Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Artificial intelligence in healthcare

Nature Biomedical Engineering volume 2, pages 719–731 (2018)Cite this article

Subjects

Abstract

Artificial intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be only the province of human experts. In this Review Article, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and summarize the economic, legal and social implications of AI in healthcare.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Supervised and unsupervised machine learning.
Fig. 2: The general architecture of artificial neural networks.
Fig. 3: Models of information flow in conventional clinical practice, conventional decision support systems, integrative decision support systems and fully automated clinical systems.
Fig. 4: Example of the interpretation of convolutional neural networks.

Similar content being viewed by others

References

  1. Simonite, T. Google’s AI eye doctor gets ready to go to work in India. WIRED (6 August 2017).

  2. Lee, R., Wong, T. Y. & Sabanayagam, C. Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss. Eye Vis. 2, 17 (2015).

    Article  Google Scholar 

  3. Lin, D. Y., Blumenkranz, M. S., Brothers, R. J. & Grosvenor, D. M. The sensitivity and specificity of single-field nonmydriatic monochromatic digital fundus photography with remote image interpretation for diabetic retinopathy screening: a comparison with ophthalmoscopy and standardized mydriatic color photography. Am. J. Ophthalmol. 134, 204–213 (2002).

    Article  PubMed  Google Scholar 

  4. Zheng, Y., He, M. & Congdon, N. The worldwide epidemic of diabetic retinopathy. Indian J. Ophthalmol. 60, 428–431 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410 (2016).

    Article  PubMed  Google Scholar 

  6. Poplin, R. et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat. Biomed. Eng. 2, 158–164 (2018).

    Article  PubMed  Google Scholar 

  7. Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N. & Folk, J. C. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit. Med. 1, 39 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Russell, S. J. & Norvig, P. Artificial Intelligence: A Modern Approach (Prentice Hall, New Jersey, 2010).

  9. Krizhevsky, A., Sutskever, I. & Hinton, G. E. in Advances in Neural Information Processing Systems 1097–1105 (Curran Associates, Nevada, 2012).

  10. Lewis-Kraus, G. The great A.I. awakening. The New York Times Magazine (14 December 2016).

  11. Kundu, M., Nasipuri, M. & Basu, D. K. Knowledge-based ECG interpretation: a critical review. Pattern Recognit. 33, 351–373 (2000).

    Article  Google Scholar 

  12. Jha, S. & Topol, E. J. Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA 316, 2353–2354 (2016).

    Article  PubMed  Google Scholar 

  13. Golub, T. R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999).

    Article  CAS  PubMed  Google Scholar 

  14. Wang, Y. et al. Gene selection from microarray data for cancer classification—a machine learning approach. Comput. Biol. Chem. 29, 37–46 (2005).

    Article  PubMed  CAS  Google Scholar 

  15. Yu, K. H. et al. Predicting ovarian cancer patients’ clinical response to platinum-based chemotherapy by their tumor proteomic signatures. J. Proteome Res. 15, 2455–2465 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Yu, K. H. et al. Omics AnalySIs System for PRecision Oncology (OASISPRO): a web-based omics analysis tool for clinical phenotype prediction. Bioinformatics 34, 319–320 (2017).

    Article  CAS  PubMed Central  Google Scholar 

  17. Check Hayden, E. The automated lab. Nature 516, 131–132 (2014).

    Article  CAS  PubMed  Google Scholar 

  18. Miller, R. A. Medical diagnostic decision support systems–past, present, and future: a threaded bibliography and brief commentary. J. Am. Med. Inform. Assoc. 1, 8–27 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Musen, M. A., Middleton, B. & Greenes, R. A. in Biomedical Informatics (eds Shortliffe, E. H. & Cimino, J. J.) 643–674 (Springer, London, 2014).

  20. Shortliffe, E. Computer-Based Medical Consultations: MYCIN Vol. 2 (Elsevier, New York, 2012).

    Chapter  Google Scholar 

  21. Szolovits, P., Patil, R. S. & Schwartz, W. B. Artificial intelligence in medical diagnosis. Ann. Intern. Med. 108, 80–87 (1988).

