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.
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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]
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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
Research works printed but not linked: 1
- Mullainathan S, J Econ Perspect (in press)
Cited by
Sorted by citations
Items 1,001–1,011 of 1,011
Li, Y., Jin, N., Zhan, Q., Huang, Y., Sun, A., Yin, F., Li, Z., Hu, J., & Liu, Z. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1495306 · Cited by 22
“Recently, machine learning (ML) has demonstrated considerable advantages in the early detection and effective management of diseases (14 – 18).”
Enhancing sepsis management through machine learning techniques: A review
Ocampo-Quintero, N., Vidal-Cortés, P., del Río Carbajo, L., Fdez-Riverola, F., Reboiro-Jato, M., & Glez-Peña, D. (2022) · Medicina Intensiva · doi:10.1016/j.medin.2020.04.003 · Cited by 20
Santamaría-García, H., Baez, S., Aponte-Canencio, D. M., Pasciarello, G. O., Donnelly-Kehoe, P. A., Maggiotti, G., Matallana, D., Hesse, E., Neely, A., Zapata, J. G., et al. (2021) · Patterns · doi:10.1016/j.patter.2020.100176 · Cited by 18
Lammert, J. M., Roberts, A. C., McRae, K., Batterink, L. J., & Butler, B. E. (2025) · Journal of Speech, Language, and Hearing Research · doi:10.1044/2024_jslhr-24-00515 · Cited by 17
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. (2020) · doi:10.1101/2020.08.17.20171769 · Cited by 67
Evaluation of knowledge and attitudes of radiology department workers about artificial intelligence
Yurdaisik, I., & Aksoy, S. H. (2021) · Annals of Clinical and Analytical Medicine · doi:10.4328/acam.20453 · Cited by 13
“The majority of AI experts think that the developments of this technology will increase the effectiveness of healthcare in the medical field and diagnostic accuracy and will reduce the workload of physicians [6-8].”
BackgroundCaliForest: calibrated random forest for health data
Park, Y., & Ho, J. C. (2020) · Proceedings of the ACM Conference on Health, Inference, and Learning · doi:10.1145/3368555.3384461 · Cited by 12
Blease, C., Kharko, A., Bernstein, M., Bradley, C., Houston, M., Walsh, I., & D Mandl, K. (2023) · JMIR Medical Education · doi:10.2196/42639 · Cited by 10
Artificial intelligence in extended perioperative medicine
Bignami, E., Darhour, L. J., & Bellini, V. (2024) · Trends in Anaesthesia and Critical Care · doi:10.1016/j.tacc.2024.101376 · Cited by 4
Alderson, P. O., Donlin, M. J., & Morrison, L. A. (2021) · doi:10.1101/2021.05.13.21255493 · Cited by 3
Patil, S. S., Jha, G. K., Mohsin, N., Patel, P., Ralte, L., & Shukla, A. K. (2026) · Cureus · doi:10.7759/cureus.109905
“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].”
Influential
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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