Artificial Intelligence Transforms the Future of Health Care

Multiple imputation using chained equations: Issues and guidance for practice

White, I. R., Royston, P., & Wood, A. M. (2011) · Statistics in Medicine · doi:10.1002/sim.4067 · Cited by 9,792

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697

Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls

Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M., & Carpenter, J. R. (2009) · BMJ · doi:10.1136/bmj.b2393 · Cited by 7,299

Refining Clinical Risk Stratification for Predicting Stroke and Thromboembolism in Atrial Fibrillation Using a Novel Risk Factor-Based Approach

Lip, G. Y. H., Nieuwlaat, R., Pisters, R., Lane, D. A., & Crijns, H. J. G. M. (2010) · Chest · doi:10.1378/chest.09-1584 · Cited by 6,879

Artificial intelligence in healthcare: past, present and future

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837

Predicting the future - big data, machine learning, and clinical medicine

Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652

Machine Learning in Medicine

Deo, R. C. (2015) · Circulation · doi:10.1161/circulationaha.115.001593 · Cited by 3,466

Machine Learning Applications in Cancer Prognosis and Prediction

Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2014.11.005 · Cited by 3,338

Spironolactone for heart failure with preserved ejection fraction

Pitt, B., Pfeffer, M. A., Assmann, S. F., Boineau, R., Anand, I. S., Claggett, B., Clausell, N., Desai, A. S., Diaz, R., Fleg, J. L., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1313731 · Cited by 2,809

Machine learning for medical imaging

Erickson, B. J., Korfiatis, P., Akkus, Z., & Kline, T. L. (2017) · RadioGraphics · doi:10.1148/rg.2017160130 · Cited by 1,672

Applications of Machine Learning in Cancer Prediction and Prognosis

Cruz, J. A., & Wishart, D. S. (2006) · Cancer Informatics · doi:10.1177/117693510600200030 · Cited by 1,371

Pulmonary Hypertension in Heart Failure With Preserved Ejection Fraction

Lam, C. S. P., Roger, V. L., Rodeheffer, R. J., Borlaug, B. A., Enders, F., & Redfield, M. M. (2009) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2008.11.051 · Cited by 1,345

Introduction of the medical emergency team (MET) system: a cluster-randomised controlled trial

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A novel clinical risk prediction model for sudden cardiac death in hypertrophic cardiomyopathy (HCM risk-SCD)

O'Mahony, C., Jichi, F., Pavlou, M., Monserrat, L., Anastasakis, A., Rapezzi, C., Biagini, E., Gimeno, J. R., Limongelli, G., McKenna, W. J., et al. (2014) · European Heart Journal · doi:10.1093/eurheartj/eht439 · Cited by 1,251

Artificial Intelligence in Precision Cardiovascular Medicine

Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.03.571 · Cited by 1,126

Using recurrent neural network models for early detection of heart failure onset

Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw112 · Cited by 928

Applications of machine learning in cancer prediction and prognosis.

Cruz, J. A., & Wishart, D. S. (2007) · PubMed Central · Cited by 879

A community effort to assess and improve drug sensitivity prediction algorithms

Community, N. D., Costello, J. C., Heiser, L. M., Georgii, E., Gönen, M., Menden, M. P., Wang, N. J., Bansal, M., Ammad-ud-din, M., Hintsanen, P., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2877 · Cited by 782

Artificial intelligence in medical practice: the question to the answer?

Miller, D. D., & Brown, E. W. (2018) · The American Journal of Medicine · doi:10.1016/j.amjmed.2017.10.035 · Cited by 774

What Is Data Ethics?

Floridi, L., & Taddeo, M. (2016) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2016.0360 · Cited by 637

Pulmonary Hypertension in Heart Failure With Preserved Ejection Fraction

Guazzi, M., Vicenzi, M., Arena, R. A., & Guazzi, M. D. (2011) · Circulation · doi:10.1161/circulationaha.110.983866 · Cited by 556

IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research

Chen, Y., Elenee Argentinis, J., & Weber, G. (2016) · Clinical Therapeutics · doi:10.1016/j.clinthera.2015.12.001 · Cited by 466

Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration

Arbabshirani, M. R., Fornwalt, B. K., Mongelluzzo, G. J., Suever, J. D., Geise, B. D., Patel, A. A., & Moore, G. J. (2018) · npj Digital Medicine · doi:10.1038/s41746-017-0015-z · Cited by 443

A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection

Cruz-Roa, A. A., Arevalo Ovalle, J. E., Madabhushi, A., & González Osorio, F. A. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_50 · Cited by 403

Secondary Use of EHR: Data Quality Issues and Informatics Opportunities.

