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.
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
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?
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.
“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]
In “Predicting the Future — Big Data, Machine Learning, and Clinical Medicine” (page unavailable), the paper distinguishes expert systems from machine learning and discusses applications in prognosis, image interpretation, and diagnosis. That supports the claim that different systems can serve different clinical purposes, but the paper does not propose a structured taxonomy or say that one helps organise the field. (LLM, conf=0.82)
Experimental — accuracy not yet measuredDiagnostic 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.
“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]
In “Predicting the Future — Big Data, Machine Learning, and Clinical Medicine” (page unavailable), the authors say machine learning can interpret radiology and pathology images, generate differential diagnoses, and find patterns in very large inputs. The paper does not specifically describe deep learning or establish laboratory and symptom-based data as inputs to diagnostic decision support; its mention of symptoms concerns prognosis. (LLM, conf=0.87)
Experimental — accuracy not yet measuredRisk stratification supports the identification of high-risk patients and enables targeted interventions and resource allocation.
“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]
The prognosis discussion in “Predicting the Future — Big Data, Machine Learning, and Clinical Medicine” (page unavailable) says machine learning can identify patient subgroups with very different mortality risks and that better estimates could inform advance care planning. It does not say that risk stratification enables targeted interventions or resource allocation, so those parts of the claim are unsupported. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIn the 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.
“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]
In “Predicting the Future — Big Data, Machine Learning, and Clinical Medicine” (page unavailable), the paper describes radiographic features and patients’ medical histories as potential inputs to prediction, and notes that clinicians interpret radiographs. It does not report a model that identified both kinds of information in a particular patient to make a prediction. The citing authors’ “our case study” is their own framing, not a case study in the cited paper. (LLM, conf=0.83)
Experimental — accuracy not yet measuredSeveral LLMs, including versions of the ChatGPT family, are employed by both EHR systems and insurance companies, creating a conflict of interest when similar datasets are used by entities with opposing objectives.
“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]
The paper’s main text discusses using EHR and insurance-claims data for machine learning, but does not address a conflict of interest between EHR systems and insurers with opposing objectives. Searches for that conflict and for insurer incentives found no supporting passage in our copy. The separate citations attached to the sentence’s LLM-use assertion are not being judged here. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Data from various healthcare settings may cause a model trained in one hospital to fail to generalise to another.
“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]
Deep learning has the potential to transform medical diagnostics.
“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]
AI is widely recognized in healthcare as a ground-breaking technological innovation that leads to better treatment outcomes.
“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]
Deep learning can identify spurious correlations but often fails to determine causation, which can be essential in a clinical context.
“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.
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 · see citing works
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 · see citing works
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 · see citing works
Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015) · American Economic Review · doi:10.1257/aer.p20151023 · Cited by 584 · see citing works
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 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Artificial intelligence in healthcare
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817 · see citing works
Scalable and accurate deep learning with electronic health records
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648 · see citing works
“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 [41].”
MethodAACR Project GENIE: Powering Precision Medicine through an International Consortium
The AACR Project GENIE Consortium, The AACR Project GENIE Consortium, André, F., Arnedos, M., Baras, A. S., Baselga, J., Bedard, P. L., Berger, M. F., Bierkens, M., Calvo, F., et al. (2017) · Cancer Discovery · doi:10.1158/2159-8290.cd-17-0151 · Cited by 1,964 · see citing works
Implementing Machine Learning in Health Care — Addressing Ethical Challenges
Char, D. S., Shah, N. H., & Magnus, D. (2018) · New England Journal of Medicine · doi:10.1056/nejmp1714229 · Cited by 1,857 · see citing works
Artificial intelligence in surgery: promises and perils
Hashimoto, D. A., Rosman, G., Rus, D., & Meireles, O. R. (2018) · Annals of Surgery · doi:10.1097/sla.0000000000002693 · Cited by 1,468 · see citing works
Can machine-learning improve cardiovascular risk prediction using routine clinical data?
Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017) · PLOS ONE · doi:10.1371/journal.pone.0174944 · Cited by 1,346 · see citing works
Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations
Chen, J. H., & Asch, S. M. (2017) · New England Journal of Medicine · doi:10.1056/nejmp1702071 · Cited by 1,171 · see citing works
Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
Aggarwal, R., Sounderajah, V., Martin, G., Ting, D. S. W., Karthikesalingam, A., King, D., Ashrafian, H., & Darzi, A. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00438-z · Cited by 946 · see citing works
<scp>eD</scp> octor: machine learning and the future of medicine
Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Lee, M. J., & Asadi, H. (2018) · Journal of Internal Medicine · doi:10.1111/joim.12822 · Cited by 956 · see citing works
Using machine learning approaches for multi-omics data analysis: A review
Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107739 · Cited by 903 · see citing works
Edge artificial intelligence for 6g: vision, enabling technologies, and applications
Letaief, K. B., Shi, Y., Lu, J., & Lu, J. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3126076 · Cited by 829 · see citing works
“This is achieved by emerging advanced technologies, including DL [311], [312], Tactile Internet, IoT, edge AI, and wireless communications.”
MethodInterpretation of Neural Networks Is Fragile
Ghorbani, A., Abid, A., & Zou, J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33013681 · Cited by 765 · see citing works
Yang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717 · see citing works
Thrall, J. H., Li, X., Li, Q., Cruz, C., Do, S., Dreyer, K., & Brink, J. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.12.026 · Cited by 710 · see citing works
Safaei, M., Sundararajan, E. A., Driss, M., Boulila, W., & Shapi'i, A. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104754 · Cited by 720 · see citing works
Introducing artificial intelligence training in medical education
Paranjape, K., Schinkel, M., Nannan Panday, R., Car, J., & Nanayakkara, P. (2019) · JMIR Medical Education · doi:10.2196/16048 · Cited by 634 · see citing works
Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021) · Applied Sciences · doi:10.3390/app11115088 · Cited by 629 · see citing works
Artificial Intelligence Transforms the Future of Health Care
Noorbakhsh-Sabet, N., Zand, R., Zhang, Y., & Abedi, V. (2019) · The American Journal of Medicine · doi:10.1016/j.amjmed.2019.01.017 · Cited by 626 · see citing works
Fairness of artificial intelligence in healthcare: review and recommendations
Ueda, D., Kakinuma, T., Fujita, S., Kamagata, K., Fushimi, Y., Ito, R., Matsui, Y., Nozaki, T., Nakaura, T., Fujima, N., et al. (2024) · Japanese Journal of Radiology · doi:10.1007/s11604-023-01474-3 · Cited by 607 · see citing works
Beyond prediction: using big data for policy problems
Athey, S. (2017) · Science · doi:10.1126/science.aal4321 · Cited by 575 · see citing works
Machine learning in medical applications: a review of state-of-the-art methods
Shehab, M., Abualigah, L., Shambour, Q., Abu-Hashem, M. A., Shambour, M. K. Y., Alsalibi, A. I., & Gandomi, A. H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105458 · Cited by 542 · see citing works
Why digital medicine depends on interoperability
Lehne, M., Sass, J., Essenwanger, A., Schepers, J., & Thun, S. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0158-1 · Cited by 501 · see citing works
Machine learning and neurosurgical outcome prediction: a systematic review
Senders, J. T., Staples, P. C., Karhade, A. V., Zaki, M. M., Gormley, W. B., Broekman, M. L. D., Smith, T. R., & Arnaout, O. (2018) · World Neurosurgery · doi:10.1016/j.wneu.2017.09.149 · Cited by 486 · see citing works
Pesapane, F., Volonté, C., Codari, M., & Sardanelli, F. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0645-y · Cited by 479 · see citing works
Larson, D. B., Chen, M. C., Lungren, M. P., Halabi, S. S., Stence, N. V., & Langlotz, C. P. (2018) · Radiology · doi:10.1148/radiol.2017170236 · Cited by 456 · see citing works
Durán, J. M., & Jongsma, K. R. (2021) · Journal of Medical Ethics · doi:10.1136/medethics-2020-106820 · Cited by 446 · see citing works
Clinical applications of machine learning algorithms: beyond the black box
