The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Predicting the future - big data, machine learning, and clinical medicine
Open accessAbstract
The algorithms of machine learning, which can sift through vast numbers of variables looking for combinations that reliably predict outcomes, will improve prognosis, displace much of the work of radiologists and anatomical pathologists, and improve diagnostic accuracy.
Overview
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
Cites
Statistics
Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“A structured taxonomy helps organise the expanding field of AI in clinical decision-making and clarifies how different systems support patient care across clinical contexts [30,31].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [31]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Diagnostic decision support focuses on identifying disease states from imaging, laboratory, pathology, and symptom-based data, often using deep learning to extract patterns from high-dimensional inputs [31,32].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [31]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Risk stratification supports the identification of high-risk patients and enables targeted interventions and resource allocation [31].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [31]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In our case study, the model identified elements of the patient’s history and radiology findings to render its prediction, which are critical data points that a clinician would also use.73”
From Scalable and accurate deep learning with electronic health records (2018) · cites this paper as [73]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Currently, several LLMs, including versions of the ChatGPT family, are employed by both EHR systems and insurance companies38–40, creating a conflict of interest when similar datasets are used by entities with opposing objectives21,41.”
From Preventing unrestricted and unmonitored AI experimentation in healthcare through transparency and accountability (2025) · cites this paper as [41]
Experimental — accuracy not yet measured
In the citing paper
“For example, data from various healthcare settings, which include different forms of bias and noise, may cause a model trained in the data of one hospital to fail to generalise to another [55].”
From Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond (2022) · cites this paper as [55]
In the citing paper
“Deep learning (DL) has the potential to transform medical diagnostics.”
From Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis (2021) · cites this paper as [2]
In the citing paper
“Similar to other fields and industries, AI is widely recognized in healthcare as a ground-breaking technological innovation that leads to better treatment outcomes [2–5].”
From Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey (2024) · cites this paper as [4]
In the citing paper
“Evidence also suggests that while deep learning can identify spurious correlations, it often fails to determine causation, which can be essential in a clinical context [187].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [187]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Machine Learning: An Applied Econometric Approach
Mullainathan, S., & Spiess, J. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.87 · Cited by 1,989
The Unreasonable Effectiveness of Data
Halevy, A. Y., Norvig, P., & Pereira, F. M. B. (2009) · IEEE Intelligent Systems · doi:10.1109/mis.2009.36 · Cited by 1,776
Restoring cortical control of functional movement in a human with quadriplegia
Bouton, C. E., Shaikhouni, A., Annetta, N. V., Bockbrader, M. A., Friedenberg, D. A., Nielson, D. M., Sharma, G., Sederberg, P. B., Glenn, B. C., Mysiw, W. J., et al. (2016) · Nature · doi:10.1038/nature17435 · Cited by 924
Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015) · American Economic Review · doi:10.1257/aer.p20151023 · Cited by 584
