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Predicting the future - big data, machine learning, and clinical medicine
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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.
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“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]
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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)
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Items 401–500 of 1,011
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
Eitel, F., Schulz, M.-A., Seiler, M., Walter, H., & Ritter, K. (2021) · Experimental Neurology · doi:10.1016/j.expneurol.2021.113608 · Cited by 73
Gutiérrez‐Tobal, G. C., Álvarez, D., Kheirandish‐Gozal, L., del Campo, F., Gozal, D., & Hornero, R. (2021) · Pediatric Pulmonology · doi:10.1002/ppul.25423 · Cited by 73
Disparate Vulnerability to Membership Inference Attacks
Kulynych, B., Yaghini, M., Cherubin, G., Veale, M., & Troncoso, C. (2021) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2022-0023 · Cited by 73
Shi, B., Chen, J., Chen, H., Lin, W., Yang, J., Chen, Y., Wu, C., & Huang, Z. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105885 · Cited by 73
Accelerating health disparities research with artificial intelligence
Green, B. L., Murphy, A., & Robinson, E. J. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1330160 · Cited by 73
Moon, S. J., Hwang, J., Kana, R. K., Torous, J. B., & Kim, J. W. (2019) · JMIR Mental Health · doi:10.2196/14108 · Cited by 73
Machine Learning na Medicina: Revisão e Aplicabilidade
Paixao, G. M., Santos, B. C., de Araujo, R. M., Ribeiro, M. H., de Moraes, J. L., & Ribeiro, A. L. P. (2022) · Arquivos Brasileiros de Cardiologia · doi:10.36660/abc.20200596 · Cited by 65
Eshghali, M., Kannan, D., Salmanzadeh-Meydani, N., & Sikaroudi, A. M. E. (2023) · Annals of Operations Research · doi:10.1007/s10479-023-05168-x · Cited by 72
Big data science: A literature review of nursing research exemplars
Westra, B. L., Sylvia, M. L., Weinfurter, E., Pruinelli, L., Park, J. I., Dodd, D. S., Keenan, G. M., Senk, P. A., Richesson, R., Baukner, V., et al. (2016) · Nursing Outlook · doi:10.1016/j.outlook.2016.11.021 · Cited by 71
Intelligent health data analytics: A convergence of artificial intelligence and big data
Abidi, S. S. R., & Abidi, S. R. (2019) · Healthcare Management Forum · doi:10.1177/0840470419846134 · Cited by 71
Alkhadar, H., Macluskey, M., White, S., Ellis, I., & Gardner, A. (2020) · Journal of Oral Pathology and Medicine · doi:10.1111/jop.13135 · Cited by 71
Aziz, F., Malek, S., Ibrahim, K. S., Shariff, R. E. R., Ahmad, W. A. W., Ali, R. M., Liu, K. T., Selvaraj, G., & Kasim, S. S. (2021) · PLoS ONE · doi:10.1371/journal.pone.0254894 · Cited by 71
Monlezun, D. J., Dart, L., VanBeber, A. D., Smith‐Barbaro, P., Costilla, V. C., Samuel, C., Terregino, C. A., Abali, E. E., Dollinger, B., Baumgartner, N., et al. (2018) · BioMed Research International · doi:10.1155/2018/5051289 · Cited by 71
Using Machine Learning to Assess Physician Competence: A Systematic Review
DIAS, R. D., Gupta, A., & Yule, S. (2018) · Academic Medicine · doi:10.1097/acm.0000000000002414 · Cited by 71
Systematic review and meta-analysis of prediction models used in cervical cancer
