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Lost in thought—the limits of the human mind and the future of medicine
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Interview with Dr. Ziad Obermeyer on how collaboration between doctors and computers will help improve medical care. (11:26)Download The complexity of medicine now exceeds the capacity of the human mind. Computers are the solution, but using them to manage this complexity will require fundamental changes in the way we think about thinking, and in the structure of medical education and research.
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“Humans are not only incapable of this level of exposure or retention, but the magnitude has also created substantial levels of stress-induced mental illness among learners (57).”
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Identification of type 2 diabetes subgroups through topological analysis of patient similarity
Li, L., Cheng, W., Glicksberg, B. S., Gottesman, O., Tamler, R., Chen, R., Böttinger, E. P., & Dudley, J. T. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aaa9364 · Cited by 554
Does Machine Learning Automate Moral Hazard and Error?
Mullainathan, S., & Obermeyer, Z. (2017) · American Economic Review · doi:10.1257/aer.p20171084 · Cited by 156
Self-citationObermeyer, Z., Cohn, B., Wilson, M., Jena, A. B., & Cutler, D. (2017) · BMJ · doi:10.1136/bmj.j239 · Cited by 103
Self-citationComputationally Generated Cardiac Biomarkers for Risk Stratification After Acute Coronary Syndrome
Syed, Z., Stultz, C. M., Scirica, B. M., & Guttag, J. V. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002557 · Cited by 31
Research works printed but not linked: 1
- Institute of Medicine. Improving diagnosis in health care. Washington, DC: National Academies Press, 2015.
Cited by
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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
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
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Ghassemi, M., Naumann, T., Schulam, P., Beam, A. L., Chen, I. Y., & Ranganath, R. (2018) · PubMed · doi:10.48550/arxiv.1806.00388 · Cited by 368
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Kolachalama, V. B., & Garg, P. S. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0061-1 · Cited by 320
Big Data and Data Science in Critical Care
Sanchez-Pinto, L. N., Luo, Y., & Churpek, M. M. (2018) · CHEST Journal · doi:10.1016/j.chest.2018.04.037 · Cited by 296
Reimagining Medical Education in the Age of AI
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Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?
Thompson, R. F., Valdés, G., Fuller, C. D., Carpenter, C. M., Morin, O., Aneja, S., Lindsay, W. D., Aerts, H. J. W. L., Agrimson, B., Deville, C., et al. (2018) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2018.05.030 · Cited by 274
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Bibault, J., Giraud, P., Housset, M., Durdux, C., Taı̈eb, J., Berger, A., Coriat, R., Chaussade, S., Dousset, B. E., Nordlinger, B., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-30657-6 · Cited by 229
Forrest, I. S., Petrazzini, B. O., Duffy, Á., Park, J. K., Márquez‐Luna, C., Jordan, D. M., Rocheleau, G., Cho, J. H., Rosenson, R. S., Narula, J., et al. (2022) · The Lancet · doi:10.1016/s0140-6736(22)02079-7 · Cited by 182
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Yun, J., Park, J. E., Lee, H., Ham, S., Kim, N., & Kim, H. S. (2019) · Scientific Reports · doi:10.1038/s41598-019-42276-w · Cited by 113
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Rush, B. N. M., Celi, L. A., & Stone, D. J. (2018) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-018-0219-z · Cited by 96
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Balagurunathan, Y., Mitchell, R., & Naqa, I. E. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.024 · Cited by 94
McWilliams, C., Lawson, D. J., Santos‐Rodríguez, R., Gilchrist, I. D., CHAMPNEYS, A. R., Gould, T. H., Thomas, M. J., & Bourdeaux, C. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-025925 · Cited by 92
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Brunner, M., McGregor, D., Keep, M., Janssen, A. L., Spallek, H., Quinn, D., Jones, A., Tseris, E. J., Yeung, W., Togher, L., et al. (2018) · Journal of Medical Internet Research · doi:10.2196/10229 · Cited by 87
