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
Research works printed but not linked: 1
- Mullainathan S, J Econ Perspect (in press)
Cited by
Sorted by citations
Items 801–900 of 1,011
AI-readiness Criteria for Biomedical Data
Clark, T., Caufield, H., Parker, J. A., Al Manir, S., Amorim, E., Eddy, J., Gim, N., Gow, B., Goar, W., Hansen, J. N., et al. (2024) · doi:10.1101/2024.10.23.619844 · Cited by 32
Advanced imaging techniques and artificial intelligence in pleural diseases: a narrative review
Marchi, G., Mercier, M., Cefalo, J., Salerni, C., Ferioli, M., Candoli, P., Gori, L., Cucchiara, F., Cenerini, G., Guglielmi, G., et al. (2025) · European Respiratory Review · doi:10.1183/16000617.0263-2024 · Cited by 32
Applications, challenges and future directions of artificial intelligence in cardio‐oncology
Ravera, F., Gilardi, N., Ballestrero, A., & Zoppoli, G. M. (2025) · European Journal of Clinical Investigation · doi:10.1111/eci.14370 · Cited by 32
Kim, Y. J., Saqlain, M., & Lee, J. Y. (2019) · Personal and Ubiquitous Computing · doi:10.1007/s00779-019-01248-7 · Cited by 32
Chiarelli, A. M., Croce, P., Assenza, G., Merla, A., Granata, G., Giannantoni, N. M., Pizzella, V., Tecchio, F., & Zappasodi, F. (2020) · International Journal of Neural Systems · doi:10.1142/s0129065720500677 · Cited by 32
Maldaner, N., Zeitlberger, A. M., Sosnova, M., Goldberg, J., Fung, C., Bervini, D., May, A. T., Bijlenga, P., Schaller, K., Roethlisberger, M., et al. (2020) · Neurosurgery · doi:10.1093/neuros/nyaa401 · Cited by 32
Jin, L., Zhao, C., Li, H., Wang, R., Wang, Q., & Wang, H. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.629671 · Cited by 32
Explainable machine learning model to predict refeeding hypophosphatemia
Choi, T. Y., Chang, M.-Y., Heo, S., & Jang, J. Y. (2021) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2021.08.022 · Cited by 32
Neurosurgery outcomes and complications in a monocentric 7-year patient registry
Sarnthein, J., Staartjes, V. E., Regli, L., Akeret, K., Bektas, D., Bellut, D., Bichsel, O., Bozinov, O., Colombo, E., Dias, S. F., et al. (2022) · Brain and Spine · doi:10.1016/j.bas.2022.100860 · Cited by 32
Artificial intelligence accelerates multi-modal biomedical process: A Survey
Li, J., Han, X., Qin, Y., Tan, F., Chen, Y., Wang, Z., Song, H., Zhou, X., Zhang, Y., Hu, L., et al. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126720 · Cited by 32
Big data phenotyping in rare diseases: some ethical issues
Hallowell, N., Parker, M. J., & Nellåker, C. (2018) · Genetics in Medicine · doi:10.1038/s41436-018-0067-8 · Cited by 32
Consensus Learning for Distributed Fuzzy Neural Network in Big Data Environment
Shi, Y., Lin, C., Chang, Y., Ding, W., Shi, Y., & Yao, X. (2020) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2020.2998919 · Cited by 32
Alam, M. A., Sohel, A., Hasan, K. M., & Ahmad, I. (2024) · Strategic Data Management and Innovation · doi:10.71292/sdmi.v1i01.6 · Cited by 31
Precision Medicine: Functional Advancements
Caskey, C. T. (2017) · Annual Review of Medicine · doi:10.1146/annurev-med-041316-090905 · Cited by 31
Marchello, C. S., Ebell, M. H., Dale, A. P., Harvill, E. T., Shen, Y., & Whalen, C. C. (2019) · The Journal of the American Board of Family Medicine · doi:10.3122/jabfm.2019.02.180219 · Cited by 31
Can machine learning improve patient selection for cardiac resynchronization therapy?