Self-citationSingle Reading with Computer-Aided Detection for Screening Mammography
Gilbert, F. J., Astley, S., Gillan, M. G., Agbaje, O. F., Wallis, M., James, J. J., Boggis, C. R. M., & Duffy, S. W. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0803545 · Cited by 296
Flagged first, then by citations
Research works printed but not linked: 1
- Mullainathan S, J Econ Perspect (in press)
Cited by
Sorted by citations
Items 601–700 of 1,011
Data-driven ICU management: Using Big Data and algorithms to improve outcomes
Carra, G., Salluh, J. I., Ramos, F. J. D. S., & Meyfroidt, G. (2020) · Journal of Critical Care · doi:10.1016/j.jcrc.2020.09.002 · Cited by 46
Application of machine learning to the prediction of postoperative sepsis after appendectomy
Bunn, C., Kulshrestha, S., Boyda, J., Balasubramanian, N., Birch, S., Karabayir, İ., Baker, M. S., Luchette, F. A., Modave, F. P., & Akbilgiç, O. (2020) · Surgery · doi:10.1016/j.surg.2020.07.045 · Cited by 46
Sentiment Analysis of Political Tweets From the 2019 Spanish Elections
Rodríguez-Ibáñez, M., Gimeno-Blanes, F.-J., Cuenca-Jiménez, P.-M., Soguero-Ruíz, C., & Rojo‐Álvarez, J. L. (2021) · IEEE Access · doi:10.1109/access.2021.3097492 · Cited by 46
Hindocha, S., & Badea, C. (2021) · AI and Ethics · doi:10.1007/s43681-021-00089-6 · Cited by 46
Charting the Unseen: How Non-Invasive Imaging Could Redefine Cardiovascular Prevention
Trimarchi, G., Pizzino, F., Paradossi, U., Gueli, I. A., Palazzini, M., Gentile, P., Di Spigno, F., Ammirati, E., Garascia, A., Tedeschi, A., et al. (2024) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd11080245 · Cited by 45
Challenges in ensuring the generalizability of image quantitation methods for MRI
Keenan, K. E., Delfino, J. G., Jordanova, K. V., Poorman, M. E., Chirra, P. V., Chaudhari, A. S., Baeßler, B., Winfield, J. M., Viswanath, S. E., & deSouza, N. M. (2021) · Medical Physics · doi:10.1002/mp.15195 · Cited by 45
The role of machine learning in HIV risk prediction
Fieggen, J., Smith, E., Arora, L., & Segal, B. (2022) · Frontiers in Reproductive Health · doi:10.3389/frph.2022.1062387 · Cited by 45
Hani, I. B., Alawadi, S., & Mrayyan, N. E. (2024) · Journal of Decision System · doi:10.1080/12460125.2024.2349425 · Cited by 44
Decoding the hallmarks of allograft dysfunction with a comprehensive pan-organ transcriptomic atlas
Robertson, H., Kim, H. J., Li, J. S. Y., Robertson, N. B., Robertson, P. R., Jimenez‐Vera, E., Ameen, F., Tran, A., Trinh, K., O’Connell, P. J., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03030-6 · Cited by 45
Ramos-Briceño, D. A., Flammia-D’Aleo, A., Fernández, G., Carrión‐Nessi, F. S., & Forero-Peña, D. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-87979-5 · Cited by 45
Artificial intelligence for direct-to-physician reporting of ambulatory electrocardiography
Johnson, L., Zadrozniak, P., Jasina, G., Grotek, A., Andrade, J. G., Svennberg, E., Diederichsen, S. Z., McIntyre, W. F., Stavrakis, S., Benezet‐Mazuecos, J., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03516-x · Cited by 47
Rahman, Q. A., Janmohamed, T., Pirbaglou, M., Clarke, H. A., Ritvo, P., Heffernan, J. M., & Katz, J. D. (2018) · Journal of Medical Internet Research · doi:10.2196/12001 · Cited by 45
Computer-Aided Detection for Breast Cancer Screening in Clinical Settings: Scoping Review
Masud, R., Al-Rei, M., & Lokker, C. (2019) · JMIR Medical Informatics · doi:10.2196/12660 · Cited by 45
Predicting High-Risk Patients and High-Risk Outcomes in Heart Failure
Wehbe, R. M., Khan, S. S., Shah, S. J., & Ahmad, F. S. (2020) · Heart Failure Clinics · doi:10.1016/j.hfc.2020.05.002 · Cited by 45
Using machine learning to predict atrial fibrillation diagnosed after ischemic stroke
Zheng, X., Wang, F., Zhang, J., Cui, X., Jiang, F., Chen, N., Zhou, J., Chen, J., Lin, S., & Zou, J. (2021) · International Journal of Cardiology · doi:10.1016/j.ijcard.2021.11.005 · Cited by 45