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2017 IEEE International Conference on Big Data (Big Data)

Jian-Yun Nie, Z. O., Nie, J.-Y., Zoran Obradovic, A., Suzumura, T., Ghosh, R., Nambiar, R., Wang, C., Zang, H., Baeza-Yates, R., Hu, X., et al. (2017) · doi:10.1109/bigdata41644.2017 · Cited by 302

Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation

Lehman, C. D., Yala, A., Schuster, T., Dontchos, B., Bahl, M., Swanson, K., & Barzilay, R. (2019) · Radiology · doi:10.1148/radiol.2018180694 · Cited by 294

Reproducible pharmacogenomic profiling of cancer cell line panels

Haverty, P. M., Lin, E., Tan, J., Yu, Y., Lam, B., Lianoglou, S., Neve, R. M., Martin, S. E., Settleman, J., Yauch, R. L., et al. (2016) · Nature · doi:10.1038/nature17987 · Cited by 279

Deep into the Brain: Artificial Intelligence in Stroke Imaging

Lee, E., Kim, Y., Kim, N., & Kang, D. (2017) · Journal of Stroke · doi:10.5853/jos.2017.02054 · Cited by 272

High-Definition Medicine

Torkamani, A., Andersen, K. G., Steinhubl, S. R., & Topol, E. J. (2017) · Cell · doi:10.1016/j.cell.2017.08.007 · Cited by 253

Early and innovative interventions for severe sepsis and septic shock: taking advantage of a window of opportunity

Rivers, E. (2005) · Canadian Medical Association Journal · doi:10.1503/cmaj.050632 · Cited by 177

Predicting Survival From Large Echocardiography and Electronic Health Record Datasets

Samad, M. D., Ulloa, A., Wehner, G. J., Jing, L., Hartzel, D. N., Good, C. W., Williams, B. A., Haggerty, C. M., & Fornwalt, B. K. (2018) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2018.04.026 · Cited by 173

Supporting Regularized Logistic Regression Privately and Efficiently

Li, W., Liu, H., Yang, P., & Xie, W. (2016) · PLoS ONE · doi:10.1371/journal.pone.0156479 · Cited by 163

Patient privacy in the era of big data

Kayaalp, M. (2018) · Balkan Medical Journal · doi:10.4274/balkanmedj.2017.0966 · Cited by 142

A Randomized Double-Blind Trial of Enalapril in Older Patients With Heart Failure and Preserved Ejection Fraction

Kitzman, D. W., Hundley, W. G., Brubaker, P. H., Morgan, T. M., Moore, J. B., Stewart, K. P., & Little, W. C. (2010) · Circulation Heart Failure · doi:10.1161/circheartfailure.109.898916 · Cited by 131

Novel Screening Tool for Stroke Using Artificial Neural Network

Abedi, V., Goyal, N., Tsivgoulis, G. K., Hosseinichimeh, N., Hontecillas, R., Bassaganya-Riera, J., Elijovich, L., Metter, J. E., Alexandrov, A. W., Liebeskind, D. S., et al. (2017) · Stroke · doi:10.1161/strokeaha.117.017033 · Cited by 124

Open source machine-learning algorithms for the prediction of optimal cancer drug therapies

Huang, C., Mezencev, R., McDonald, J. F., & Vannberg, F. O. (2017) · PLoS ONE · doi:10.1371/journal.pone.0186906 · Cited by 119

Machine learning, natural language programming, and electronic health records: The next step in the artificial intelligence journey?