Watson, D. S., Krutzinna, J., Bruce, I. N., Griffiths, C. E., McInnes, I. B., Barnes, M. R., & Floridi, L. (2019) · BMJ · doi:10.1136/bmj.l886 · Cited by 450 · see citing works
Identifying Ethical Considerations for Machine Learning Healthcare Applications
Char, D. S., Abràmoff, M. D., & Feudtner, C. (2020) · The American Journal of Bioethics · doi:10.1080/15265161.2020.1819469 · Cited by 415 · see citing works
Simon, G. E., Johnson, E., Lawrence, J. M., Rossom, R. C., Ahmedani, B., Lynch, F. L., Beck, A., Waitzfelder, B., Ziebell, R., Penfold, R. B., et al. (2018) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.17101167 · Cited by 400 · see citing works
Han, E.-R., Yeo, S., Kim, M.-J., Lee, Y.-H., Park, K.-H., & Roh, H. (2019) · BMC Medical Education · doi:10.1186/s12909-019-1891-5 · Cited by 393 · see citing works
Andaur Navarro, C. L., Damen, J. A. A., Takada, T., Nijman, S. W. J., Dhiman, P., Ma, J., Collins, G. S., Bajpai, R., Riley, R. D., Moons, K. G. M., et al. (2021) · BMJ · doi:10.1136/bmj.n2281 · Cited by 415 · see citing works
AI in Health: State of the Art, Challenges, and Future Directions
Wang, F., & Preininger, A. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677908 · Cited by 398 · see citing works
Schaffter, T., Buist, D. S. M., Lee, C. I., Nikulin, Y., Ribli, D., Guan, Y., Lotter, W., Jie, Z., Du, H., Wang, S., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.0265 · Cited by 409 · see citing works
Federated learning enables big data for rare cancer boundary detection
Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.-H., Reina, G. A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33407-5 · Cited by 394 · see citing works
Machine learning and integrative analysis of biomedical big data
Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P. (2019) · Genes · doi:10.3390/genes10020087 · Cited by 380 · see citing works
Explainable, trustworthy, and ethical machine learning for healthcare: A survey
Rasheed, K., Qayyum, A., Ghaly, M., Al-Fuqaha, A., Razi, A., & Qadir, J. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106043 · Cited by 372 · see citing works
Axes of a revolution: challenges and promises of big data in healthcare
Shilo, S., Rossman, H., & Segal, E. (2020) · Nature Medicine · doi:10.1038/s41591-019-0727-5 · Cited by 370 · see citing works
Ethical implications of AI and robotics in healthcare: A review
Elendu, C., Amaechi, D. C., Elendu, T. C., Jingwa, K. A., Okoye, O. K., John Okah, M., Ladele, J. A., Farah, A. H., & Alimi, H. A. (2023) · Medicine · doi:10.1097/md.0000000000036671 · Cited by 370 · see citing works
“[8] By examining these ethical dimensions, this manuscript seeks to provide a comprehensive understanding of the complex ethical landscape surrounding AI and robotics in health-care.”
BackgroundPrecision medicine in the era of artificial intelligence: implications in chronic disease management
Subramanian, M., Wojtusciszyn, A., Favre, L., Boughorbel, S., Shan, J., Letaief, K. B., Pitteloud, N., & Chouchane, L. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02658-5 · Cited by 362 · see citing works
Machine learning for real-time prediction of complications in critical care: a retrospective study
Meyer, A., Zverinski, D., Pfahringer, B., Kempfert, J., Kuehne, T., Sündermann, S. H., Stamm, C., Hofmann, T., Falk, V., & Eickhoff, C. (2018) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(18)30300-x · Cited by 350 · see citing works
Blease, C., Kaptchuk, T. J., Bernstein, M. H., Mandl, K. D., Halamka, J. D., & DesRoches, C. M. (2019) · Journal of Medical Internet Research · doi:10.2196/12802 · Cited by 346 · see citing works
Machine learning and medical education
Kolachalama, V. B., & Garg, P. S. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0061-1 · Cited by 320 · see citing works
Strohm, L., Hehakaya, C., Ranschaert, E. R., Boon, W. P. C., & Moors, E. H. M. (2020) · European Radiology · doi:10.1007/s00330-020-06946-y · Cited by 329 · see citing works
Artificial Intelligence in Undergraduate Medical Education: A Scoping Review
Lee, J., Wu, A. S., Li, D., & Kulasegaram, K. (. (2021) · Academic Medicine · doi:10.1097/acm.0000000000004291 · Cited by 324 · see citing works
Predicting hospital admission at emergency department triage using machine learning
Hong, W. S., Haimovich, A. D., & Taylor, R. A. (2018) · PLOS ONE · doi:10.1371/journal.pone.0201016 · Cited by 326 · see citing works
Balki, I., Amirabadi, A., Levman, J., Martel, A. L., Emersic, Z., Meden, B., Garcia-Pedrero, A., Ramirez, S. C., Kong, D., Moody, A. R., et al. (2019) · Canadian Association of Radiologists Journal · doi:10.1016/j.carj.2019.06.002 · Cited by 299 · see citing works
A systematic survey of computer-aided diagnosis in medicine: Past and present developments
Yanase, J., & Triantaphyllou, E. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112821 · Cited by 303 · see citing works
Artificial Intelligence and Patient-Centered Decision-Making
Bjerring, J. C., & Busch, J. (2021) · Philosophy & Technology · doi:10.1007/s13347-019-00391-6 · Cited by 274 · see citing works
Recht, M. P., Dewey, M., Dreyer, K., Langlotz, C., Niessen, W., Prainsack, B., & Smith, J. J. (2020) · European Radiology · doi:10.1007/s00330-020-06672-5 · Cited by 268 · see citing works
Big data and black-box medical algorithms
Price, W. N. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aao5333 · Cited by 254 · see citing works
Human–machine partnership with artificial intelligence for chest radiograph diagnosis
Patel, B. N., Rosenberg, L., Willcox, G., Baltaxe, D., Lyons, M., Irvin, J., Rajpurkar, P., Amrhein, T., Gupta, R., Halabi, S., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0189-7 · Cited by 254 · see citing works
Artificial intelligence and the future of psychiatry: Insights from a global physician survey
Doraiswamy, P. M., Blease, C., & Bodner, K. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101753 · Cited by 253 · see citing works
Artificial intelligence-based methods for fusion of electronic health records and imaging data
Mohsen, F., Ali, H., El Hajj, N., & Shah, Z. (2022) · Scientific Reports · doi:10.1038/s41598-022-22514-4 · Cited by 250 · see citing works
Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature
Triantafyllidis, A. K., & Tsanas, A. (2019) · Journal of Medical Internet Research · doi:10.2196/12286 · Cited by 243 · see citing works
Regulation of predictive analytics in medicine
Parikh, R. B., Obermeyer, Z., & Navathe, A. S. (2019) · Science · doi:10.1126/science.aaw0029 · Cited by 223 · see citing works
Self-citationPublic Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media
Gao, S., He, L., Chen, Y., Li, D., & Lai, K. (2020) · Journal of Medical Internet Research · doi:10.2196/16649 · Cited by 221 · see citing works
Homecare robotic systems for healthcare 4.0: visions and enabling technologies
Yang, G., Pang, Z., Jamal Deen, M., Dong, M., Zhang, Y.-T., Lovell, N., & Rahmani, A. M. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2990529 · Cited by 213 · see citing works
Henry, K. E., Kornfield, R., Sridharan, A., Linton, R. C., Groh, C., Wang, T., Wu, A., Mutlu, B., & Saria, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00597-7 · Cited by 207 · see citing works
Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review
Huang, J., Galal, G., Etemadi, M., & Vaidyanathan, M. (2022) · JMIR Medical Informatics · doi:10.2196/36388 · Cited by 186 · see citing works
Gao, Y., Zeng, S., Xu, X., Li, H., Yao, S., Song, K., Li, X., Chen, L., Tang, J., Xing, H., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00278-8 · Cited by 185 · see citing works
Nair, M., Svedberg, P., Larsson, I., & Nygren, J. M. (2024) · PLOS ONE · doi:10.1371/journal.pone.0305949 · Cited by 179 · see citing works
An introduction and overview of machine learning in neurosurgical care
Senders, J. T., Zaki, M. M., Karhade, A. V., Chang, B., Gormley, W. B., Broekman, M. L., Smith, T. R., & Arnaout, O. (2018) · Acta Neurochirurgica · doi:10.1007/s00701-017-3385-8 · Cited by 178 · see citing works
Deep learning in pharmacogenomics: from gene regulation to patient stratification
Kalinin, A. A., Higgins, G. A., Reamaroon, N., Soroushmehr, S., Allyn-Feuer, A., Dinov, I. D., Najarian, K., & Athey, B. D. (2018) · Pharmacogenomics · doi:10.2217/pgs-2018-0008 · Cited by 207 · see citing works
Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics
Wood, A., Najarian, K., & Kahrobaei, D. (2021) · ACM Computing Surveys · doi:10.1145/3394658 · Cited by 179 · see citing works
Artificial Intelligence: Threat or Boon to Radiologists?
Recht, M., & Bryan, R. N. (2017) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.07.007 · Cited by 172 · see citing works
Shashikumar, S. P., Stanley, M. D., Sadiq, I., Li, Q., Holder, A., Clifford, G. D., & Nemati, S. (2017) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2017.08.013 · Cited by 167 · see citing works
A Clinician's Guide to Artificial Intelligence: How to Critically Appraise Machine Learning Studies
Faes, L., Liu, X., Wagner, S. K., Fu, D. J., Balaskas, K., Sim, D. A., Bachmann, L. M., Keane, P. A., & Denniston, A. K. (2020) · Translational Vision Science & Technology · doi:10.1167/tvst.9.2.7 · Cited by 162 · see citing works
Big data in IBD: a look into the future
Olivera, P., Danese, S., Jay, N., Natoli, G., & Peyrin-Biroulet, L. (2019) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-019-0102-5 · Cited by 151 · see citing works
Putting the data before the algorithm in big data addressing personalized healthcare
Cahan, E. M., Hernandez-Boussard, T., Thadaney-Israni, S., & Rubin, D. L. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0157-2 · Cited by 149 · see citing works
Big data and medical research in China
Zhang, L., Wang, H., Li, Q., Zhao, M.-H., & Zhan, Q.-M. (2018) · BMJ · doi:10.1136/bmj.j5910 · Cited by 148 · see citing works
Jha, N., Shankar, P. R., Al-Betar, M. A., Mukhia, R., Hada, K., & Palaian, S. (2022) · Advances in Medical Education and Practice · doi:10.2147/amep.s368519 · Cited by 143 · see citing works
Artificial intelligence: Essentials for nursing
McGrow, K. (2019) · Nursing · doi:10.1097/01.nurse.0000577716.57052.8d · Cited by 146 · see citing works
Factors associated with healthy aging in Latin American populations
Santamaria-Garcia, H., Sainz-Ballesteros, A., Hernandez, H., Moguilner, S., Maito, M., Ochoa-Rosales, C., Corley, M., Valcour, V., Miranda, J. J., Lawlor, B., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02495-1 · Cited by 141 · see citing works
Bertsimas, D., Kung, J., Trichakis, N., Wang, Y., Hirose, R., & Vagefi, P. A. (2019) · American Journal of Transplantation · doi:10.1111/ajt.15172 · Cited by 141 · see citing works
Artificial intelligence in pancreatic cancer
Huang, B., Huang, H., Zhang, S., Zhang, D., Shi, Q., Liu, J., & Guo, J. (2022) · Theranostics · doi:10.7150/thno.77949 · Cited by 142 · see citing works
“The most significant advantage of ML is the ability to integrate vast amounts of data and combine the observed and predicted quantities in nonlinear and highly interactive ways [46].”
BackgroundReinertsen, E., & Clifford, G. D. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aabf64 · Cited by 136 · see citing works
Merath, K., Hyer, J. M., Mehta, R., Farooq, A., Bagante, F., Sahara, K., Tsilimigras, D. I., Beal, E., Paredes, A. Z., Wu, L., et al. (2020) · Journal of Gastrointestinal Surgery · doi:10.1007/s11605-019-04338-2 · Cited by 131 · see citing works
Pavuluri, S., Sangal, R., Sather, J., & Taylor, R. A. (2024) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2024-101120 · Cited by 129 · see citing works
Blease, C., Locher, C., Leon-Carlyle, M., & Doraiswamy, M. (2020) · DIGITAL HEALTH · doi:10.1177/2055207620968355 · Cited by 121 · see citing works
Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review
Borchert, R. J., Azevedo, T., Badhwar, A., Bernal, J., Betts, M., Bruffaerts, R., Burkhart, M. C., Dewachter, I., Gellersen, H. M., Low, A., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13412 · Cited by 125 · see citing works
Finding missed cases of familial hypercholesterolemia in health systems using machine learning
Banda, J. M., Sarraju, A., Abbasi, F., Parizo, J., Pariani, M., Ison, H., Briskin, E., Wand, H., Dubois, S., Jung, K., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0101-5 · Cited by 123 · see citing works
“Machine-learning algorithms can analyze large datasets and determine combinations of variables that consistently classify or predict a certain outcome.(9) Such models have been widely applied in non-medical fields(10) with nascent but promising use in medicine.”