Kai, L. (2022) · IET Communications · doi:10.1049/cmu2.12358 · Cited by 45
Comeau, D. S., Bitterman, D. S., & Celi, L. A. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01443-2 · Cited by 44
Automation to optimise physician treatment of individual patients: examples in psychiatry
Bauer, M., Monteith, S., Geddes, J., Gitlin, M. J., Grof, P., Whybrow, P. C., & Glenn, T. (2019) · The Lancet Psychiatry · doi:10.1016/s2215-0366(19)30041-0 · Cited by 44
A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients
Yoo, D., Divard, G., Raynaud, M., Cohen, A., Mone, T., Rosenthal, J. T., Bentall, A., Stegall, M. D., Naesens, M., Zhang, H., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-44595-z · Cited by 44
Sahoo, R. K., Sahoo, K. C., Negi, S., Baliarsingh, S. K., Panda, B., & Pati, S. (2025) · Patient Education and Counseling · doi:10.1016/j.pec.2025.108680 · Cited by 44
Olawade, D. B., Soladoye, A. A., Omodunbi, B. A., Aderinto, N., & Adeyanju, I. (2025) · International Journal of Cardiology · doi:10.1016/j.ijcard.2025.133443 · Cited by 44
Park, T., Lee, I., Lee, S. W., & Kong, S. W. (2025) · Korean Journal of Pediatrics · doi:10.3345/cep.2025.00962 · Cited by 46
Hung, C.-Y., Lin, C., Lan, T., Peng, G.-S., & Lee, C.-C. (2019) · PLoS ONE · doi:10.1371/journal.pone.0213007 · Cited by 44
Fairness for Robust Log Loss Classification
Rezaei, A., Fathony, R., Memarrast, O., & Ziebart, B. D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6002 · Cited by 44
Kedra, J., Radstake, T. R. D. J., Pandit, A., Baraliakos, X., Berenbaum, F., Finckh, A., Fautrel, B., Stamm, T., Gómez-Cabrero, D., Pristipino, C., et al. (2019) · RMD Open · doi:10.1136/rmdopen-2019-001004 · Cited by 44
Artificial Intelligence and Machine Learning: A New Disruptive Force in Orthopaedics
Poduval, M., Ghose, A., Manchanda, S., Bagaria, V. B., & Sinha, A. (2020) · Indian Journal of Orthopaedics · doi:10.1007/s43465-019-00023-3 · Cited by 44
Machine Learning Can Improve Clinical Detection of Low BMD: The DXA-HIP Study
Erjiang, E., Wang, T., Yang, L., Dempsey, M., Brennan, A., Yu, M., Chan, W. P., Whelan, B., Silke, C., O’Sullivan, M., et al. (2020) · Journal of Clinical Densitometry · doi:10.1016/j.jocd.2020.10.004 · Cited by 44
Human Falling Detection Algorithm Based on Multisensor Data Fusion with SVM
Pan, D., Liu, H., Qu, D., & Zhang, Z. (2020) · Mobile Information Systems · doi:10.1155/2020/8826088 · Cited by 44
Isotope identification using deep learning: An explanation
Gomez-Fernandez, M., Wong, W., Tokuhiro, A., Welter, K. B., Alhawsawi, A. M., Yang, H., & Higley, K. A. (2020) · Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · doi:10.1016/j.nima.2020.164925 · Cited by 44
Cone-beam computed tomography cinematic rendering: clinical, teaching and research applications
Bueno, M. R., Estrela, C., Granjeiro, J. M., Estrela, M. R. D. A., AZEVEDO, B. C., & Diogenes, A. R. (2021) · Brazilian Oral Research · doi:10.1590/1807-3107bor-2021.vol35.0024 · Cited by 44
Robust Fairness Under Covariate Shift
Rezaei, A., Liu, A., Memarrast, O., & Ziebart, B. D. (2021) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v35i11.17135 · Cited by 44
Identification and prediction of phubbing behavior: a data-driven approach
Rahman, A., Duradoni, M., & Guazzini, A. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06649-5 · Cited by 44
Filho, A. D. P. C., Batista, A. F. D. M., & dos Santos, H. G. (2021) · Journal of Medical Internet Research · doi:10.2196/10969 · Cited by 44
Clinical use of artificial intelligence requires AI-capable organizations
Novak, L. L., Russell, R. G., Garvey, K., Patel, M., Thomas Craig, K. J., Snowdon, J., & Miller, B. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad028 · Cited by 43
Nwebonyi, N., Silva, S., & de Freitas, C. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.852971 · Cited by 43
Establishment of a model for predicting preterm birth based on the machine learning algorithm