    Article  CAS  PubMed  Google Scholar 

  22. de Dombal, F. T., Leaper, D. J., Staniland, J. R., McCann, A. P. & Horrocks, J. C. Computer-aided diagnosis of acute abdominal pain. Br. Med. J. 2, 9–13 (1972).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Shortliffe, E. H. et al. Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. Comput. Biomed. Res. 8, 303–320 (1975).

    Article  CAS  PubMed  Google Scholar 

  24. Barnett, G. O., Cimino, J. J., Hupp, J. A. & Hoffer, E. P. DXplain. An evolving diagnostic decision-support system. JAMA 258, 67–74 (1987).

    Article  CAS  PubMed  Google Scholar 

  25. Miller, R. A., McNeil, M. A., Challinor, S. M., Masarie, F. E. Jr & Myers, J. D. The INTERNIST-1/QUICK MEDICAL REFERENCE Project — status report. Western J. Med. 145, 816–822 (1986).

  26. Berner, E. S. et al. Performance of four computer-based diagnostic systems. N. Engl. J. Med. 330, 1792–1796 (1994).

    Article  CAS  PubMed  Google Scholar 

  27. Szolovits, P. & Pauker, S. G. Categorical and probabilistic reasoning in medical diagnosis. Artif. Intell. 11, 115–144 (1978).

    Article  Google Scholar 

  28. Deo, R. C. Machine learning in medicine. Circulation 132, 1920–1930 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Yu, K. H. & Snyder, M. Omics profiling in precision oncology. Mol. Cell. Proteomics 15, 2525–2536 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Roberts, K. et al. Biomedical informatics advancing the national health agenda: the AMIA 2015 year-in-review in clinical and consumer informatics. J. Am. Med. Inform. Assoc. 24, 185–190 (2017).

    Article  Google Scholar 

  31. Cloud AutoML ALPHA (Google Cloud); https://cloud.google.com/automl/

  32. Goodfellow, I., Bengio, Y., Courville, A. & Bengio, Y. Deep Learning 1 (MIT Press, Cambridge, 2016).

    Google Scholar 

  33. Gill, N. S. Overview and applications of artificial neural networks. Xenonstack https://www.xenonstack.com/blog/data-science/artificial-neural-networks-applications-algorithms/ (2017).

  34. TOP500 List – November 2006 (TOP500); https://www.top500.org/list/2006/11/

  35. Beam, A. L. & Kohane, I. S. Translating artificial intelligence into clinical care. JAMA 316, 2368–2369 (2016).

    Article  PubMed  Google Scholar 

  36. Kamentsky, L. et al. Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software. Bioinformatics 27, 1179–1180 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ching, T. et al. Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15, 20170387 (2018).

  38. Tomczak, K., Czerwinska, P. & Wiznerowicz, M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol. 19, 68–77 (2015).

    Google Scholar 

  39. Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Ljosa, V., Sokolnicki, K. L. & Carpenter, A. E. Annotated high-throughput microscopy image sets for validation. Nat. Methods 9, 637 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Williams, E. et al. The image data resource: a bioimage data integration and publication platform. Nat. Methods 14, 775–781 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. DesRoches, C. M. et al. Electronic health records in ambulatory care–a national survey of physicians. N. Engl. J. Med. 359, 50–60 (2008).

    Article  CAS  PubMed  Google Scholar 

  43. Hsiao, C. J. et al. Office-based physicians are responding to incentives and assistance by adopting and using electronic health records. Health Aff. 32, 1470–1477 (2013).

    Article  Google Scholar 

  44. Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Beck, A. H. et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci. Transl. Med. 3, 108ra113 (2011).

    Article  PubMed  Google Scholar 

  46. Yu, K. H. et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat. Commun. 7, 12474 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Shademan, A. et al. Supervised autonomous robotic soft tissue surgery. Sci. Transl. Med. 8, 337ra364 (2016).

    Article  Google Scholar 

  48. Reed, J. C. Chest Radiology: Plain Film Patterns and Differential Diagnoses (Elsevier Health Sciences, Philadelphia, 2010).

