases and the prospects of artificial intelligence solutions

Rennie, O. (2024) · Medicine Health Care and Philosophy · doi:10.1007/s11019-024-10206-x · Cited by 6 · see citing works

Artificial intelligence in human genomics and biomedicine

Heil, R., Heyen, N. B., Baumann, M., Hüsing, B., Bachlechner, D., Schmoch, U., & König, H. (2021) · TATuP - Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis · doi:10.14512/tatup.30.3.30 · Cited by 6 · see citing works

Convergence of patient- and physician-reported outcomes in the French National Registry of Facioscapulohumeral Dystrophy

Sanson, B., Stalens, C., Guien, C., Villa, L., Eng, C., Rabarimeriarijaona, S., Bernard, R., Cintas, P., Solé, G., Tiffreau, V., et al. (2022) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-021-01793-6 · Cited by 6 · see citing works

Prediction of Tuberculosis Disease Progression with AI Analysis of Clinical Data

Pandey, S. K., Singh, K. U., Dingankar, R. S., Jadhav, K., Gupta, K., & Yadav, R. K. (2023) · International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) · doi:10.1109/icaiihi57871.2023.10489091 · Cited by 5 · see citing works

Diagnosis of odontogenic keratocysts and non-keratocysts using edge attention convolution neural network

Yakolli, N., Shivanna, D. B., Rao, R. S., Patil, S., Ronsivalle, V., Cicciù, M., & Minervini, G. (2024) · Minerva Dental and Oral Science · doi:10.23736/s2724-6329.24.04874-5 · Cited by 5 · see citing works

Bioinformatics and AI/ML approaches using multi-omics data to accelerate diagnosis and delivery of precision care for patients with rare diseases

Singh, K., Usman, S., Zeeshan, S., Yanamala, N. V. K., Nichols, M., Bhise, V. V., Bokhari, S., Sengupta, P. P., & Ahmed, Z. (2025) · Methods in cell biology · doi:10.1016/bs.mcb.2025.03.018 · Cited by 5 · see citing works

ChatGPT in Oral Pathology: Bright Promise or Diagnostic Mirage

Suárez, A., Freire, Y., García, V. D., Santamaría-Laorden, A., Pérez, J. O., Ajuria, M. S., Algar, J., & Carreras‐Presas, C. M. (2025) · Medicina · doi:10.3390/medicina61101744 · Cited by 5 · see citing works

A Self-attentive Meta-learning Approach for Image-Based Few-Shot Disease Detection

Ouahab, A., Ahmed, O. B., & Fernández-Maloigne, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16876-5_12 · Cited by 5 · see citing works

Is the diagnostic model based on convolutional neural network superior to pediatric radiologists in the ultrasonic diagnosis of biliary atresia?

Duan, X., Yang, L., Zhu, W., Yuan, H., Xiang-fen, X., Wen, H., Liu, W., & Chen, M. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2023.1308338 · Cited by 5 · see citing works

Artificial Intelligence in Infectious Skin Disease

Rokni, G. R., Gholizadeh, N., Babaei, M., & Das, K. (2024) · Dermatological Reviews · doi:10.1002/der2.241 · Cited by 5 · see citing works

Discovery of compounds inhibiting SARS-COV-2 multi-targets

Sasidharan, S., Sarkar, N., & Saudagar, P. (2022) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.2025149 · Cited by 5 · see citing works

The world is not enough – the value of increasing registry data in idiopathic pulmonary fibrosis

Moor, C. C., Kreuter, M., Luppi, F., & Wuyts, W. A. (2020) · Respiratory Research · doi:10.1186/s12931-020-01377-1 · Cited by 5 · see citing works

Editorial: Exploring the future of neurology: how AI is revolutionizing diagnoses, treatments, and beyond

Alkawadri, R., Hillis, J. M., & Asano, E. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1556510 · Cited by 5 · see citing works

Combined Use of Microwave Sensing Technologies and Artificial Intelligence for Biomedical Monitoring and Imaging

Martínez-Lozano, A., Bernal, A. B., Roy, L., Vicente-Samper, J. M., & Juan, C. G. (2026) · Biosensors · doi:10.3390/bios16010067 · Cited by 4 · see citing works

Comparative Analysis Of Various Machine Learning Algorithms For Biological Age Prediction