Hu, S.-Y., Santus, E., Forsyth, A. W., Malhotra, D., Haimson, J., Chatterjee, N. A., Kramer, D. B., Barzilay, R., Tulsky, J. A., & Lindvall, C. J. (2019) · PLoS ONE · doi:10.1371/journal.pone.0222397 · Cited by 31
Using artificial intelligence to improve medical services in China
Li, R., Yang, Y., Wu, S., Huang, K., Chen, W., Liu, Y., & Lin, H. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2019.11.108 · Cited by 31
Assessment and prediction of spine surgery invasiveness with machine learning techniques
Campagner, A., Berjano, P. L., Lamartina, C., Langella, F., Lombardi, G., & Cabitza, F. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103796 · Cited by 32
Leiter, R. E., Santus, E., Jin, Z., Lee, K. C., Yusufov, M., Chien, I., Ramaswamy, A., Moseley, E. T., Qian, Y., Schrag, D., et al. (2020) · Journal of Pain and Symptom Management · doi:10.1016/j.jpainsymman.2020.06.010 · Cited by 31
Security, privacy, and information-sharing aspects of healthcare artificial intelligence
Hlávka, J. P. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818438-7.00010-1 · Cited by 31
Muscas, G., Matteuzzi, T., Becattini, E., Orlandini, S., Battista, F., Laiso, A., Nappini, S., Limbucci, N., Renieri, L., Carangelo, B. R., et al. (2020) · Acta Neurochirurgica · doi:10.1007/s00701-020-04484-6 · Cited by 31
Mar, J., Gorostiza, A., Ibarrondo, O., Cernuda, C., Arrospide, A., Iruín, Á., Larrañaga, I., Tainta, M., Ezpeleta, E., & Alberdi, A. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-200345 · Cited by 31
Silva, K. D., Mathews, N., Teede, H. J., Forbes, A. B., Jönsson, D., Demmer, R. T., & Enticott, J. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104305 · Cited by 31
Wu, T., Zheng, R. F., Lin, Z. Z., Gong, H. R., & Li, H. (2021) · BMC Emergency Medicine · doi:10.1186/s12873-021-00501-8 · Cited by 31
Evaluation of pediatric ophthalmologists' perspectives of artificial intelligence in ophthalmology
Valikodath, N. G., Al-Khaled, T., Cole, E. D., Ting, D. S. W., Tu, E. Y., Campbell, J. P., Chiang, M. F., Hallak, J., & Chan, R. V. P. (2021) · Journal of American Association for Pediatric Ophthalmology and Strabismus · doi:10.1016/j.jaapos.2021.01.011 · Cited by 31
THYROID DETECTION USING MACHINE LEARNING
Chandan, R., Vasan, C., Chethan, M. S., & Devikarani, H. S. (2021) · International Journal of Engineering Applied Sciences and Technology · doi:10.33564/ijeast.2021.v05i09.028 · Cited by 31
Spatial Technology Assessment of Green Space Exposure and Myopia
Yang, Y., Chen, W., Xu, A., Zhao, L., Ding, X., Li, J., Zhu, Y., Chen, C., Long, E., Liu, Z., et al. (2021) · Ophthalmology · doi:10.1016/j.ophtha.2021.07.031 · Cited by 31
Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology
Lim, J. S., Hong, M., Lam, W. S. T., Zhang, Z., Teo, Z. L., Liu, Y., Ng, W. Y., Foo, L. L., & Ting, D. S. W. (2022) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000846 · Cited by 31
Piloting a Survey-Based Assessment of Transparency and Trustworthiness with Three Medical AI Tools
Fehr, J., Jaramillo-Gutierrez, G., Oala, L., Gröschel, M. I., Bierwirth, M., Balachandran, P., Leite, A. W., & Lippert, C. (2022) · Healthcare · doi:10.3390/healthcare10101923 · Cited by 31
Correia, M. I. T. D. (2023) · Clinical Nutrition Open Science · doi:10.1016/j.nutos.2022.12.010 · Cited by 31