BackgroundAttitudes of Patients and Their Relatives Toward Artificial Intelligence in Neurosurgery
Palmisciano, P., Jamjoom, A. A. B., Taylor, D., Stoyanov, D., & Marcus, H. J. (2020) · World Neurosurgery · doi:10.1016/j.wneu.2020.03.029 · Cited by 113 · see citing works
Deep learning in biomedical data science
Baldi, P. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013343 · Cited by 119 · see citing works
Applications of machine learning in routine laboratory medicine: Current state and future directions
Rabbani, N., Kim, G. Y. E., Suarez, C. J., & Chen, J. H. (2022) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2022.02.011 · Cited by 118 · see citing works
Nitski, O., Azhie, A., Qazi-Arisar, F. A., Wang, X., Ma, S., Lilly, L., Watt, K. D., Levitsky, J., Asrani, S. K., Lee, D. S., et al. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00040-6 · Cited by 110 · see citing works
Approaching autonomy in medical artificial intelligence
Bitterman, D. S., Aerts, H. J. W. L., & Mak, R. H. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30187-4 · Cited by 107 · see citing works
From images to actions: Opportunities for artificial intelligence in radiology
Kahn, C. E. (2017) · Radiology · doi:10.1148/radiol.2017171734 · Cited by 88 · see citing works
Advancements in AI based healthcare techniques with FOCUS ON diagnostic techniques
Kalra, N., Verma, P., & Verma, S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108917 · Cited by 95 · see citing works
(2019) · AMA Journal of Ethics · doi:10.1001/amajethics.2019.138 · Cited by 89 · see citing works
Gebrael, G., Sahu, K. K., Chigarira, B., Tripathi, N., Mathew Thomas, V., Sayegh, N., Maughan, B. L., Agarwal, N., Swami, U., & Li, H. (2023) · Cancers · doi:10.3390/cancers15143717 · Cited by 85 · see citing works
Biology and medicine in the landscape of quantum advantages
Cordier, B. A., Sawaya, N. P. D., Guerreschi, G. G., & McWeeney, S. K. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2022.0541 · Cited by 87 · see citing works
De Silva, D., Ranasinghe, W., Bandaragoda, T., Adikari, A., Mills, N., Iddamalgoda, L., Alahakoon, D., Lawrentschuk, N., Persad, R., Osipov, E., et al. (2018) · PLOS ONE · doi:10.1371/journal.pone.0205855 · Cited by 86 · see citing works
Advancing Alzheimer’s research: a review of big data promises
Zhang, R., Simon, G., & Yu, F. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.07.002 · Cited by 77 · see citing works
Observational studies and their utility for practice
Gilmartin-Thomas, J. F., Liew, D., & Hopper, I. (2018) · Australian Prescriber · doi:10.18773/austprescr.2018.017 · Cited by 82 · see citing works
Should artificial intelligence augment medical decision making? The case for an autonomy algorithm
(2018) · AMA Journal of Ethics · doi:10.1001/amajethics.2018.902 · Cited by 78 · see citing works
Foy, B. H., Sundt, T. M., Carlson, J. C. T., Aguirre, A. D., & Higgins, J. M. (2022) · Nature Communications · doi:10.1038/s41467-022-32222-2 · Cited by 84 · see citing works
London, A. J. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100622 · Cited by 76 · see citing works
Cloud-based federated learning implementation across medical centers
Rajendran, S., Obeid, J. S., Binol, H., D`Agostino, R., Foley, K., Zhang, W., Austin, P., Brakefield, J., Gurcan, M. N., & Topaloglu, U. (2021) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00060 · Cited by 76 · see citing works
Issa, W. B., Shorbagi, A., Al-Sharman, A., Rababa, M., Al-Majeed, K., Radwan, H., Refaat Ahmed, F., Al-Yateem, N., Mottershead, R., Abdelrahim, D. N., et al. (2024) · BMC Medical Education · doi:10.1186/s12909-024-06076-9 · Cited by 78 · see citing works
AI and professional liability assessment in healthcare. a revolution in legal medicine?
Terranova, C., Cestonaro, C., Fava, L., & Cinquetti, A. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2023.1337335 · Cited by 73 · see citing works
- 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 · 2012: 1
- 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