Jha, A. K., Mithun, S., Sherkhane, U. B., Jaiswar, V., Osong, B., Purandare, N. C., Kannan, S., Prabhash, K., Gupta, S., Vanneste, B. G. L., et al. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102549 · Cited by 70
Zhuhadar, L., & Lytras, M. D. (2023) · Sustainability · doi:10.3390/su151813484 · Cited by 70
Park, S. H., & Kressel, H. Y. (2018) · Journal of Korean Medical Science · doi:10.3346/jkms.2018.33.e152 · Cited by 70
Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation
Thongprayoon, C., Kaewput, W., Kovvuru, K. R., Hansrivijit, P., Kanduri, S. R., Bathini, T. J., Chewcharat, A., Leeaphorn, N., Suarez, M. L. G., & Cheungpasitporn, W. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9041107 · Cited by 70
Automated ASPECT scoring in acute ischemic stroke: comparison of three software tools
Hoelter, P., Muehlen, I., Goelitz, P., Beuscher, V. D., Schwab, S., & Doerfler, A. (2020) · Neuroradiology · doi:10.1007/s00234-020-02439-3 · Cited by 69
Song, Y., Yang, X., Luo, Y., Ou-yang, C.-L., Yu, Y., Ma, Y., Li, H., Lou, J., Liu, Y., Chen, Y., et al. (2022) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.13991 · Cited by 69
Lünich, M. (2022) · Springer eBooks · doi:10.1007/978-3-658-36368-0 · Cited by 68
AI support for ethical decision-making around resuscitation: proceed with care
Biller-Andorno, N., Ferrario, A., Joebges, S., Krones, T., Massini, F., Barth, P., Arampatzis, G., & Krauthammer, M. (2022) · Journal of Medical Ethics · doi:10.1136/medethics-2020-106786 · Cited by 67
Morelli, V., Palmieri, S., Lania, A., Tresoldi, A., Corbetta, S., Cairoli, E., Eller-Vainicher, C., Arosio, M., Copetti, M., Grossi, E., et al. (2017) · European Journal of Endocrinology · doi:10.1530/eje-17-0047 · Cited by 67
Simonov, M., Ugwuowo, U. C., Moreira, E., Yamamoto, Y., Biswas, A., Martin, M. C., Testani, J. M., & Wilson, F. P. (2019) · PLoS Medicine · doi:10.1371/journal.pmed.1002861 · Cited by 67
Jing, Y., Hu, Z., Fan, P., Xue, Y., Wang, L., Tarter, R. E., Kirisci, L., Wang, J., Vanyukov, M. M., & Xie, X. (2019) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2019.107605 · Cited by 67
Medical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration
Kim, H., Kim, D., & Yoon, K. (2019) · Endocrinology and Metabolism · doi:10.3803/enm.2019.34.4.349 · Cited by 68
Heinrichs, H., Kies, A., Nagel, S. K., & Kiessling, F. (2025) · Journal of Medical Internet Research · doi:10.2196/74187 · Cited by 66
Osawa, I., Goto, T., Yamamoto, Y., & Tsugawa, Y. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00354-8 · Cited by 66
Zhu, W., Li, W., Tian, Z., Zhang, Y., Wang, K., Zhang, Y., Liu, J., & Yang, X. (2020) · Translational Stroke Research · doi:10.1007/s12975-020-00811-2 · Cited by 66
Tajdari, M., Pawar, A., Li, H., Tajdari, F., Maqsood, A., Cleary, E. J., Saha, S. K., Zhang, Y. J., Sarwark, J. F., & Liu, W. K. (2020) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2020.113590 · Cited by 66
Machine learning for molecular thermodynamics
Ding, J., Xu, N., Nguyen, M. T., Qiao, Q., Shi, Y., He, Y., & Shao, Q. (2021) · Chinese Journal of Chemical Engineering · doi:10.1016/j.cjche.2020.10.044 · Cited by 66
on behalf of the RETRO study group, Vodenčarević, A., Taşçılar, K., Hartmann, F., Reiser, M., Hueber, A. J., Haschka, J., Bayat, S., Meinderink, T., Knitza, J., et al. (2021) · Arthritis Research & Therapy · doi:10.1186/s13075-021-02439-5 · Cited by 66