Ting, D. S. W., Cheung, C. Y.-L., Nguyen, Q., Sabanayagam, C., Lim, G., Lim, Z. W., Tan, G. S. W., Soh, Y. Q., Schmetterer, L. F., Wang, Y. X., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0097-x · Cited by 85
Blease, C., Kharko, A., Bernstein, M., Bradley, C., Houston, M., Walsh, I., Hägglund, M., DesRoches, C., & Mandl, K. D. (2022) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100480 · Cited by 81
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Corazza, G. R., Formagnana, P., & Lenti, M. V. (2018) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2018.11.009 · Cited by 74
Cabitza, F., & Zeitoun, J.-D. (2019) · Annals of Translational Medicine · doi:10.21037/atm.2019.04.07 · Cited by 72
Pamporaki, C., Berends, A. M. A., Filippatos, A., Prodanov, T., Meuter, L., Prejbisz, A., Beuschlein, F., Faßnacht, M., Timmers, H. J. L. M., Nölting, S., et al. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00094-8 · Cited by 70
Chaturvedi, U., Chauhan, S. B., & Singh, I. (2025) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.12.003 · Cited by 104
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
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Sheth, S. A., Giancardo, L., Colasurdo, M., Srinivasan, V. M., Niktabe, A., & Kan, P. T. M. (2022) · Journal of NeuroInterventional Surgery · doi:10.1136/neurintsurg-2021-018142 · Cited by 62
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Nie, K., Al‐Hallaq, H., Li, X. A., Benedict, S., Sohn, J. W., Moran, J. M., Yong Fan, A., Huang, M., Knopp, M. V., Michalski, J. M., et al. (2019) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2019.01.087 · Cited by 63
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Lee, K.-S., Jang, J.-Y., Yu, Y.-D., Heo, J. S., Han, H.-S., Yoon, Y.-S., Kang, C. M., Hwang, H. K., & Kang, S. (2021) · International Journal of Surgery · doi:10.1016/j.ijsu.2021.106050 · Cited by 55
Seccia, R., Gammelli, D., Dominici, F., Romano, S., Landi, A. C., Salvetti, M., Tacchella, A., Zaccaria, A., Crisanti, A., Grassi, F., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230219 · Cited by 60
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Burmester, G. R. (2018) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2017-212888 · Cited by 53
Cosgriff, C. V., Celi, L. A., & Stone, D. J. (2019) · Biomedical Engineering and Computational Biology · doi:10.1177/1179597219856564 · Cited by 52
Ahmed, Z. (2022) · Emerging Topics in Life Sciences · doi:10.1042/etls20210244 · Cited by 52
Dong, T., Yang, C., Cui, B., Zhang, T., Sun, X., Song, K., Wang, L., Kong, B., & Yang, X. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00464 · Cited by 46
Dong, J., Geng, Y., Lu, D., Li, B., Tian, L., Lin, D. C.-C., & Zhang, Y. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01629 · Cited by 46
Piccialli, F., Calabrò, F., Crisci, D., Cuomo, S., Prezioso, E., Mandile, R., Troncone, R., Greco, L., & Auricchio, R. (2021) · Scientific Reports · doi:10.1038/s41598-021-84951-x · Cited by 47
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Fiske, A., Buyx, A., & Prainsack, B. (2018) · Academic Medicine · doi:10.1097/acm.0000000000002395 · Cited by 50
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Morris, A. H., Stagg, B. C., Lanspa, M. J., Orme, J. F., Clemmer, T. P., Weaver, L. K., Thomas, F., Grissom, C. K., Hirshberg, E. L., East, T. D., et al. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa294 · Cited by 42
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Ermers, N. J., Hagoort, K., & Scheepers, F. E. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00472 · Cited by 41
Schenck, E. J., Hoffman, K. L., Cusick, M. M., Kabariti, J., Sholle, E., & Campion, T. R. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103789 · Cited by 38
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Wang, A. Q., Karaman, B. K., Kim, H., Rosenthal, J., Saluja, R., Young, S. I., & Sabuncu, M. R. (2024) · IEEE Access · doi:10.1109/access.2024.3387702 · Cited by 41
Ouchi, K., Lindvall, C. J., Chai, P. R., & Boyer, E. W. (2018) · Journal of Medical Toxicology · doi:10.1007/s13181-018-0667-3 · Cited by 34
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Balsano, C., Alisi, A., Brunetto, M. R., Invernizzi, P., Burra, P., Piscaglia, F., Alvaro, D., Bonino, F., Carbone, M., Faita, F., et al. (2021) · Digestive and Liver Disease · doi:10.1016/j.dld.2021.06.011 · Cited by 33
González‐Nóvoa, J. A., Campanioni, S., Busto, L., Fariña, J., Rodriguez-Andina, J. J., Vila, D., Íñiguez, A., & Veiga, C. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20043455 · Cited by 30