Zhang, Y., Du, S., Hu, T., Xu, S., Lu, H., Xu, C., Li, J., & Zhu, X. (2023) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-023-06058-7 · Cited by 44
Computational Cytology: Lessons Learned from Pap Test Computer-Assisted Screening
Lew, M., Wilbur, D. C., & Pantanowitz, L. (2020) · Acta Cytologica · doi:10.1159/000508629 · Cited by 43
Radiomics and “radi-…omics” in cancer immunotherapy: a guide for clinicians
Porcu, M., Solinas, C., Mannelli, L., Micheletti, G., Lambertini, M., Willard-Gallo, K. E., Neri, E., Flanders, A. E., & Saba, L. (2020) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2020.103068 · Cited by 43
Valente, F., Henriques, J. M. O., Paredes, S., Rocha, T., Carvalho, P., & Morais, J. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102113 · Cited by 43
Stenzinger, A., Edsjö, A., Ploeger, C., Friedman, M., Fröhling, S., Wirta, V., Seufferlein, T. T. W., Botling, J., Duyster, J., Akhras, M. S., et al. (2021) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2021.05.026 · Cited by 43
Zhou, J., Ji, N., Wang, G., Zhang, Y., Song, H., Yuan, Y., Yang, C., Jin, Y., Zhang, Z., Zhang, L., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104097 · Cited by 43
Ji, C., Patil, K. R., Yeo, B. T. T., & Eickhoff, S. B. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.07.025 · Cited by 43
Pesapane, F., Hauglid, M. K., Fumagalli, M., Petersson, L., Parkar, A. P., Cassano, E., & Horgan, D. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111852 · Cited by 42
Machine learning of big data in gaining insight into successful treatment of hypertension
Koren, G., Nordon, G., Radinsky, K., & Shalev, V. (2018) · Pharmacology Research & Perspectives · doi:10.1002/prp2.396 · Cited by 42
Stieglitz, T. (2019) · Neuroethics · doi:10.1007/s12152-019-09406-7 · Cited by 42
Rocha, T. A. H., Elahi, C., da Silva, N. C., Sakita, F. M., Fuller, A. T., Mmbaga, B. T., Green, E. P., Haglund, M. M., Staton, C. A., & Vissoci, J. R. N. (2019) · Journal of neurosurgery · doi:10.3171/2019.2.jns182098 · Cited by 42
Karnuta, J. M., Golubovsky, J. L., Haeberle, H. S., Rajan, P. V., Navarro, S. M., Kamath, A. F., Schaffer, J. L., Krebs, V. E., Pelle, D. W., & Ramkumar, P. N. (2019) · The Spine Journal · doi:10.1016/j.spinee.2019.10.007 · Cited by 42
Logistic index for keratoconus detection and severity scoring (Logik)
Issarti, I., Consejo, A., Jiménez‐García, M., Kreps, E. O., Koppen, C., & Rozema, J. J. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103809 · Cited by 42
Klang, E., Kummer, B. R., Dangayach, N. S., Zhong, A., Kia, A., Timsina, P., Cossentino, I., Costa, A. B., Levin, M. A., & Oermann, E. K. (2021) · Scientific Reports · doi:10.1038/s41598-021-80985-3 · Cited by 42
Wuni, A.-R., Botwe, B. O., & Akudjedu, T. N. (2021) · Radiography · doi:10.1016/j.radi.2021.07.021 · Cited by 42
Machine Learning in Prediction of Bladder Cancer on Clinical Laboratory Data
Tsai, I.-J., Shen, W.-C., Lee, C.-L., Wang, H., & Lin, C. (2022) · Diagnostics · doi:10.3390/diagnostics12010203 · Cited by 42
Kunze, K. N., Orr, M. N., Krebs, V. E., Bhandari, M., & Piuzzi, N. S. (2022) · Bone & Joint Open · doi:10.1302/2633-1462.31.bjo-2021-0123.r1 · Cited by 42
Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging
Szabó, L. E., Raisi‐Estabragh, Z., Salih, A. M., McCracken, C., Pujadas, E. R., Gkontra, P., Kiss, M., Maurovich‐Horvat, P., Vágó, H., Merkely, B. P., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.1016032 · Cited by 42
Intelligent oncology: The convergence of artificial intelligence and oncology
Lin, B., Tan, Z., Mo, Y., Yang, X., Liu, Y., & Xu, B. (2022) · Journal of the National Cancer Center · doi:10.1016/j.jncc.2022.11.004 · Cited by 44
Artificial Intelligence in Medicine: Revolutionizing Healthcare for Improved Patient Outcomes
Mehta, V. (2023) · Journal of Medical Research and Innovation · doi:10.32892/jmri.292 · Cited by 42
Improved Chemical Prediction from Scarce Data Sets via Latent Space Enrichment