Ma, Y., Lü, Q., Yuan, F., & Chen, H. (2023) · Journal of Orthopaedic Surgery and Research · doi:10.1186/s13018-023-03551-9 · Cited by 31
Arya, N., Saha, S., Mathur, A., & Saha, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-30143-8 · Cited by 31
Kim, K., Kim, M., Adamopoulos, I. E., & Tagkopoulos, I. (2019) · Clinical Immunology · doi:10.1016/j.clim.2019.03.002 · Cited by 31
Human-centred artificial intelligence for mobile health sensing: challenges and opportunities
Dang, T., Spathis, D., Ghosh, A., & Mascolo, C. (2023) · Royal Society Open Science · doi:10.1098/rsos.230806 · Cited by 30
Deng, W., Wang, D., Wan, Y.-D., Lai, S., Ding, Y., & Wang, X. (2024) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1287434 · Cited by 30
Deepa, R., Sadu, V. B., C, P. G., & Sivasamy, A. (2024) · AIP Advances · doi:10.1063/5.0191990 · Cited by 30
Predicting Alzheimer's disease CSF core biomarkers: a multimodal Machine Learning approach
Gaeta, A. M., Quijada-López, M., Barbé, F., Vaca, R., Pujol, M., Mínguez, O., Sánchez‐de‐la‐Torre, M., Muñoz‐Barrutia, A., & Piñol‐Ripoll, G. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1369545 · Cited by 30
Chau, M. T. (2024) · Journal of medical imaging and radiation sciences · doi:10.1016/j.jmir.2024.101733 · Cited by 30
Cruz, J. P., Sembekova, A., Omirzakova, D., Bolla, S. R., & Balay‐odao, E. M. (2024) · Health Professions Education · doi:10.55890/2452-3011.1296 · Cited by 30
Emi-Johnson, O. G., & Nkrumah, K. J. (2025) · Cureus · doi:10.7759/cureus.82437 · Cited by 30
A unifying framework for improving health care
Djulbegović, B., Bennett, C. L., & Guyatt, G. H. (2018) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.13066 · Cited by 30
Berger, J. S., Haskell, L. P., Ting, W., Lurie, F., Chang, S., Mueller, L. A., Elder, K., Rich, K., Crivera, C., Schein, J. R., et al. (2020) · The EPMA Journal · doi:10.1007/s13167-019-00196-9 · Cited by 30
Ryu, S. M., Seo, S. W., & Lee, S. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-1008-4 · Cited by 30
Understanding Early Childhood Obesity via Interpretation of Machine Learning Model Predictions
Pang, X., Forrest, C. B., Lê‐Scherban, F., & Masino, A. J. (2019) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2019.00235 · Cited by 30
Thrall, J. H., Fessell, D. P., & Pandharipande, P. V. (2021) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.07.010 · Cited by 30
Hur, S., Min, J. Y., Yoo, J., Kim, K., Chung, C. R., Dykes, P. C., & Cha, W. C. (2021) · Journal of Medical Internet Research · doi:10.2196/23508 · Cited by 30
Medical Technologies Past and Present: How History Helps to Understand the Digital Era
Rampton, V., Böhmer, M., & Winkler, A. (2021) · Journal of Medical Humanities · doi:10.1007/s10912-021-09699-x · Cited by 30
Appropriate use of machine learning in healthcare
Ozaydin, B., Berner, E. S., & Cimino, J. J. (2021) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2021.100041 · Cited by 30
Machine Learning and Statistical Prediction of Pitching Arm Kinetics
Nicholson, K. F., Collins, P. G. S., Waterman, B. R., & Bullock, G. S. (2021) · The American Journal of Sports Medicine · doi:10.1177/03635465211054506 · Cited by 30
Modelling of longitudinal data to predict cardiovascular disease risk: a methodological review
Stevens, D., Lane, D. A., Harrison, S. L., Lip, G. Y. H., & Kolamunnage‐Dona, R. (2021) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01472-x · Cited by 30
Li, X., Wang, Y., & Xu, J. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.07.045 · Cited by 30
Artificial intelligence in melanoma: A systematic review
Zhang, S., Wang, Y., Zheng, Q., Li, J., Huang, J., & Long, X. (2022) · Journal of Cosmetic Dermatology · doi:10.1111/jocd.15323 · Cited by 30
Wen, J., Liu, C.-F., Chung, M., & Tsai, Y.-C. (2022) · Taiwanese Journal of Obstetrics and Gynecology · doi:10.1016/j.tjog.2021.11.038 · Cited by 30
Gao, Y., Li, S., Jin, Y., Zhou, L., Sun, S., Xu, X., Li, S., Yang, H., Zhang, Q., & Wang, Y. (2022) · JMIR Public Health and Surveillance · doi:10.2196/35750 · Cited by 30
Applications of machine learning in microbial natural product drug discovery
Arnold, A., Alexander, J., Liu, G., & Stokes, J. M. (2023) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2023.2251400 · Cited by 30
The performance of artificial intelligence in prostate magnetic resonance imaging screening
Owida, H. A., Hassan, M. R., Ali, A. M., Alnaimat, F., Sharah, A. A., Abuowaida, S. F. A., & Alshdaifat, N. (2024) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v14i2.pp2234-2241 · Cited by 30
Huesch, M. D., Cherian, R. A., Labib, S., & Mahraj, R. P. M. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.12.017 · Cited by 30
Fang, G., Annis, I. E., Elston-Lafata, J., & Cykert, S. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz036 · Cited by 30
What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support SystemsPreprint
Lahav, O., Mastronarde, N., & van der Schaar, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.10799 · Cited by 30
Predicting outcomes of psychotherapy for depression with electronic health record data
Coley, R. Y., Boggs, J. M., Beck, A., & Simon, G. E. (2021) · Journal of Affective Disorders Reports · doi:10.1016/j.jadr.2021.100198 · Cited by 29
Challenges of big data analytics for sustainable supply chains in healthcare – a resource-based view
Hussain, M., Ajmal, M. M., Subramanian, G., Khan, M., & Anas, S. (2023) · Benchmarking An International Journal · doi:10.1108/bij-06-2022-0390 · Cited by 29
Elhakim, M. T., Stougaard, S. W., Graumann, O., Nielsen, M., Lång, K., Gerke, O., Larsen, L. B., & Rasmussen, B. S. (2023) · Cancer Imaging · doi:10.1186/s40644-023-00643-x · Cited by 29
Joy, Z. H., Rahman, M. M., Uzzaman, A., & Maraj, M. A. A. (2024) · GLOBAL MAINSTREAM JOURNAL · doi:10.62304/ijhm.v1i3.162 · Cited by 29
Prescription Precision: A Comprehensive Review of Intelligent Prescription Systems
Tantray, J., Patel, A., Wani, S. N., Kosey, S., & Prajapati, B. G. (2024) · Current Pharmaceutical Design · doi:10.2174/0113816128321623240719104337 · Cited by 29
Manager Appraisal of Artificial Intelligence Investments
Queiroz, M., Anand, A., & Baird, A. M. (2024) · Journal of Management Information Systems · doi:10.1080/07421222.2024.2376383 · Cited by 29
Sensor Networks and Data Management in Healthcare: Emerging Technologies and New Challenges
Pike, M., Mustafa, N., Towey, D., & Brusić, V. (2019) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2019.00123 · Cited by 29
Han, S., Yang, X., Zhou, Q., Jing, Z., & Wu, W. (2020) · Cancer Medicine · doi:10.1002/cam4.3289 · Cited by 29
Ronzio, L., Cabitza, F., Barbaro, A., & Banfi, G. (2021) · Diagnostics · doi:10.3390/diagnostics11020372 · Cited by 29
Sun, X., Douiri, A., & Gulliford, M. C. (2022) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2022.01.009 · Cited by 29
Wang, J. J., Singh, R. K., Miselis, H. H., & Stapleton, S. (2022) · JMIR Medical Education · doi:10.2196/32183 · Cited by 29
Efficacy of Emerging Technologies to Manage Childhood Obesity
Alotaibi, M., Alnajjar, F., Cappuccio, M. L., Khalid, S., Alhmiedat, T. A., & Mubin, O. (2022) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s357176 · Cited by 29
Improving child health through Big Data and data science
Vesoulis, Z. A., Husain, A. N., & Cole, F. S. (2022) · Pediatric Research · doi:10.1038/s41390-022-02264-9 · Cited by 29
Proposal of SVM Utility Kernel for Breast Cancer Survival Estimation
Arya, N., Mathur, A., Saha, S., & Saha, S. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3198879 · Cited by 29
Fahmy, D. M., Alksas, A., Elnakib, A. A., Mahmoud, A. H., Kandil, H., Khalil, A., Ghazal, M. A., Bogaert, E. V., Contractor, S. G., & El-Baz, A. S. (2022) · Cancers · doi:10.3390/cancers14246123 · Cited by 29
Toward best practices in embedded ethics: Suggestions for interdisciplinary technology development
Tigard, D. W., Braun, M., Breuer, S., Ritt, K., Fiske, A., McLennan, S., & Buyx, A. (2023) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2023.104467 · Cited by 29
AI-Enhanced Culturally Sensitive Public Health Messaging: A Scoping Review
Davies, G. K., Davies, M. L. K., Adewusi, E., Moneke, K. C., Adeleke, O., Mosaku, L. A., Oboh, A., Shaba, D. S., Isa, A. K., Egbedimame, J., et al. (2024) · E-Health Telecommunication Systems and Networks · doi:10.4236/etsn.2024.134004 · Cited by 29
Breathomics and treatable traits for chronic airway diseases
Brinkman, P., der Zee, A. H. M., & Wagener, A. H. (2018) · Current Opinion in Pulmonary Medicine · doi:10.1097/mcp.0000000000000534 · Cited by 29
When a robot teaches humans: Automated feedback selection accelerates motor learning
Rauter, G., Gerig, N., Sigrist, R., Riener, R., & Wolf, P. (2019) · Science Robotics · doi:10.1126/scirobotics.aav1560 · Cited by 29
On Sharing Models Instead of Data using Mimic learning for Smart Health Applications
Baza, M., Salazar, A., Abdallah, M. M., Abdallah, M. M., & Akkaya, K. (2020) · IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) · doi:10.1109/iciot48696.2020.9089457 · Cited by 29
Exploiting complex medical data with interpretable deep learning for adverse drug event prediction
Rebane, J. T., Samsten, I., & Papapetrou, P. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101942 · Cited by 29
Addressing Measurement Error in Random Forests Using Quantitative Bias Analysis
Jiang, T., Gradus, J. L., Lash, T. L., & Fox, M. P. (2021) · American Journal of Epidemiology · doi:10.1093/aje/kwab010 · Cited by 29
Artificial Intelligence (AI) and rheumatology: a potential partnership
Kothari, S., Gionfrida, L., Bharath, A. A., & Abraham, S. (2019) · Rheumatology · doi:10.1093/rheumatology/kez194 · Cited by 29
Stagg, B. C., Stein, J. D., Medeiros, F. A., Wirostko, B. M., Crandall, A. S., Hartnett, M. E., Cummins, M. R., Morris, A. H., Hess, R., & Kawamoto, K. (2020) · Ophthalmology Glaucoma · doi:10.1016/j.ogla.2020.08.006 · Cited by 29
Perioperative intelligence: applications of artificial intelligence in perioperative medicine
Maheshwari, K., Ruetzler, K., & Saugel, B. (2020) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-019-00379-9 · Cited by 28
The Environmental Costs of Artificial Intelligence for Healthcare
Katirai, A. (2024) · Asian Bioethics Review · doi:10.1007/s41649-024-00295-4 · Cited by 28
Piotrowska, U., Tsoi, J. K. H., Singh, P., Banerjee, A., & Sobczak, M. (2025) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.5c01062 · Cited by 28
Jayakumar, P., Rathouz, P. J., Lin, E., Trutner, Z. D., Uhler, L. M., Andrawis, J. P., Koenig, K. M., Tsevat, J., & Bozic, K. J. (2025) · EClinicalMedicine · doi:10.1016/j.eclinm.2025.103545 · Cited by 28
Infectious Disease Surveillance in the Era of Big Data and AI: Opportunities and Pitfalls
Idahor, C. O., Esomu, E.-J. O., Ogbonna, N., Momoh, Z., Ogbeide, O. A., Ikhu-Omoregbe, O., Adigwe, A., Erhabor, O. M., Osaghae, O., & Orons, N. (2025) · Cureus · doi:10.7759/cureus.93929 · Cited by 29
Operational calibration: debugging confidence errors for DNNs in the field
Li, Z., Ma, X., Xu, C., Xu, J., Cao, C., & Lü, J. (2020) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3368089.3409696 · Cited by 28
Neurophysiological Correlates of Concussion: Deep Learning for Clinical Assessment
Boshra, R., Ruiter, K. I., DeMatteo, C. A., Reilly, J. P., & Connolly, J. F. (2019) · Scientific Reports · doi:10.1038/s41598-019-53751-9 · Cited by 28
Radiomics in diffusion data: a test–retest, inter- and intra-reader DWI phantom study
Dreher, C., Kuder, T. A., König, F., Mlynarska-Bujny, A., Tenconi, C., Paech, D., Schlemmer, H., Ladd, M. E., & Bickelhaupt, S. (2020) · Clinical Radiology · doi:10.1016/j.crad.2020.06.024 · Cited by 28
Molecular Imaging in Primary Staging of Prostate Cancer Patients: Current Aspects and Future Trends
Manafi‐Farid, R., Ranjbar, S., Araghi, Z. J., Pilz, J., Schweighofer-Zwink, G., Pirich, C., & Beheshti, M. (2021) · Cancers · doi:10.3390/cancers13215360 · Cited by 28
Edwards, C. R., Chamunyonga, C., Searle, B., & Reddan, T. (2022) · Ultrasound · doi:10.1177/1742271x211072473 · Cited by 28
Klemt, C., Laurencin, S. J., Alpaugh, K., Tirumala, V., Barghi, A., Yeo, I., Subih, M. A., & Kwon, Y. (2022) · Journal of the American Academy of Orthopaedic Surgeons · doi:10.5435/jaaos-d-21-01039 · Cited by 28
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
Measuring PET Spatial Resolution Using a Cylinder Phantom Positioned at an Oblique Angle
Lodge, M. A., Leal, J. P., Rahmim, A., Sunderland, J. J., & Frey, E. C. (2018) · Journal of Nuclear Medicine · doi:10.2967/jnumed.118.209593 · Cited by 28
Xue, Y., Liang, H., Norbury, J., Gillis, R., & Killingworth, B. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.09.009 · Cited by 28
Prognostic models in primary biliary cholangitis
Cristoferi, L., Nardi, A., Ronca, V., Invernizzi, P., Mells, G. F., & Carbone, M. (2018) · Journal of Autoimmunity · doi:10.1016/j.jaut.2018.10.024 · Cited by 28
Your data is gold – Data donation for better healthcare?
Strotbaum, V., Pobiruchin, M., Schreiweis, B., Wiesner, M., & Strahwald, B. (2019) · it - Information Technology · doi:10.1515/itit-2019-0024 · Cited by 28
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