  49. Lodwick, G. S., Haun, C. L., Smith, W. E., Keller, R. F. & Robertson, E. D. Computer diagnosis of primary bone tumors: a preliminary report. Radiology 80, 273–275 (1963).

    Article  Google Scholar 

  50. van Ginneken, B., Setio, A. A., Jacobs, C. & Ciompi, F. Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. In IEEE 12th International Symposium Biomedical Imaging (ISBI) 286–289 (IEEE, 2015).

  51. Lakhani, P. & Sundaram, B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284, 574–582 (2017).

    Article  PubMed  Google Scholar 

  52. Wang, X. et al. ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Preprint at https://arxiv.org/abs/1705.02315 (2017).

  53. Yao, L. et al. Learning to diagnose from scratch by exploiting dependencies among labels. Preprint at https://arxiv.org/abs/1710.10501 (2017).

  54. Rajpurkar, P. et al. CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. Preprint at https://arxiv.org/abs/1711.05225 (2017).

  55. Samala, R. K. et al. Mass detection in digital breast tomosynthesis: deep convolutional neural network with transfer learning from mammography. Med. Phys. 43, 6654–6666 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Arevalo, J., González, F. A., Ramos-Pollán, R., Oliveira, J. L. & Lopez, M. A. G. Convolutional neural networks for mammography mass lesion classification. In IEEE 37th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC) 797–800 (IEEE, 2015).

  57. 510(k) Premarket Notification (FDA, 2017); https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K163253

  58. Marr, B. First FDA approval for clinical cloud-based deep learning in healthcare. Forbes (20 January 2017).

  59. Rigel, D. S., Friedman, R. J., Kopf, A. W. & Polsky, D. ABCDE—an evolving concept in the early detection of melanoma. Arch. Dermatol. 141, 1032–1034 (2005).

    Article  PubMed  Google Scholar 

  60. Thomas, L. et al. Semiological value of ABCDE criteria in the diagnosis of cutaneous pigmented tumors. Dermatology 197, 11–17 (1998).

    Article  CAS  PubMed  Google Scholar 

  61. Ercal, F., Chawla, A., Stoecker, W. V., Lee, H. C. & Moss, R. H. Neural network diagnosis of malignant melanoma from color images. IEEE Trans. Biomed. Eng. 41, 837–845 (1994).

    Article  CAS  PubMed  Google Scholar 

  62. Wolf, J. A. et al. Diagnostic inaccuracy of smartphone applications for melanoma detection. JAMA Dermatol. 149, 422–426 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Panwar, N. et al. Fundus photography in the 21st century — a review of recent technological advances and their implications for worldwide healthcare. Telemed. J. E. Health 22, 198–208 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  64. American Diabetes Association. 10. Microvascular complications and foot care. Diabetes Care 40, 88–98 (2017).

    Article  Google Scholar 

  65. Menke, A., Casagrande, S., Geiss, L. & Cowie, C. C. Prevalence of and trends in diabetes among adults in the United States, 1988–2012. JAMA 314, 1021–1029 (2015).

    Article  CAS  PubMed  Google Scholar 

  66. Abràmoff, M. D. et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Investigative Opthalmology Visual Sci. 57, 5200–5206 (2016).

    Article  Google Scholar 

  67. Rorke, L. B. Pathologic diagnosis as the gold standard. Cancer 79, 665–667 (1997).

    Article  CAS  PubMed  Google Scholar 

  68. Lakhani, S. R. & Ashworth, A. Microarray and histopathological analysis of tumours: the future and the past? Nat. Rev. Cancer 1, 151–157 (2001).

    Article  CAS  PubMed  Google Scholar 

  69. Rubegni, P. et al. Automated diagnosis of pigmented skin lesions. Int. J. Cancer 101, 576–580 (2002).

    Article  CAS  PubMed  Google Scholar 

  70. Stang, A. et al. Diagnostic agreement in the histopathological evaluation of lung cancer tissue in a population-based case-control study. Lung Cancer 52, 29–36 (2006).