Iovanac, N. C., & Savoie, B. M. (2019) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b01398 · Cited by 41
Readiness Towards Artificial Intelligence Among Undergraduate Medical Students in Malaysia
Faculty of Medicine, M. U. C. M., Xuan, P. Y., Fahumida, M. I. F., Faculty of Medicine, M. U. C. M., Al Nazir Hussain, M. I., Faculty of Medicine, M. U. C. M., Jayathilake, N. T., Faculty of Medicine, M. U. C. M., Khobragade, S., Faculty of Medicine, M. U. C. M., et al. (2023) · Education in Medicine Journal · doi:10.21315/eimj2023.15.2.4 · Cited by 41
Fairness of Machine Learning Algorithms for Predicting Foregone Preventive Dental Care for Adults
Schuch, H. S., Borba, M. F., Silva, G. F. D. S., Kawachi, I. I., Filho, A. D. P. C., & Elani, H. W. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.41625 · Cited by 41
Liu, Z., Ma, C., She, W., & Xie, M. (2024) · Applied Sciences · doi:10.3390/app14020632 · Cited by 41
Kahraman, F., Aktaş, A., Bayrakceken, S., Çakar, T. E., Tarcan, H. S., Bayram, M. B., Durak, B., & Ülman, Y. I. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1428396 · Cited by 41
2016 New Horizons Lecture: Beyond Imaging—Radiology of Tomorrow
Hricak, H. (2018) · Radiology · doi:10.1148/radiol.2017171503 · Cited by 41
Debiasing versus knowledge retrieval checklists to reduce diagnostic error in ECG interpretation
Sibbald, M., Sherbino, J., Ilgen, J. S., Zwaan, L., Blissett, S., Monteiro, S., & Norman, G. R. (2019) · Advances in Health Sciences Education · doi:10.1007/s10459-019-09875-8 · Cited by 41
Artificial Intelligence in Radiology Residency Training
Forney, M. C., & McBride, A. F. (2020) · Seminars in Musculoskeletal Radiology · doi:10.1055/s-0039-3400270 · Cited by 41
Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach
Yu, C.-S., Lin, Y.-J., Lin, C.-H., Lin, S.-Y., Wu, J. L., & Chang, S.-S. (2020) · Journal of Medical Internet Research · doi:10.2196/18585 · Cited by 41
Cowling, T. E., Cromwell, D. A., Bellot, A., Sharples, L. D., & van der Meulen, J. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.12.018 · Cited by 41
Aikemu, B., Xue, P., Hong, H., Jia, H., Wang, C., Li, S., Huang, L., Ding, X., Zhang, H., Cai, G., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.594182 · Cited by 41
Karri, R., Kawai, A., Thong, Y. J., Ramson, D. M., Perry, L. A., Segal, R., Smith, J. A., & Penny‐Dimri, J. C. (2021) · Heart Lung and Circulation · doi:10.1016/j.hlc.2021.05.101 · Cited by 41
Radiomics for detecting prostate cancer bone metastases invisible in CT: a proof-of-concept study
Hinzpeter, R., Baumann, L., Guggenberger, R., Huellner, M. W., Alkadhi, H., & Baeßler, B. (2021) · European Radiology · doi:10.1007/s00330-021-08245-6 · Cited by 41
Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests
Xu, S., Hom, J., Balasubramanian, S., Schroeder, L. F., Najafi, N., Roy, S., & Chen, J. H. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.10967 · Cited by 40
Reps, J. M., Williams, R. D., You, S. C., Falconer, T., Minty, E., Callahan, A., Ryan, P. B., Park, R. W., Lim, H.-S., & Rijnbeek, P. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-00991-3 · Cited by 40
A2M-LEUK: attention-augmented algorithm for blood cancer detection in children
Talaat, F. M., & Gamel, S. A. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08678-8 · Cited by 40
Application of ChatGPT-based blended medical teaching in clinical education of hepatobiliary surgery
Wu, C., Chen, L., Han, M. S., Li, Z., Yang, N., & Yu, C. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2339412 · Cited by 40
Artificial Intelligence (AI) in Pharmaceutical Formulation and Dosage Calculations
Joshi, S., & Sheth, S. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17111440 · Cited by 39
Huff, T. J., Ludwig, P. E., & Zuñiga, J. M. (2018) · Expert Review of Medical Devices · doi:10.1080/17434440.2018.1473033 · Cited by 40
How will “democratization of artificial intelligence” change the future of radiologists?
Kobayashi, Y., Ishibashi, M., & Kobayashi, H. (2018) · Japanese Journal of Radiology · doi:10.1007/s11604-018-0793-5 · Cited by 40
Machine Learning Increases Diagnosticity in Psychometric Evaluation of Alexithymia in Fibromyalgia
Orrù, G., Gemignani, A., Ciacchini, R., Bazzichi, L. M., & Conversano, C. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2019.00319 · Cited by 40
Machine Learning in Rheumatic Diseases
Jiang, M., Li, Y., Jiang, C., Zhao, L., Zhang, X., & Lipsky, P. E. (2020) · Clinical Reviews in Allergy & Immunology · doi:10.1007/s12016-020-08805-6 · Cited by 40
Nie, X., Cai, Y., Liu, J., Liu, X., Zhao, J., Yang, Z., Wen, M., & Liu, L. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2020.610531 · Cited by 40
Wang, H., & Ma, S. (2021) · Socio-Economic Planning Sciences · doi:10.1016/j.seps.2021.101043 · Cited by 40
Current applications of artificial intelligence in vascular surgery
Fischer, U. M., Shireman, P. K., & Lin, J. C. (2021) · Seminars in Vascular Surgery · doi:10.1053/j.semvascsurg.2021.10.008 · Cited by 40
A Machine Learning-Based Prediction Model for Cardiovascular Risk in Women With Preeclampsia
Wang, G., Zhang, Y., Li, S., Zhang, J., Jiang, D., Li, X., Li, Y., & Du, J. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.736491 · Cited by 40
Zhou, C.-M., Xue, Q., Wang, Y., Tong, J., Ji, M., & Yang, J. (2020) · Surgery Today · doi:10.1007/s00595-020-02170-9 · Cited by 39
Artificial Intelligence for Emerging Technology in Surgery: Systematic Review and Validation
Nwoye, E. O., Woo, W. L., Gao, B., & Anyanwu, T. (2022) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2022.3183852 · Cited by 39
Machine Learning and Health Science Research: Tutorial
Cho, H., She, J., de Marchi, D., El-Zaatari, H., Barnes, E. L., Kahkoska, A. R., Kosorok, M. R., & Virkud, A. (2023) · Journal of Medical Internet Research · doi:10.2196/50890 · Cited by 39
Marsden, H., Kemos, P., Venzi, M., Noy, M., Maheswaran, S., Francis, N., Hyde, C., Mullarkey, D., Kalsi, D., & Thomas, L. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1302363 · Cited by 39
Using artificial intelligence to enhance patient autonomy in healthcare decision-making
Quiñones, J. L. G. (2024) · AI & Society · doi:10.1007/s00146-024-01956-6 · Cited by 39
Revolutionizing Cardiology: The Role of Artificial Intelligence in Echocardiography
Maturi, B., Dulal, S., Ibrahim, A., Ramakrishna, M., Chinta, V., Nanda, N. C., Sharma, A. K., & Ravipati, H. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14020625 · Cited by 39
Morris, A. H. (2018) · Annals of the American Thoracic Society · doi:10.1513/annalsats.201706-449kv · Cited by 39
Inflammatory Bowel Disease: Complexity and Variability Need Integration
Fiocchi, C. (2018) · Frontiers in Medicine · doi:10.3389/fmed.2018.00075 · Cited by 39
Kong, X., Ai, B., Kong, Y., Su, L., Ning, Y., Howard, N., Gong, S., Li, C., Wang, J., Lee, W.-T., et al. (2019) · PubMed Central · Cited by 39
Zoli, M., Staartjes, V. E., Guaraldi, F., Friso, F., Rustici, A., Asioli, S., Sollini, G., Pasquini, E., Regli, L., Serra, C., et al. (2020) · Neurosurgical FOCUS · doi:10.3171/2020.3.focus2060 · Cited by 39
Manafi‐Farid, R., Ziarati, N. K., Vali, R., Mottaghy, F. M., & Beheshti, M. (2020) · Methods · doi:10.1016/j.ymeth.2020.05.023 · Cited by 39
Overview of Artificial Intelligence Applications in Chinese Medicine Therapy
Feng, C., Zhou, S., Qu, Y., Wang, Q., Bao, S., Li, Y., & Yang, T. (2021) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2021/6678958 · Cited by 39
Natural Language Processing Enhances Prediction of Functional Outcome After Acute Ischemic Stroke
Sung, S., Chen, C., Pan, R., Hu, Y., & Jeng, J. (2021) · Journal of the American Heart Association · doi:10.1161/jaha.121.023486 · Cited by 39
Ryu, S. M., Lee, S.-H., Kim, E., & Eoh, W. (2018) · World Neurosurgery · doi:10.1016/j.wneu.2018.12.091 · Cited by 39
Artificial intelligence and radiomics in nuclear medicine: potentials and challenges
Aktolun, C. (2019) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-019-04593-0 · Cited by 39
Application of Artificial Intelligence in Oncology Nursing
Zhou, T., Luo, Y., Li, J., Zhang, H., Meng, Z., Xiong, W., & Zhang, J. (2023) · Cancer Nursing · doi:10.1097/ncc.0000000000001254 · Cited by 38
Demystifying Statistics and Machine Learning in Analysis of Structured Tabular Data
Khosravi, B., Weston, A. D., Nugen, F., Mickley, J. P., Kremers, H. M., Wyles, C. C., Carter, R. E., & Taunton, M. J. (2023) · The Journal of Arthroplasty · doi:10.1016/j.arth.2023.08.045 · Cited by 38
Logothetis, S. B., Green, D., Holland, M., & Moubayed, N. A. (2023) · Scientific Reports · doi:10.1038/s41598-023-40661-0 · Cited by 38
Explainable AI in Healthcare Application
Sindiramutty, S. R., Tee, W. J., Balakrishnan, S., Kaur, S., Thangaveloo, R., Jazri, H., Khan, N. A., Gharib, A. H., & Manchuri, A. R. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-6684-6361-1.ch005 · Cited by 38
Metadata
- Authors
- Obermeyer, Z., & Emanuel, E. J.
- Affiliations
- Brigham and Women's Hospital (US)
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2016-09-29
- Volume
- 375
- Issue
- 13
- Pages
- 1216-1219
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- PMC
- PMC5070532
- OpenAlex
- W2525984666
- Semantic Scholar
- 4d1fdd81f033cd58f3723bfc61e7d12079647a7a
- Cited by
- 3,652
- References
- 5
- Citations by year
- 2026: 494 · 2025: 596 · 2024: 492 · 2023: 378 · 2022: 371 · 2021: 389 · 2020: 348 · 2019: 286 · 2018: 195 · 2017: 95 · 2016: 3
- FWCI
- 140.21
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (DP5OD012161)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- machine learning, big data, clinical medicine, outcome prediction, diagnostic accuracy, radiology, pathological anatomy
- MeSH terms
- Machine Learning, Algorithms, Diagnosis, Expert Systems, Prognosis, Clinical Medicine, Computational Biology
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