Vaidya, C. D. (2021) · i-manager s Journal on Computer Science · doi:10.26634/jcom.8.3.18048 · Cited by 4 · see citing works

Future prospective of AI in drug discovery

Bhowmick, M., Goswami, S., Bhowmick, P., Hait, S., Rath, D., & Yasmin, S. (2025) · Advances in pharmacology · doi:10.1016/bs.apha.2025.01.009 · Cited by 4 · see citing works

Comparison of machine learning models for mucopolysaccharidosis early diagnosis using UAE medical records

Shehhi, A. A., Alblooshi, H., Fadul, R., Tumzghi, N., Tenaiji, A. A., Harbi, M. A., & Al-Jasmi, F. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-13879-3 · Cited by 3 · see citing works

AI Powered Identification of Drug Targets and Pathways for Diagnosis and Treatment Planning: A Review

Sharma, A. (2023) · Nanomedicine & Nanotechnology Open Access · doi:10.23880/nnoa-16000232 · Cited by 3 · see citing works

Automatic Classification and Visualization of Text Data on Rare Diseases

Rei, L., Costa, J. P., & Draksler, T. Z. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14050545 · Cited by 3 · see citing works

Few-Shot Object Detection Based on Adaptive Attention Mechanism and Large-Margin Softmax

Huang, R. H., Lin, R., Dong, A., & Wang, Z. (2022) · AATCC Journal of Research · doi:10.1177/24723444221136626 · Cited by 3 · see citing works

Are Current Educational and Therapeutic Programs, Directed at Rare Disease Transplant Candidates and Recipients, Sufficient to Support Them on the Path From Diagnosis to Life After Allogenic Transplantation?—Recommendations for Member State Policymakers

Bartoszewicz, M., Prokop, P., Kosieradzki, M., & Fiedor, P. S. (2024) · Transplantation Proceedings · doi:10.1016/j.transproceed.2024.01.064 · Cited by 3 · see citing works

Target-based vs phenotypic drug discovery: opportunities and challenges with evidence-based application

Tauro, S. J., Dhokchawle, B. V., Nahar, D., Nadar, S., Thakor, E., & Mohite, P. B. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-23932-8.00002-9 · Cited by 3 · see citing works

Pediatrics 4.0: the Transformative Impacts of the Latest Industrial Revolution on Pediatrics

Onur, D., & Ozbakir, C. (2025) · Health Care Analysis · doi:10.1007/s10728-025-00536-z · Cited by 3 · see citing works

Künstliche Intelligenz und Gesundheit. Ethische, philosophische und sozialwissenschaftliche Explorationen

Sonar, A., & Weber, K. (2022) · Franz Steiner Verlag eBooks · doi:10.25162/9783515129770 · Cited by 3 · see citing works

Artificial intelligence in healthcare: possibilities of patent protection

Эриванцева, Т. Н., & Blokhina, Y. V. (2021) · FARMAKOEKONOMIKA Modern Pharmacoeconomics and Pharmacoepidemiology · doi:10.17749/2070-4909/farmakoekonomika.2021.063 · Cited by 3 · see citing works

Artificial intelligence in rare diseases: toward clinical impact

Amorim, A. M. B., Orzeł, U., Caniceiro, A. B., Rosário‐Ferreira, N., & Moreira, I. S. (2025) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2025.10.010 · Cited by 14 · see citing works

Navigating the uncharted territory of rare diseases: leveraging artificial intelligence to achieve groundbreaking advances, with a focus on Congenital Disorders of Glycosylation (CDG): A comprehensive analysis

Choubisa, V., & Soni, K. (2024) · International Journal of Science and Research Archive · doi:10.30574/ijsra.2024.12.2.1255

Artificial Intelligence Supports Research Progress in the Diagnosis and Treatment of Rare Diseases

Mengchun, G., Yuanshi, J., Wuren, M., Peng, L., Ye, J., Jifa, H., Ling, N., Wenzhao, S., & Shuyang, Z. (2022) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.12376/j.issn.2097-0501.2022.02.003

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

Unifying the odyssey: artificial intelligence for rare disease diagnosis and therapy

Ho, M., Žitnik, M., Azachi, R., Basu, S., Rajpurkar, P., & Sidlow, R. (2026) · Health and Technology · doi:10.1007/s12553-026-01057-y · Cited by 4 · see citing works