Mehta, N., & Devarakonda, M. V. (2018) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2018.02.025 · Cited by 118

Heart Failure With Preserved Ejection Fraction

Udelson, J. E. (2011) · Circulation · doi:10.1161/circulationaha.111.071696 · Cited by 114

A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation

Smith, S. W., Walsh, B., Grauer, K., Wang, K., Rapin, J., Li, J., Fennell, W. H., & Taboulet, P. (2018) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2018.11.013 · Cited by 107

Decoding the Genomics of Abdominal Aortic Aneurysm

Li, J., Pan, C., Zhang, S., Spin, J. M., Deng, A. C., Leung, L. L. K., Dalman, R. L., Tsao, P. S., & Snyder, M. P. (2018) · Cell · doi:10.1016/j.cell.2018.07.021 · Cited by 95

Machine learning and genomics: precision medicine versus patient privacy

Azencott, C.-A. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2017.0350 · Cited by 89

Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder

Schnyer, D. M., Clasen, P. C., Gonzalez, C. E., & Beevers, C. G. (2017) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2017.03.003 · Cited by 72

Classification of short single-lead electrocardiograms (ECGs) for atrial fibrillation detection using piecewise linear spline and XGBoost

Chen, Y., Wang, X., Jung, Y., Abedi, V., Zand, R., Bikak, M., & Adibuzzaman, M. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aadf0f · Cited by 68

Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas

Kesorn, K., Ongruk, P., Chompoosri, J., Phumee, A., Thavara, U., Tawatsin, A., & Siriyasatien, P. (2015) · PLoS ONE · doi:10.1371/journal.pone.0125049 · Cited by 66

Identifying seizure onset zone from electrocorticographic recordings: A machine learning approach based on phase locking value

Elahian, B., Yeasin, M., Mudigoudar, B. D., Wheless, J. W., & Abbas Babajani-feremi, A. (2017) · Seizure · doi:10.1016/j.seizure.2017.07.010 · Cited by 66

Serendipity in anticancer drug discovery

Hargrave-Thomas, E. (2012) · World Journal of Clinical Oncology · doi:10.5306/wjco.v3.i1.1 · Cited by 49

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

SecureMA: protecting participant privacy in genetic association meta-analysis

Xie, W., Kantarcıoğlu, M., Bush, W. S., Crawford, D. C., Denny, J. C., Heatherly, R. D., & Malin, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu561 · Cited by 43

Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach

Kagawa, R., Kawazoe, Y., Ida, Y., Shinohara, E., Tanaka, K., Imai, T., & Ohe, K. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816681584 · Cited by 41

Ecological Niche Modeling for Filoviruses: A Risk Map for Ebola and Marburg Virus Disease Outbreaks in Uganda.

Nyakarahuka, L., Ayebare, S., Mosomtai, G., Kankya, C., Lutwama, J. J., Mwiine, F. N., & Skjerve, E. (2017) · Europe PMC (PubMed Central) · doi:10.1371/currents.outbreaks.07992a87522e1f229c7cb023270a2af1 · Cited by 38

Modeling and Simulation as a Tool to Bridge Efficacy and Safety Data in Special Populations

Harnisch, L. O., Shepard, T., Pons, G., & Pasqua, O. E. D. (2013) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1038/psp.2013.6 · Cited by 37

Modeling new immunoregulatory therapeutics as antimicrobial alternatives for treating Clostridium difficile infection

Leber, A. J., Hontecillas, R., Abedi, V., Tubau‐Juni, N., Zoccoli-Rodriguez, V., Stewart, C., & Bassaganya-Riera, J. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.05.003 · Cited by 36

St. Jude Children’s Research Hospital

Morrison, K. (2015) · doi:10.4135/9781483345758.n537 · Cited by 29

Optimizing drug development in oncology by clinical trial simulation: Why and how?

Gal, J., Milano, G. A., Ferrero, J.-­., Saâda-Bouzid, E., Viotti, J., Chabaud, S., Gougis, P., Tourneau, C. L., Schiappa, R., Paquet, A. C., et al. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx055 · Cited by 28

Machine learning and medicine: book review and commentary

Koprowski, R., & Foster, K. R. (2018) · BioMedical Engineering OnLine · doi:10.1186/s12938-018-0449-9 · Cited by 28

Supervised learning methods in modeling of CD4+ T cell heterogeneity

Lu, P., Abedi, V., Mei, Y., Hontecillas, R., Hoops, S., Carbo, A., & Bassaganya-Riera, J. (2015) · BioData Mining · doi:10.1186/s13040-015-0060-6 · Cited by 22

Computer-Aided Drug Discovery

Lu, P., Bevan, D. R., Leber, A. J., Hontecillas, R., Tubau‐Juni, N., & Bassaganya-Riera, J. (2018) · Springer eBooks · doi:10.1007/978-3-319-73238-1_2 · Cited by 16