    Article  PubMed  Google Scholar 

  71. Yu, K. H. et al. Association of omics features with histopathology patterns in lung adenocarcinoma. Cell Syst. 5, 620–627 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Litjens, G. et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6, 26286 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Bejnordi, B. E. et al. Machine learning detection of breast cancer lymph node metastases. JAMA 318, 2199–2210 (2017).

    Article  Google Scholar 

  74. Cireşan, D. C., Giusti, A., Gambardella, L. M. & Schmidhuber, J. in Medical Image Computing and Computer-Assisted Intervention — MICCAI 2013 (eds Mori, K. et al.) 411–418 (Springer, Berlin, Heidelberg, 2013).

  75. Manak, M. S. et al. Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-018-0285-z (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Robboy, S. J. et al. Pathologist workforce in the United States: I. Development of a predictive model to examine factors influencing supply. Arch. Pathol. Lab. Med. 137, 1723–1732 (2013).

    Article  PubMed  Google Scholar 

  77. Litjens, G. et al. A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017).

    Article  PubMed  Google Scholar 

  78. Quang, D., Chen, Y. & Xie, X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics 31, 761–763 (2015).

    Article  CAS  PubMed  Google Scholar 

  79. Quang, D. & Xie, X. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res. 44, e107 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. DePristo, M. & Poplin, R. DeepVariant: highly accurate genomes with deep neural networks. Google AI Blog https://research.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html (2017).

  81. Poplin, R. et al. Creating a universal SNP and small indel variant caller with deep neural networks. Preprint at https://www.biorxiv.org/content/early/2016/12/14/092890 (2018).

  82. Kamps, R. et al. Next-generation sequencing in oncology: genetic diagnosis, risk prediction and cancer classification. Int. J. Mol. Sci. 18, 308 (2017).

    Article  PubMed Central  CAS  Google Scholar 

  83. He, Z. & Yu, W. Stable feature selection for biomarker discovery. Comput. Biol. Chem. 34, 215–225 (2010).

    Article  CAS  PubMed  Google Scholar 

  84. Zhang, Z. et al. Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer. Cancer Res. 64, 5882–5890 (2004).

    Article  CAS  PubMed  Google Scholar 

  85. Wallden, B. et al. Development and verification of the PAM50-based Prosigna breast cancer gene signature assay. BMC Med. Genomics 8, 54 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Sweeney, T. E., Wong, H. R. & Khatri, P. Robust classification of bacterial and viral infections via integrated host gene expression diagnostics. Sci. Transl. Med. 8, 346ra391 (2016).

    Article  CAS  Google Scholar 

  87. Huang, T., Hoffman, B., Meschino, W., Kingdom, J. & Okun, N. Prediction of adverse pregnancy outcomes by combinations of first and second trimester biochemistry markers used in the routine prenatal screening of Down syndrome. Prenat. Diagn. 30, 471–477 (2010).

    PubMed  Google Scholar 

  88. Mook, S. et al. Metastatic potential of T1 breast cancer can be predicted by the 70-gene MammaPrint signature. Ann. Surg. Oncol. 17, 1406–1413 (2010).

    Article  PubMed  Google Scholar 

  89. Farina, D. et al. Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation. Nat. Biomed. Eng. 1, 0025 (2017).

    Article  Google Scholar 

  90. Altman, R. B. Artificial intelligence (AI) systems for interpreting complex medical datasets. Clin. Pharmacol. Ther. 101, 585–586 (2017).

    Article  CAS  PubMed  Google Scholar 

  91. Cai, X. et al. Real-time prediction of mortality, readmission, and length of stay using electronic health record data. J. Am. Med. Inform. Assoc. 23, 553–561 (2016).