Lombi, L., & Rossero, E. (2023) · Sociology of Health & Illness · doi:10.1111/1467-9566.13702 · Cited by 66
Data Resource Profile: Regional healthcare information platform in Halland, Sweden
Ashfaq, A., Lönn, S., Nilsson, H., Eriksson, J. A., Kwatra, J., Yasin, Z. M., Slutzman, J. E., Wallenfeldt, T., Obermeyer, Z., Anderson, P. D., et al. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz262 · Cited by 66
Self-citationAung, H. L., Aghvinian, M. N., Gouse, H., Robbins, R. N., Brew, B. J., Mao, L., & Cysique, L. A. (2020) · AIDS and Behavior · doi:10.1007/s10461-020-03053-3 · Cited by 65
Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning
Izadgoshasb, H., Kandiri, A., Shakor, P., Laghi, V., & Gasparini, G. (2021) · Applied Sciences · doi:10.3390/app112210826 · Cited by 65
Madakkatel, I., Zhou, A., McDonnell, M. D., & Hyppönen, E. (2021) · Scientific Reports · doi:10.1038/s41598-021-02476-9 · Cited by 65
Li, X., Wu, R., Zhao, W., Shi, R., Zhu, Y., Wang, Z., Pan, H., & Wang, D. (2023) · Scientific Reports · doi:10.1038/s41598-023-32160-z · Cited by 65
Radiology Residents’ Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study
Chen, Y., Wu, Z., Wang, P., Xie, L., Yan, M., Jiang, M., Yang, Z., Zheng, J., Zhang, J., & Zhu, J. (2023) · Journal of Medical Internet Research · doi:10.2196/48249 · Cited by 65
Juárez‐Orozco, L. E., Knol, R. J. J., Sánchez-Catasús, C. A., Martinez‐Manzanera, O., van der Zant, F. M., & Knuuti, J. M. (2018) · Journal of Nuclear Cardiology · doi:10.1007/s12350-018-1304-x · Cited by 65
Triantafyllidis, A. K., Polychronidou, E., Alexiadis, A., Rocha, C., Oliveira, D. N., da Silva, A. S., Freire, A. L., Macedo, C., Sousa, I. F., Werbet, E., et al. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101844 · Cited by 65
Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol
Maffulli, N., Rodriguez, H. C., Stone, I. W., Nam, A., Song, A. J., Gupta, M., Alvarado, R., Ramón, D., & Gupta, A. (2020) · Journal of Orthopaedic Surgery and Research · doi:10.1186/s13018-020-02002-z · Cited by 65
Layard Horsfall, H., Palmisciano, P., Khan, D. Z., Muirhead, W., Koh, C. H., Stoyanov, D., & Marcus, H. J. (2021) · World Neurosurgery · doi:10.1016/j.wneu.2020.10.171 · Cited by 64
Racine, A. M., Tommet, D., D’Aquila, M., Fong, T. G., Gou, Y., Tabloski, P. A., Metzger, E. D., Hshieh, T. T., Schmitt, E. M., Vasunilashorn, S. M., et al. (2020) · Journal of General Internal Medicine · doi:10.1007/s11606-020-06238-7 · Cited by 64
Tang, T., Yu, Z., Xu, Q., Peng, Z., Fan, Y., Wang, K., Ren, Q., Qu, J., & Zhao, M. (2020) · Eye and Vision · doi:10.1186/s40662-020-00214-2 · Cited by 64
The Impact of Artificial Intelligence on Dental Implantology: A Narrative Review
Altalhi, A. M., Alharbi, F. S., Alhodaithy, M. A., Almarshedy, B. S., Al-saaib, M. Y., jfshar, R. M. A., Aljohani, A. S., Alshareef, A. H., Muhayya, M., & AL-harbi, N. H. (2023) · Cureus · doi:10.7759/cureus.47941 · Cited by 64
Ajana, S., Cougnard‐Grégoire, A., Colijn, J. M., Merle, B. M. J., Verzijden, T., de Jong, P. T. V. M., Albert Hofman, D., Vingerling, J. R., Hejblum, B. P., Korobelnik, J., et al. (2020) · Ophthalmology · doi:10.1016/j.ophtha.2020.08.031 · Cited by 64
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Schmidt, H. G., & Mamede, S. (2020) · Advances in Health Sciences Education · doi:10.1007/s10459-020-10011-0 · Cited by 65
Functional Neuroimaging in Traumatic Brain Injury: From Nodes to Networks
Medaglia, J. D. (2017) · Frontiers in Neurology · doi:10.3389/fneur.2017.00407 · Cited by 63
Goebel, J., Stenzel, E., Guberina, N., Wanke, I., Koehrmann, M., Kleinschnitz, C., Umutlu, L., Forsting, M., Mönninghoff, C., & Radbruch, A. (2018) · Neuroradiology · doi:10.1007/s00234-018-2098-x · Cited by 63
Ye, H., Wu, P., Zhu, T., Xiao, Z., Zhang, X., Zheng, L., Zheng, R., Sun, Y., Zhou, W., Fu, Q., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3052835 · Cited by 63
Liu, W., Li, Z., Luo, Z., Liao, W., Liu, Z., & Liu, J. (2021) · Cancer Medicine · doi:10.1002/cam4.3776 · Cited by 63
Shi, B., Ye, H., Zheng, L., Lyu, J., Chen, C., Heidari, A. A., Hu, Z., Chen, H., & Wu, P. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104698 · Cited by 63
RetractedWagner, S. K., Hughes, F., Cortina‐Borja, M., Pontikos, N., Struyven, R., Liu, X., Montgomery, H., Alexander, D. C., Topol, E. J., Petersen, S. E., et al. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-058552 · Cited by 63
Dourado, D. D. A., & Aith, F. (2022) · Revista de Saúde Pública · doi:10.11606/s1518-8787.2022056004461 · Cited by 63
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Cabitza, F., Ciucci, D., & Rasoini, R. (2018) · Lecture notes in information systems and organisation · doi:10.1007/978-3-319-90503-7_10 · Cited by 63
Karnuta, J. M., Navarro, S. M., Haeberle, H. S., Billow, D. G., Krebs, V. E., & Ramkumar, P. N. (2019) · Journal of Orthopaedic Trauma · doi:10.1097/bot.0000000000001454 · Cited by 63
Fan, M., Billings, A. C., Zhu, X., & Yu, P. (2019) · Communication & Sport · doi:10.1177/2167479519834348 · Cited by 63
The number needed to benefit: estimating the value of predictive analytics in healthcare
Liu, V. X., Bates, D. W., Wiens, J., & Shah, N. H. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz088 · Cited by 63
Machine intelligence in non-invasive endocrine cancer diagnostics
Thomasian, N. M., Kamel, I. R., & Bai, H. X. (2021) · Nature Reviews Endocrinology · doi:10.1038/s41574-021-00543-9 · Cited by 63
Cervical cancer survival prediction by machine learning algorithms: a systematic review
Rahimi, M., Akbari, A., Asadi, F., & Emami, H. (2023) · BMC Cancer · doi:10.1186/s12885-023-10808-3 · Cited by 62
Gazis, I.-Z., Schoening, T., Alevizos, E., & Greinert, J. (2018) · Biogeosciences · doi:10.5194/bg-15-7347-2018 · Cited by 62
Cognitive slowing and its underlying neurobiology in temporal lobe epilepsy
Hwang, G., Dabbs, K., Conant, L. L., Nair, V. A., Mathis, J., Almane, D. N., Nencka, A. S., Birn, R. M., Humphries, C., Raghavan, M., et al. (2019) · Cortex · doi:10.1016/j.cortex.2019.02.022 · Cited by 62
Advanced decision‐making using patient‐reported outcome measures in total joint replacement
Jayakumar, P., & Bozic, K. J. (2020) · Journal of Orthopaedic Research® · doi:10.1002/jor.24614 · Cited by 62
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van Assen, M., Lee, S., & Cecco, C. N. D. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109083 · Cited by 62
Liu, J., Xiong, Y., Zhong, M., Yang, Y., Guo, X., Tan, X., & Zhao, B. (2020) · Neurosurgery · doi:10.1093/neuros/nyaa052 · Cited by 61
Matthews, B., Shannon, B., & Roxburgh, M. (2023) · International Journal of Art & Design Education · doi:10.1111/jade.12460 · Cited by 61
ChatGPT Assisting Diagnosis of Neuro-Ophthalmology Diseases Based on Case Reports
Madadi, Y., Delsoz, M., Lao, P. A. O., Fong, J. W., Hollingsworth, T., Kahook, M. Y., & Yousefi, S. (2024) · Journal of Neuro-Ophthalmology · doi:10.1097/wno.0000000000002274 · Cited by 61
Farris, A. B., Vizcarra, J. C., Amgad, M. T., Cooper, L., Gutman, D. A., & Hogan, J. (2020) · Histopathology · doi:10.1111/his.14304 · Cited by 61
Lee, Y.-M., Kim, S., Lee, Y.-H., Kim, H.-S., Seo, S. W., Kim, H., & Kim, K. J. (2024) · Academic Medicine · doi:10.1097/acm.0000000000005618 · Cited by 60
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Klein, W. M. P., O’Connell, M. E., Bloch, M., Czajkowski, S. M., Green, P. A., Han, P. K. J., Moser, R. P., Nebeling, L. C., & Vanderpool, R. C. (2021) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djab139 · Cited by 60
Bullock, G. S., Hughes, T., Arundale, A. J. H., Ward, P., Collins, P. G. S., & Kluzek, S. (2022) · Sports Medicine · doi:10.1007/s40279-022-01655-6 · Cited by 61
Ge, J., Kim, W. R., Lai, J. C., & Kwong, A. J. (2022) · Journal of Hepatology · doi:10.1016/j.jhep.2022.03.003 · Cited by 60
Loupy, A., Sablik, M., Khush, K. K., & Reese, P. P. (2025) · The Lancet · doi:10.1016/s0140-6736(25)00195-3 · Cited by 62
Machine Learning for Accurate Intraoperative Pediatric Middle Ear Effusion Diagnosis
Crowson, M. G., Hartnick, C. J., Diercks, G. R., Gallagher, T. Q., Fracchia, M. S., Setlur, J., & Cohen, M. S. (2021) · PEDIATRICS · doi:10.1542/peds.2020-034546 · Cited by 60
Application of Artificial Intelligence (AI) in a Cephalometric Analysis: A Narrative Review
Kiełczykowski, M., Kamiński, K., Perkowski, K., Zadurska, M., & Czochrowska, E. M. (2023) · Diagnostics · doi:10.3390/diagnostics13162640 · Cited by 59
Classification of hospital admissions into emergency and elective care: a machine learning approach
Krämer, J., Schreyögg, J., & Busse, R. (2017) · Health Care Management Science · doi:10.1007/s10729-017-9423-5 · Cited by 59
Nørfeldt, L., Boetker, J. P., Edinger, M., Genina, N., & Rantanen, J. (2019) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2019.04.025 · Cited by 59
Logistics industry monitoring system based on wireless sensor network platform
Jiang, J., Wang, H., Mu, X., & Guan, S. U. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.03.016 · Cited by 59
Raparelli, V., Norris, C. M., Bender, U., Herrero, M. T., Kautzky‐Willer, A., Kublickiene, K. R., Emam, K. E., & Pilote, L. (2021) · BMJ Global Health · doi:10.1136/bmjgh-2021-005413 · Cited by 59
Wu, T., Lin, X., Mu, Y., Li, H., & Guo, Y. S. (2021) · Clinical Cardiology · doi:10.1002/clc.23541 · Cited by 58
Martinez, D. S.-L., Noseworthy, P. A., Akbilgiç, O., Herrmann, J., Ruddy, K. J., Hamid, A. B., Maddula, R., Singh, A., Davis, R. L., Güntürkün, F., et al. (2022) · American Heart Journal Plus Cardiology Research and Practice · doi:10.1016/j.ahjo.2022.100129 · Cited by 58
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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
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