Phase III Placebo-Controlled, Randomized Clinical Trial With Synthetic Crohn's Disease Patients to Evaluate Treatment Response

Abedi, V., Lu, P., Hontecillas, R., Verma, M., Vess, G., Philipson, C. W., Carbo, A., Leber, A. J., Juni, N. T., Hoops, S., et al. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-804203-8.00028-6 · Cited by 15

Development of Synthetic Patient Populations and In Silico Clinical Trials

Zand, R., Abedi, V., Hontecillas, R., Lu, P., Noorbakhsh-Sabet, N., Verma, M., Leber, A. J., Tubau‐Juni, N., & Bassaganya-Riera, J. (2018) · Springer eBooks · doi:10.1007/978-3-319-73238-1_5 · Cited by 15

Empirical study using network of semantically related associations in bridging the knowledge gap

Abedi, V., Yeasin, M., & Zand, R. (2014) · Journal of Translational Medicine · doi:10.1186/s12967-014-0324-9 · Cited by 14

Closing the loop — Finding lung cancer patients using NLP

Karunakaran, B., Misra, D., Marshall, K., Mathrawala, D., & Kethireddy, S. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258203 · Cited by 13

Temporal and Spatial Monitoring and Prediction of Epidemic Outbreaks

Zamiri, A., Yazdi, H. S., & Goli, S. A. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2338213 · Cited by 10

Accelerated Path to Cures

(2018) · Springer eBooks · doi:10.1007/978-3-319-73238-1 · Cited by 7

Latent-Based Imputation of Laboratory Measures from Electronic Health Records: Case for Complex DiseasesPreprint

Abedi, V., Shivakumar, M., Lu, P., Hontecillas, R., Leber, A. J., Ahuja, M., Ulloa, A., Shellenberger, M., & Bassaganya-Riera, J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/275743 · Cited by 6

Temporal progression in functional connectivity determines individual differences in working memory capacity

Bashivan, P., Yeasin, M., & Bidelman, G. M. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966220 · Cited by 5

Introduction to Accelerated Path to Cures and Precision Medicine in Inflammatory Bowel Disease

Bassaganya-Riera, J., & Hontecillas, R. (2018) · Springer eBooks · doi:10.1007/978-3-319-73238-1_1 · Cited by 3

Pathologists meet their match in tumour-spotting algorithm

(2018) · Nature · doi:10.1038/d41586-018-06724-3 · Cited by 1

Artificial intelligence literacy in higher and adult education: A scoping literature review

Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100101 · Cited by 658

Contributions and risks of artificial intelligence (AI) in building smarter cities: insights from a systematic review of the literature

Yigitcanlar, T., Desouza, K., Butler, L., & Roozkhosh, F. (2020) · Energies · doi:10.3390/en13061473 · Cited by 555

SHIFTing artificial intelligence to be responsible in healthcare: a systematic review

Siala, H., & Wang, Y. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.114782 · Cited by 358

Generative AI in medical practice: in-depth exploration of privacy and security challenges

Chen, Y., & Esmaeilzadeh, P. (2024) · Journal of Medical Internet Research · doi:10.2196/53008 · Cited by 353

Artificial Intelligence (AI) or Intelligence Augmentation (IA): What Is the Future?

Hassani, H., Silva, E. S., Unger, S., TajMazinani, M., & Feely, S. M. (2020) · AI · doi:10.3390/ai1020008 · Cited by 443

Artificial Intelligence and Healthcare Ethics

Rana, A., Goedmakers, C. M. W., & Smith, T. R. (2021) · Springer eBooks · doi:10.1007/978-3-030-78075-3_31

Practical guidance on artificial intelligence for health-care data

Ghassemi, M., Naumann, T., Schulam, P., Beam, A. L., Chen, I. Y., & Ranganath, R. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30084-6 · Cited by 170

The Reply

Abedi, V., & Zand, R. (2019) · The American Journal of Medicine · doi:10.1016/j.amjmed.2019.06.037

Inteligencia artificial para el avance de los sistemas de salud. Posibles aportes y retos

Bermúdez‐Tamayo, C., & Jiménez-Pernett, J. (2022) · Revista de Derecho de la Seguridad Social, Laborum

Personalized, Data-Rich, and Population-Level Care

Owais Durrani., M. D., & Glatter, R. (2026) · CRC Press eBooks · doi:10.1201/9781003767244-9