    Article  PubMed  Google Scholar 

  92. Makar, M., Ghassemi, M., Cutler, D. M. & Obermeyer, Z. Short-term mortality prediction for elderly patients using medicare claims data. Int. J. Mach. Learn. Comput. 5, 192–197 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  93. Ng, T., Chew, L. & Yap, C. W. A clinical decision support tool to predict survival in cancer patients beyond 120 days after palliative chemotherapy. J. Palliat. Med. 15, 863–869 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Delen, D., Oztekin, A. & Kong, Z. J. A machine learning-based approach to prognostic analysis of thoracic transplantations. Artif. Intell. Med. 49, 33–42 (2010).

    Article  PubMed  Google Scholar 

  95. Churpek, M. M. et al. Predicting cardiac arrest on the wards: a nested case-control study. Chest 141, 1170–1176 (2012).

    Article  PubMed  Google Scholar 

  96. Churpek, M. M. et al. Multicenter development and validation of a risk stratification tool for ward patients. Am. J. Respir. Crit. Care Med. 190, 649–655 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Lundberg, S. M. et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-018-0304-0 (2018).

  98. Li, X. et al. Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biol. 15, e2001402 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  99. Majumder, S., Mondal, T. & Deen, M. J. Wearable sensors for remote health monitoring. Sensors 17, 130 (2017).

    Article  PubMed Central  Google Scholar 

  100. Pastorino, M., Arredondo, M., Cancela, J. & Guillen, S. Wearable sensor network for health monitoring: the case of Parkinson disease. J. Phys. Conf. Ser. 450, 012055 (2013).

    Article  Google Scholar 

  101. Mercer, K., Li, M., Giangregorio, L., Burns, C. & Grindrod, K. Behavior change techniques present in wearable activity trackers: a critical analysis. JMIR Mhealth Uhealth 4, e40 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Takacs, J. et al. Validation of the Fitbit One activity monitor device during treadmill walking. J. Sci. Med. Sport 17, 496–500 (2014).

    Article  PubMed  Google Scholar 

  103. Yang, R., Shin, E., Newman, M. W. & Ackerman, M. S. When fitness trackers don’t ‘fit’: end-user difficulties in the assessment of personal tracking device accuracy. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing 623–634 (ACM, 2015).

  104. Endeavour Partners. Inside wearables: how the science of human behavior change offers the secret to long-term engagement. Medium https://blog.endeavour.partners/inside-wearable-how-the-science-of-human-behavior-change-offers-the-secret-to-long-term-engagement-a15b3c7d4cf3 (2017).

  105. Herz, J. C. Wearables are totally failing the people who need them most. Wired (11 June 2014).

  106. Clawson, J., Pater, J. A., Miller, A. D., Mynatt, E. D. & Mamykina, L. No longer wearing: investigating the abandonment of personal health-tracking technologies on Craigslist. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing 647–658 (ACM, 2015).

  107. Wheeler, M. J. Overview on robotics in the laboratory. Ann. Clin. Biochem. 44, 209–218 (2007).

    Article  CAS  PubMed  Google Scholar 

  108. Moustris, G. P., Hiridis, S. C., Deliparaschos, K. M. & Konstantinidis, K. M. Evolution of autonomous and semi-autonomous robotic surgical systems: a review of the literature. Int. J. Med. Robot. 7, 375–392 (2011).

    Article  CAS  PubMed  Google Scholar 

  109. Gomes, P. Surgical robotics: reviewing the past, analysing the present, imagining the future. Robot. Comput. Integr. Manuf. 27, 261–266 (2011).

    Article  Google Scholar 

  110. Majdani, O. et al. A robot-guided minimally invasive approach for cochlear implant surgery: preliminary results of a temporal bone study. Int. J. Comput. Assist. Radiol. Surg. 4, 475–486 (2009).

    Article  PubMed  Google Scholar 

  111. Elek, R. et al. Recent trends in automating robotic surgery. In 2016 IEEE 20th Jubilee International Conference on Intelligent Engineering Systems (INES) 27–32 (IEEE, 2016).

  112. Liew, C. The future of radiology augmented with artificial intelligence: a strategy for success. Eur. J. Radiol. 102, 152–156 (2018).

    Article  PubMed  Google Scholar 

  113. Jones, L., Golan, D., Hanna, S. & Ramachandran, M. Artificial intelligence, machine learning and the evolution of healthcare: a bright future or cause for concern? Bone Joint Res. 7, 223–225 (2018).