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Silverman, A. L., Shung, D. L., Stidham, R. W., Kochhar, G. S., & Iacucci, M. (2024) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2024.05.048 · Cited by 30
Impact of big data on oral health outcomes
Nanayakkara, S., Zhou, X., & Spallek, H. (2018) · Oral Diseases · doi:10.1111/odi.13007 · Cited by 28
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Forrest, I. S., Vy, H. M. T., Rocheleau, G., Jordan, D. M., Petrazzini, B. O., Nadkarni, G. N., Cho, J. H., Ganapathi, M., Huang, K., Chung, W. K., et al. (2025) · Science · doi:10.1126/science.adm7066 · Cited by 28
Casà, C., Marotta, C., Pumpo, M. D., Cozzolino, A., D'Aviero, A., Frisicale, E. M., Silenzi, A., Gabbrielli, F., Bertinato, L., & Brusaferro, S. (2021) · Annali dell Istituto Superiore di Sanità · doi:10.4415/ann_21_01_01 · Cited by 26
Henckert, D., Malorgio, A., Schweiger, G., Raimann, F. J., Piekarski, F., Zacharowski, K. D., Hottenrott, S., Meybohm, P., Tscholl, D. W., Spahn, D. R., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12062096 · Cited by 26
Natural Language Processing of Patient-Experience Comments After Primary Total Knee Arthroplasty
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Precision Medicine: Academic dreaming or clinical reality?
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Inteligencia artificial y aprendizaje automático en medicina
Vega, M. Á., Mora, L. M. Q., & Badilla, M. V. C. (2020) · Revista Medica Sinergia · doi:10.31434/rms.v5i8.557 · Cited by 43
The Environmental Costs of Artificial Intelligence for Healthcare
Katirai, A. (2024) · Asian Bioethics Review · doi:10.1007/s41649-024-00295-4 · Cited by 28
Blom, M., Ashfaq, A., Sant’Anna, A., Anderson, P. D., & Lingman, M. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-028015 · Cited by 21
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de Souza, H. S. P., & Fiocchi, C. (2018) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izx111 · Cited by 20
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Bradley, A., Meer, R. V. D., & McKay, C. J. (2019) · Pancreas · doi:10.1097/mpa.0000000000001312 · Cited by 20
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Fiocchi, C., & Iliopoulos, D. (2021) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaa343 · Cited by 21
Zakim, D., Brandberg, H., Amrani, S. E., Hultgren, A., Stathakarou, N., Nifakos, S., Kahan, T., Spaak, J., Koch, S., & Sundberg, C. J. (2021) · PLoS ONE · doi:10.1371/journal.pone.0257677 · Cited by 20
Athreya, A. P., Iyer, R. K., Wang, L., Weinshilboum, R. M., & Bobo, W. V. (2019) · Pharmacogenomics · doi:10.2217/pgs-2019-0119 · Cited by 21
Cui, X., Chang, Y., Yang, C., Cong, Z., Wang, B., & Leng, Y. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm13010050 · Cited by 19
A Surgeon’s Guide to Machine Learning
Lammers, D. T., Eckert, C., Ahmad, M. A., Bingham, J. R., & Eckert, M. J. (2021) · Annals of Surgery Open · doi:10.1097/as9.0000000000000091 · Cited by 19
Willem, T., Krammer, S., Böhm, A. ‐., French, L. E., Hartmann, D., Lasser, T., & Buyx, A. (2022) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.18192 · Cited by 20
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Magherini, R., Mussi, E., Volpe, Y., Furferi, R., Buonamici, F., & Servi, M. (2022) · Sensors · doi:10.3390/s22134989 · Cited by 19
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Metadata
- Authors
- Obermeyer, Z., & Lee, T. H.
- Affiliations
- Brigham and Women's Hospital (US)
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2017-09-28
- Volume
- 377
- Issue
- 13
- Pages
- 1209-1211
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- PMC
- PMC5754014
- OpenAlex
- W2758348074
- Cited by
- 252
- References
- 4
- Citations by year
- 2026: 11 · 2025: 22 · 2024: 30 · 2023: 31 · 2022: 25 · 2021: 31 · 2020: 33 · 2019: 28 · 2018: 39 · 2017: 1
- FWCI
- 16.08
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (DP5OD012161)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- artificial intelligence in healthcare, human-computer collaboration, limits of human cognition, clinical complexity, medical education, medical research, future of medicine, clinical decision support
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Decision Making, Forecasting, Humans, Medicine, Thinking, Datasets as Topic
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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.
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