    Article  CAS  Google Scholar 

  114. Obermeyer, Z. & Emanuel, E. J. Predicting the future — big data, machine learning, and clinical medicine. N. Engl. J. Med. 375, 1216–1219 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  115. Krause, J. et al. Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy. Ophthalmology 125, 1264–1272 (2018).

    Article  PubMed  Google Scholar 

  116. Rebholz-Schuhmann, D. et al. The CALBC silver standard corpus for biomedical named entities—a study in harmonizing the contributions from four independent named entity taggers. In LREC 568–573 (2010).

  117. Kirby, J. C. et al. PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability. J. Am. Med. Inform. Assoc. 23, 1046–1052 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  118. Simonyan, K., Vedaldi, A. & Zisserman, A. Deep inside convolutional networks: visualising image classification models and saliency maps. Preprint at https://arxiv.org/abs/1312.6034 (2013).

  119. Ribeiro, M. T., Singh, S. & Guestrin, C. “Why should I trust you?”: explaining the predictions of any classifier. Preprint at https://arxiv.org/abs/1602.04938 (2016).

  120. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article  CAS  PubMed  Google Scholar 

  121. Boulanger-Lewandowski, N., Bengio, Y. & Vincent, P. Modeling temporal dependencies in high-dimensional sequences: application to polyphonic music generation and transcription. Preprint at https://arxiv.org/abs/1206.6392 (2012).

  122. Zoph, B. & Le, Q. V. Neural architecture search with reinforcement learning. Preprint at https://arxiv.org/abs/1611.01578 (2016).

  123. Lee, L. M. & Gostin, L. O. Ethical collection, storage, and use of public health data: a proposal for a national privacy protection. JAMA 302, 82–84 (2009).

    Article  CAS  PubMed  Google Scholar 

  124. Narayan, S., Gagné, M. & Safavi-Naini, R. Privacy preserving EHR system using attribute-based infrastructure. In Proceedings of the 2010 ACM Workshop on Cloud Computing Security Workshop 47–52 (ACM, 2010).

  125. Dolin, R. H. et al. HL7 Clinical Document Architecture, Release 2. J. Am. Med. Inform. Assoc. 13, 30–39 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  126. Mandl, K. D. & Kohane, I. S. Escaping the EHR trap—the future of health IT. N. Engl. J. Med. 366, 2240–2242 (2012).

  127. Mandel, J. C., Kreda, D. A., Mandl, K. D., Kohane, I. S. & Ramoni, R. B. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J. Am. Med. Inform. Assoc. 23, 899–908 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  128. All eyes are on AI. Nat. Biomed. Eng. 2, 139 (2018).

  129. Yu, K. H. & Kohane I. S. Framing the challenges of artificial intelligence in medicine. BMJ Qual. Safety https://doi.org/10.1136/bmjqs-2018-008551 (2018).

  130. Dignum, V. Ethics in artificial intelligence: introduction to the special issue. Ethics Inf. Technol. 20, 1–3 (2018).

    Article  Google Scholar 

  131. Price, I. & Nicholson, W. Artificial Intelligence in Health Care: Applications and Legal Implications (Univ. Michigan Law School, 2017).

  132. Mukherjee, S. A.I. versus M.D. What happens when diagnosis is automated? The New Yorker (3 April 2017).

  133. Del Beccaro, M. A., Jeffries, H. E., Eisenberg, M. A. & Harry, E. D. Computerized provider order entry implementation: no association with increased mortality rates in an intensive care unit. Pediatrics 118, 290–295 (2006).

    Article  PubMed  Google Scholar 

  134. Longhurst, C. A. et al. Decrease in hospital-wide mortality rate after implementation of a commercially sold computerized physician order entry system. Pediatrics 126, 14–21 (2010).

    Article  PubMed  Google Scholar 

  135. Carspecken, C. W., Sharek, P. J., Longhurst, C. & Pageler, N. M. A clinical case of electronic health record drug alert fatigue: consequences for patient outcome. Pediatrics 131, 1970–1973 (2013).

    Article  Google Scholar 

  136. Ash, J. S., Berg, M. & Coiera, E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J. Am. Med. Inform. Assoc. 11, 104–112 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  137. Lehman, C. D. et al. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern. Med. 175, 1828–1837 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  138. Koppel, R. et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA 293, 1197–1203 (2005).

    Article  CAS  PubMed  Google Scholar 

  139. Middleton, B. et al. Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. J. Am. Med. Inform. Assoc. 20, 2–8 (2013).

    Article  Google Scholar 

  140. Gottlieb, S. Twitter (12 April 2018); https://twitter.com/SGottliebFDA/status/984378648781312002

  141. Digital Health Software Precertification (Pre-Cert) Program (FDA); https://www.fda.gov/MedicalDevices/DigitalHealth/DigitalHealthPreCertProgram/default.htm

  142. Estrin, D. & Sim, I. Open mHealth architecture: an engine for health care innovation. Science 330, 759–760 (2010).

    Article  CAS  PubMed  Google Scholar 

  143. Shortliffe, E. H. Computer programs to support clinical decision making. JAMA 258, 61–66 (1987).

    Article  CAS  PubMed  Google Scholar 

  144. Armbruster, D. A., Overcash, D. R. & Reyes, J. Clinical chemistry laboratory automation in the 21st century—amat victoria curam (victory loves careful preparation). Clin. Biochem. Rev. 35, 143–153 (2014).

  145. Rosenfeld, L. A golden age of clinical chemistry: 1948–1960. Clin. Chem. 46, 1705–1714 (2000).

    Article  CAS  PubMed  Google Scholar 

  146. Kuperman, G. J. et al. Medication-related clinical decision support in computerized provider order entry systems: a review. J. Am. Med. Inform. Assoc. 14, 29–40 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  147. Glassman, P. A., Simon, B., Belperio, P. & Lanto, A. Improving recognition of drug interactions: benefits and barriers to using automated drug alerts. Med. Care 40, 1161–1171 (2002).

    Article  PubMed  Google Scholar 

  148. FDA permits marketing of artificial intelligence algorithm for aiding providers in detecting wrist fractures. https://www.fda.gov/newsevents/newsroom/pressannouncements/ucm608833.htm (FDA, 2018).

  149. Haenssle, H. A. et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann. Oncol. 29, 1836–1842 (2018).

    Article  CAS  PubMed  Google Scholar 

  150. Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Preprint at https://arxiv.org/abs/1409.1556 (2014).

  151. Murphy, K. P. & Bach F. Machine Learning: A Probabilistic Perspective (MIT Press, Cambridge, 2012).

Download references

Acknowledgements

K.-H.Y. is supported by a Harvard Data Science Postdoctoral Fellowship. I.S.K. was supported in part by the NIH grant OT3OD025466. Figure 4 was generated by using the computational infrastructure supported by the AWS Cloud Credits for Research, the Microsoft Azure Research Award, and the NVIDIA GPU Grant Programme.

Author information

Authors and Affiliations

  1. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Kun-Hsing Yu, Andrew L. Beam & Isaac S. Kohane

  2. Boston Children’s Hospital, Boston, MA, USA

    Isaac S. Kohane

Authors
  1. Kun-Hsing Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew L. Beam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Isaac S. Kohane
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K.-H.Y. conceived and designed the article, performed the literature review and wrote and revised the manuscript. A.L.B. and I.S.K. edited the manuscript. I.S.K. supervised the work.

Corresponding author

Correspondence to Isaac S. Kohane.

Ethics declarations

Competing interests

Harvard Medical School (K.-H.Y.) submitted a provisional patent application on digital pathology profiling to the United States Patent and Trademark Office (USPTO).

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, KH., Beam, A.L. & Kohane, I.S. Artificial intelligence in healthcare. Nat Biomed Eng 2, 719–731 (2018). https://doi.org/10.1038/s41551-018-0305-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41551-018-0305-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing