Skip to main content

Thank you for visiting 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.

High-performance medicine: the convergence of human and artificial intelligence


The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient–doctor relationship or facilitate its erosion remains to be seen.

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

Access options

Rent or buy this article

Prices vary by article type



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

Fig. 1

Debbie Maizels/Springer Nature

Fig. 2: Examples of AI applications across the human lifespan.

Debbie Maizels/Springer Nature

Fig. 3: The virtual medical coach model with multi-modal data inputs and algorithms to provide individualized guidance.

Debbie Maizels/Springer Nature

Fig. 4: Call for due process of AI studies in medicine.

Debbie Maizels/Springer Nature

Fig. 5: The analogy between self-driving cars and medicine.

Debbie Maizels/Springer Nature


  1. Thakrar, A. P. et al. Child mortality in the US and 19 OECD comparator nations: a 50-year time-trend analysis. Health Aff. (Millwood) 37, 140–149 (2018).

    Google Scholar 

  2. Roser, M. Link between health spending and life expectancy: US is an outlier. In Our World in Data (2017).

  3. Singh, H. et al. The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ Qual. Saf. 23, 727–731 (2014).

    PubMed  PubMed Central  Google Scholar 

  4. Berwick, D. M. & Hackbarth, A. D. Eliminating waste in US health care. JAMA 307, 1513–1516 (2012).

    CAS  PubMed  Google Scholar 

  5. 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 (2017).

  6. Li, Z. et al. Thoracic disease identification and localization with limited supervision. Preprint at (2017).

  7. Singh, R. et al. Deep learning in chest radiography: detection of findings and presence of change. PLoS ONE 13, e0204155 (2018).

    PubMed  PubMed Central  Google Scholar 

  8. Nam, J. G. et al. Development and validation of deep learning–based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology (2018).

  9. Lindsey, R., et al. Deep neural network improves fracture detection by clinicians. Proc. Natl. Acad. Sci. USA 115, 11591–11596 (2018).

  10. Gale, W. et al. Detecting hip fractures with radiologist-level performance using deep neural networks. Preprint at (2017).

  11. Rajpurkar, P. MURA dataset: towards radiologist-level abnormality detection in musculoskeletal radiographs. Preprint at (2017).

  12. Ridley, E. L. Deep learning shows promise for bone age assessment. In Aunt Minnie (2017).

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

    PubMed  Google Scholar 

  14. Bar, A. et al. Compression fractures detection on CT. Preprint at (2017).

  15. Ridley, E. L. Deep-learning algorithm can stratify lung nodule risk. In Aunt Minnie (2017).

  16. Yasaka, K. et al. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study. Radiology 286, 887–896 (2018).

    PubMed  Google Scholar 

  17. Liu, F. et al. Joint shape representation and classification for detecting PDAC in abdominal CT scans. Preprint at (2018).

  18. Shadmi, R. et al. Fully-convolutional deep-learning based system for coronary calcium score prediction from non-contrast chest CT. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (IEEE, 2018).

  19. Arbabshirani, M. R. et al. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digit. Med. 1, 9 (2018).

  20. Chilamkurthy, S. et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 392, 2388–2396 (2018).

  21. Chilamkurthy, S. et al. Development and validation of deep learning algorithms for detection of critical findings in head CT ccans. Preprint at (2018).

  22. Lieman-Sifry, J. et al. FastVentricle: cardiac segmentation with ENet. Preprint at (2017).

  23. Madani, A.. et al. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit. Med. 1, 6 (2018).

  24. Zhang, J. et al. Fully automated echocardiogram interpretation in clinical practice feasibility and diagnostic accuracy. Circulation 138, 1623–1635 (2018).

  25. Yee, K. M. AI algorithm matches radiologists in breast screening exams. In Aunt Minnie (2017).

  26. Lehman, C. D. et al. Mammographic breast density assessment using deep learning: clinical implementation. Radiology (2018).

  27. Titano, J. J. et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat. Med. 24, 1337–1341 (2018).

  28. Saito, T. & Rehmsmeier, M. The precision–recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE 10, e0118432 (2015).

    PubMed  PubMed Central  Google Scholar 

  29. Lobo, J. et al. AUC: a misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17, 145–151 (2007).

  30. Keane, P. & Topol, E. With an eye to AI and autonomous diagnosis. NPJ Digit. Med. 1, 40 (2018).

  31. Abramoff, M. et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. N PJ Digit. Med. 1, 39 (2018).

  32. Kanagasingam, Y. et al. Evaluation of artificial intelligence–based grading of diabetic retinopathy in primary care. JAMA Netw. Open 1, e182665 (2018).

  33. Coudray, N. et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559–1567 (2018).

  34. Liu, Y. et al. Artificial intelligence–based breast cancer nodal metastasis detection. Arch. Pathol. Lab. Med. (2018).

  35. Steiner, D. F., et al. Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer. Am. J. Surg. Pathol. 42, 1636–1646 (2018).

  36. Mori, Y. et al. Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy. Ann. Intern. Med. 169, 357–366 (2018).

  37. Wang, P. et al. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat. Biomed. Eng. 2, 741–748 (2018).

    Google Scholar 

  38. Long, E. et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nat. Biomed. Eng. 1, 1–8 (2017).

    Google Scholar 

  39. Acs, B. & Rimm, D. L. Not just digital pathology, intelligent digital pathology. JAMA Oncol. 4, 403–404 (2018).

    PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Ehteshami Bejnordi, B. et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318, 2199–2210 (2017).

    PubMed  PubMed Central  Google Scholar 

  42. Golden, J. A. Deep learning algorithms for detection of lymph node metastases from breast cancer: helping artificial intelligence be seen. JAMA 318, 2184–2186 (2017).

    PubMed  Google Scholar 

  43. Cruz-Roa, A. et al. Accurate and reproducible invasive breast cancer detection in whole-slide images: a deep learning approach for quantifying tumor extent. Sci. Rep. 7, 46450 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Wong, D. & Yip, S. Machine learning classifies cancer. Nature 555, 446–447 (2018).

    CAS  PubMed  Google Scholar 

  45. Capper, D. et al. DNA methylation–based classification of central nervous system tumours. Nature 555, 469–474 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Yang, S. J. et al. Assessing microscope image focus quality with deep learning. BMC Bioinformatics 19, 77 (2018).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  48. 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).

  49. Han, S. S. et al. Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. J. Invest. Dermatol. 138, 1529–1538 (2018).

    CAS  PubMed  Google Scholar 

  50. Wong, T. Y. & Bressler, N. M. Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. JAMA 316, 2366–2367 (2016).

    PubMed  Google Scholar 

  51. 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).

    PubMed  Google Scholar 

  52. Burlina, P. M. et al. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. 135, 1170–1176 (2017).

    PubMed  PubMed Central  Google Scholar 

  53. Kermany, D. S. et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172, 1122–1131.e1129 (2018).

    CAS  PubMed  Google Scholar 

  54. Ting, D. S. W. et al. AI for medical imaging goes deep. Nat. Med. 24, 539–540 (2018).

    CAS  PubMed  Google Scholar 

  55. Rampasek, L. & Goldenberg, A. Learning from everyday images enables expert-like diagnosis of retinal diseases. Cell 172, 893–895 (2018).

    CAS  PubMed  Google Scholar 

  56. De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342–1350 (2018).

  57. Mutlu, U. et al. Association of retinal neurodegeneration on optical coherence tomography with dementia: a population-based study. JAMA Neurol. 75, 1256–1263 (2018).

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

    Google Scholar 

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

  60. Brown, J. M. et al. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol. 136, 803–810 (2018).

    PubMed  Google Scholar 

  61. Willems, J. et al. The diagnostic performance of computer programs for the interpretation of electrocardiograms. N. Engl. J. Med. 325, 1767–1773 (1991).

    CAS  PubMed  Google Scholar 

  62. Strodthoff, N. & Strodthoff, C. Detecting and interpreting myocardial infarctions using fully convolutional neural networks. Preprint at (2018).

  63. Rajpurkar, P. et al. Cardiologist-level arrhythmia detection with convolutional neural networks. Preprint at (2017).

  64. Holme, Ø. & Aabakken, L. Making colonoscopy smarter with standardized computer-aided diagnosis Ann. Intern. Med. 169, 409–410 (2018).

  65. Petrone, J. FDA approves stroke-detecting AI software. Nat. Biotechnol. 36, 290 (2018).

  66. Hsu, J. & Spectrum. AI could make detecting autism easier. In The Atlantic (2018).

  67. Lundberg, S. et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat. Biomed. Eng. 2, 749–760 (2018).

    Google Scholar 

  68. Peters, A. Having a heart attack? This AI helps emergency dispatchers find out. In Fast Company (2018).

  69. Patel, N. M. et al. Enhancing next-generation sequencing-guided cancer care through cognitive computing. Oncologist 23, 179–185 (2018).

    PubMed  Google Scholar 

  70. De Graaf, M. Will Al replace fertility doctors? Why computers are the only ones that can end the agony of failed IVF cycles, miscarriages, and risky multiple birth. In Daily Mail (2018).

  71. Gurovich, Y. et al. DeepGestalt—identifying rare genetic syndromes using deep learning. Preprint at (2017).

  72. Bahl, M. et al. High-risk breast lesions: a machine learning model to predict pathologic upgrade and reduce unnecessary surgical excision. Radiology 286, 810–818 (2018).

    PubMed  Google Scholar 

  73. Coiera, E. et al. The digital scribe. NPJ Digit. Med. 1, 58 (2018).

  74. The burden of depression. Nature 515, 163 (2014).

  75. Cao, B. et al. DeepMood: modeling mobile phone typing dynamics for mood detection. Preprint at (2018).

  76. Mohr, D. C. et al. A solution-focused research approach to achieve an implementable revolution in digital mental health. JAMA Psychiatry 75, 113–114 (2018).

    PubMed  Google Scholar 

  77. Frankel, J. How artificial intelligence could help diagnose mental disorders. In The Atlantic (2016).

  78. Barrett, P. M. et al. Digitising the mind. Lancet 389, 1877 (2017).

    PubMed  Google Scholar 

  79. Firth, J. et al. The efficacy of smartphone-based mental health interventions for depressive symptoms: a meta-analysis of randomized controlled trials. World Psychiatry 16, 287–298 (2017).

  80. Fitzpatrick, K. K. et al. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR Ment. Health 4, e19 (2017).

    PubMed  PubMed Central  Google Scholar 

  81. Eichstaedt, J. C. et al. Facebook language predicts depression in medical records. Proc. Natl. Acad. Sci. USA 115, 11203–11208 (2018).

  82. Chekroud, A. M. et al. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 3, 243–250 (2016).

    PubMed  Google Scholar 

  83. Schnyer, D. M. et al. Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder. Psychiatry Res. 264, 1–9 (2017).

    PubMed Central  Google Scholar 

  84. Reece, A. G. & Danforth, C. M. Instagram photos reveal predictive markers of depression. EPJ Data Science 6, 15 (2017).

  85. Wager, T. D. & Woo, C. W. Imaging biomarkers and biotypes for depression. Nat. Med. 23, 16–17 (2017).

    CAS  PubMed  Google Scholar 

  86. Walsh, C. G. et al. Predicting risk of suicide attempts over time through machine learning. Clin. Psychol. Sci. 5, 457–469 (2017).

  87. Franklin, J. C. et al. Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research. Psychol. Bull. 143, 187–232 (2017).

    PubMed  Google Scholar 

  88. Just, M. A. et al. Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth. Nat. Hum. Behav. 1, 911–919 (2017).

    PubMed  PubMed Central  Google Scholar 

  89. Chung, Y. et al. Use of machine learning to determine deviance in neuroanatomical maturity associated with future psychosis in youths at clinically high risk. JAMA Psychiatry 75, 960–968 (2018).

  90. Yang, Z. et al. Clinical assistant diagnosis for electronic medical record based on convolutional neural network. Sci. Rep. 8, 6329 (2018).

    PubMed  PubMed Central  Google Scholar 

  91. Avati, A. et al. Improving palliative care with deep learning. Preprint at (2017).

  92. Cleret de Langavant, L. et al. Unsupervised machine learning to identify high likelihood of dementia in population-based surveys: development and validation study. J. Med. Internet. Res. 20, e10493 (2018).

    PubMed  PubMed Central  Google Scholar 

  93. Oh, J. et al. A generalizable, data-driven approach to predict daily risk of Clostridium difficile infection at two large academic health centers. Infect. Control. Hosp. Epidemiol. 39, 425–433 (2018).

    PubMed  Google Scholar 

  94. Bennington-Castro, J. AI can predict when we’ll die—here’s why that’s a good thing. In NBC News (2018).

  95. Elfiky, A. et al. Development and application of a machine learning approach to assess short-term mortality risk among patients with cancer starting chemotherapy. JAMA Netw. Open 1, e180926 (2018).

  96. Rajkomar, A. et al. Scalable and accurate deep learning with electronic health records. NPJ Digit. Med. 1, 18 (2018).

  97. Miotto, R. et al. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci. Rep. 6, 26094 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Mathotaarachchi, S. et al. Identifying incipient dementia individuals using machine learning and amyloid imaging. Neurobiol. Aging. 59, 80–90 (2017).

    PubMed  Google Scholar 

  99. Yoon, J. et al. Personalized survival predictions via Trees of Predictors: an application to cardiac transplantation. PLoS ONE 13, e0194985 (2018).

    PubMed  PubMed Central  Google Scholar 

  100. Wong, A. et al. Development and validation of an electronic health record–based machine learning model to estimate delirium risk in newly hospitalized patients without known cognitive impairment. JAMA Netw. Open 1, e181018 (2018).

  101. Alaa, A. M. & van der Schaar, M. Prognostication and risk factors for cystic fibrosis via automated machine learning. Sci. Rep. 8, 11242 (2018).

    PubMed  PubMed Central  Google Scholar 

  102. Horng, S. et al. Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLoS ONE 12, e0174708 (2017).

    PubMed  PubMed Central  Google Scholar 

  103. Henry, K. E. et al. A targeted real-time early warning score (TREWScore) for septic shock. Sci. Transl. Med. 7, 299ra122 (2015).

    PubMed  Google Scholar 

  104. Culliton, P. et al. Predicting severe sepsis using text from the electronic health record. Preprint at (2017).

  105. Razavian, N. et al. Multi-task prediction of disease onsets from longitudinal lab tests. PMLR 56, 73–100 (2016).

    Google Scholar 

  106. Shameer, K. et al. Predictive modeling of hospital readmission rates using electronic medical record-wide machine learning: a case-study using Mount Sinai Heart Failure Cohort. Pac. Symp. Biocomput. 22, 276–287 (2017).

    PubMed  Google Scholar 

  107. Bhagwat, N. et al. Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease using longitudinal data. PLoS Comput. Biol. 14, e1006376 (2018).

    PubMed  PubMed Central  Google Scholar 

  108. Komorowski, M. et al. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nat. Med. 24, 1716–1720 (2018).

  109. Zaidi, D. AI is transforming medical diagnosis, prosthetics, and vision aids. In Venture Beat (2017).

  110. Putin, E. et al. Deep biomarkers of human aging: application of deep neural networks to biomarker development. Aging 8, 1021–1033 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Wang, Z. et al. Predicting age by mining electronic medical records with deep learning characterizes differences between chronological and physiological age. J. Biomed. Inform. 76, 59–68 (2017).

    PubMed  PubMed Central  Google Scholar 

  112. Horvath, S. & Raj, K. DNA methylation–based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 19, 371–384 (2018).

    CAS  PubMed  Google Scholar 

  113. Rose, S. Machine Learning for Prediction in Electronic Health Data. JAMA Netw. Open 1, e181404(2018).

  114. Haque, A. et al. Towards vision-based smart hospitals: a system for tracking and monitoring hand hygiene compliance. Preprint at (2017).

  115. Suresh, H. et al. Clinical intervention prediction and understanding with deep neural networks. Preprint at (2017).

  116. Kwolek, B. & Kepski, M. Human fall detection on embedded platform using depth maps and wireless accelerometer. Comput. Methods Programs Biomed. 117, 489–501 (2014).

    PubMed  Google Scholar 

  117. Prasad, N. et al. A reinforcement learning approach to weaning of mechanical ventilation in intensive care units. Preprint at (2018).

  118. Maier-Hein, L. et al. Surgical data science for next-generation interventions. Nat. Biomed. Eng. 1, 691–696 (2017).

    Google Scholar 

  119. Hung, A. J. et al. Automated performance metrics and machine learning algorithms to measure surgeon performance and anticipate clinical outcomes in robotic surgery. JAMA Surg. 153, 770–771 (2018).

  120. Gehlbach, P. L. Robotic surgery for the eye. Nat. Biomed. Eng. 2, 627–628 (2018).

  121. Nikolov, S. et al. Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy. Preprint at (2018).

  122. Zhu, B. et al. Image reconstruction by domain-transform manifold learning. Nature 555, 487–492 (2018).

    CAS  PubMed  Google Scholar 

  123. Harvey, H. Can AI enable a 10 minute MRI? In Towards Data Science (2018).

  124. Ridley, E. L. Artificial intelligence guides lower PET tracer dose. In Aunt Minnie (2018).

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

    PubMed  Google Scholar 

  126. Tuegel, E. J. et al. Reengineering aircraft structural life prediction using a digital twin. Int. J. Aerosp. 2011, 154798 (2011).

  127. Tarassenko, L. & Topol, E. Monitoring the health of jet engines and people. JAMA (2018).

  128. Buhr, S. FDA clears AliveCor’s Kardiaband as the first medical device accessory for the Apple Watch. In TechCrunch (2017).

  129. Victory, J. What did journalists overlook about the Apple Watch ‘heart monitor’ feature? In HealthNewsReview (2018).

  130. Fingas, R. Apple Watch Series 4 EKG tech got FDA clearance less than 24 hours before reveal. In AppleInsider (2018).

  131. Carroll, A. E. That new apple watch EKG feature? There are more downs than ups. In The New York Times (2018).

  132. Levine, B. & Brown, A. Onduo delivers diabetes clinic and coaching to your smartphone. In Diatribe (2018).

  133. Han, Q. et al. A hybrid recommender system for patient–doctor matchmaking in primary care. Preprint at (2018).

  134. Zmora, N. et al. Taking it personally: personalized utilization of the human microbiome in health and disease. Cell. Host. Microbe. 19, 12–20 (2016).

    CAS  PubMed  Google Scholar 

  135. Korem, T. et al. Bread affects clinical parameters and induces gut microbiome–associated personal glycemic responses. Cell. Metab. 25, 1243–1253 e1245 (2017).

    CAS  PubMed  Google Scholar 

  136. Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).

    CAS  PubMed  Google Scholar 

  137. Hall, H. et al. Glucotypes reveal new patterns of glucose dysregulation. PLoS Biol. 16, e2005143 (2018).

    PubMed  PubMed Central  Google Scholar 

  138. Albers, D. J. et al. Personalized glucose forecasting for type 2 diabetes using data assimilation. PLoS. Comput. Biol. 13, e1005232 (2017).

    PubMed  PubMed Central  Google Scholar 

  139. Hulman, A. et al. Glucose patterns during an oral glucose tolerance test and associations with future diabetes, cardiovascular disease and all-cause mortality rate. Diabetologia 61, 101–107 (2018).

    CAS  PubMed  Google Scholar 

  140. Thaiss, C. A. et al. Hyperglycemia drives intestinal barrier dysfunction and risk for enteric infection. Science 359, 1376–1383 (2018).

    CAS  PubMed  Google Scholar 

  141. Wu, D. et al. Glucose-regulated phosphorylation of TET2 by AMPK reveals a pathway linking diabetes to cancer. Nature 559, 637–641 (2018).

    CAS  PubMed  Google Scholar 

  142. Bally, L. et al. Closed-loop insulin delivery for glycemic control in noncritical care. N. Engl. J. Med. 379, 547–556 (2018).

    CAS  PubMed  Google Scholar 

  143. Christiansen, E. M. et al. In silico labeling: predicting fluorescent labels in unlabeled images. Cell 173, 792–803 e719 (2018).

  144. Sullivan, D. P. & Lundberg, E. Seeing more: a future of augmented microscopy. Cell 173, 546–548 (2018).

    CAS  PubMed  Google Scholar 

  145. Ounkomol, C. et al. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nat. Methods 15, 917–920 (2018).

  146. Ota, S. et al. Ghost cytometry. Science 360, 1246–1251 (2018).

    CAS  PubMed  Google Scholar 

  147. Nitta, N. et al. Intelligent image-activated cell sorting. Cell 175, 266–276 e213 (2018).

    CAS  PubMed  Google Scholar 

  148. Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Preprint at (2017).

  149. Gut, G. et al. Multiplexed protein maps link subcellular organization to cellular states. Science 361, eaar7042 (2018).

  150. Sullivan, D. P. et al. Deep learning is combined with massive-scale citizen science to improve large-scale image classification. Nat. Biotechnol. 36, 820–828 (2018).

    CAS  PubMed  Google Scholar 

  151. Poplin, R. et al. Creating a universal SNP and small indel variant caller with deep neural networks. Preprint at (2016).

  152. Sundaram, L. et al. Predicting the clinical impact of human mutation with deep neural networks. Nat. Genet. 50, 1161–1170 (2018).

    CAS  PubMed  Google Scholar 

  153. Zhou, J. et al. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat. Genet. 50, 1171–1179 (2018).

    CAS  PubMed  Google Scholar 

  154. Zhou, J. & Troyanskaya, O. G. Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12, 931–934 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  155. Luo, R., et al. Clairvoyante: a multi-task convolutional deep neural network for variant calling in single molecule sequencing. Preprint at (2018).

  156. Leung, M. et al. Machine learning in genomic medicine: a review of computational problems and data sets. In Proceedings of the IEEE Vol. 104, 176–197 (IEEE, 2016).

  157. Poplin, R. et al. A universal SNP and small-indel variant caller using deep neural networks. Nat. Biotechnol. 36, 983–987 (2018).

  158. Riesselman, A. et al. Deep generative models of genetic variation capture the effects of mutations. Nat. Methods 15, 816–822 (2018).

  159. Wood, D. E. et al. A machine learning approach for somatic mutation discovery. Sci. Transl. Med. 10, eaar7939 (2018).

  160. Behravan, H. et al. Machine learning identifies interacting genetic variants contributing to breast cancer risk: a case study in Finnish cases and controls. Sci. Rep. 8, 13149 (2018).

    PubMed  PubMed Central  Google Scholar 

  161. Lin, C. et al. Using neural networks for reducing the dimensions of single-cell RNA-seq data. Nucleic Acids Res. 45, e156 (2017).

    PubMed  PubMed Central  Google Scholar 

  162. Angermueller, C. et al. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome. Biol. 18, 67 (2017).

    PubMed  PubMed Central  Google Scholar 

  163. AlQuraishi, M. End-to-end differentiable learning of protein structure. Preprint at (2018).

  164. Espinoza, J. L. Machine learning for tackling microbiota data and infection complications in immunocompromised patients with cancer. J. Intern. Med. (2018).

  165. van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729.e727 (2018).

    PubMed  Google Scholar 

  166. Zitnik, M. et al. Machine learning for integrating data in biology and medicine: principles, practice, and opportunities. Preprint at (2018).

  167. Camacho, D. M. et al. Next-generation machine learning for biological networks. Cell 173, 1581–1592 (2018).

    CAS  PubMed  Google Scholar 

  168. Kim, H. K. et al. Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity. Nat. Biotechnol. 36, 239–241 (2018).

    CAS  PubMed  Google Scholar 

  169. Listgarten, J. et al. Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs. Nat. Biomed. Eng. 2, 38–47 (2018).

    PubMed  PubMed Central  Google Scholar 

  170. Caravagna, G. et al. Detecting repeated cancer evolution from multi-region tumor sequencing data. Nat. Methods 15, 707–714 (2018).

    CAS  PubMed  Google Scholar 

  171. Manak, M. et al. Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning. Nature Biomed. Eng. 2, 761–772 (2018).

  172. Hassabis, D. et al. Neuroscience-inspired artificial intelligence. Neuron 95, 245–258 (2017).

    CAS  PubMed  Google Scholar 

  173. Robie, A. A. et al. Mapping the neural substrates of behavior. Cell 170, 393–406 e328 (2017).

    CAS  PubMed  Google Scholar 

  174. Dasgupta, S. et al. A neural algorithm for a fundamental computing problem. Science 358, 793–796 (2017).

    CAS  PubMed  Google Scholar 

  175. Januszewski, M. et al. High-precision automated reconstruction of neurons with flood-filling networks. Nat. Methods 15, 605–610 (2018).

    CAS  PubMed  Google Scholar 

  176. Savelli, F. & Knierim, J. J. AI mimics brain codes for navigation. Nature 557, 313–314 (2018).

    CAS  PubMed  Google Scholar 

  177. Banino, A. et al. Vector-based navigation using grid-like representations in artificial agents. Nature 557, 429–433 (2018).

    CAS  PubMed  Google Scholar 

  178. Adam, G. C. Two artificial synapses are better than one. Nature 558, 39–40 (2018).

    CAS  PubMed  Google Scholar 

  179. Wright, C. D. Phase-change devices: crystal-clear neuronal computing. Nat. Nanotechnol. 11, 655–656 (2016).

    CAS  PubMed  Google Scholar 

  180. Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).

    CAS  PubMed  Google Scholar 

  181. Smalley, E. AI-powered drug discovery captures pharma interest. Nat. Biotechnol. 35, 604–605 (2017).

    CAS  PubMed  Google Scholar 

  182. Schneider, G. Automating drug discovery. Nat. Rev. Drug. Discov. 17, 97–113 (2018).

    CAS  PubMed  Google Scholar 

  183. Chakradhar, S. Predictable response: finding optimal drugs and doses using artificial intelligence. Nat. Med. 23, 1244–1247 (2017).

    CAS  PubMed  Google Scholar 

  184. Lowe, D. AI designs organic syntheses. Nature 555, 592–593 (2018).

    CAS  PubMed  Google Scholar 

  185. Luechtefeld, T. et al. Machine learning of toxicological big data enables read-across structure activity relationships (RASAR) outperforming animal test reproducibility. Toxicol. Sci. 165, 198–212 (2018).

  186. Hie, B. et al. Realizing private and practical pharmacological collaboration. Science 362, 347–350 (2018).

    CAS  PubMed  Google Scholar 

  187. Bilsland, E. et al. Plasmodium dihydrofolate reductase is a second enzyme target for the antimalarial action of triclosan. Sci. Rep. 8, 1038 (2018).

    PubMed  PubMed Central  Google Scholar 

  188. Artificially-intelligent robot scientist ‘Eve’ could boost search for new drugs. In University of Cambridge Research (2015).

  189. Ross, C. & Swetlitz, I. IBM’s Watson supercomputer recommended ‘unsafe and incorrect’ cancer treatments, internal documents show. In Stat News (2018).

  190. Miliard, M. As FDA signals wider AI approval, hospitals have a role to play. In Healthcare IT News (2018).

  191. Castelvecchi, D. Can we open the black box of AI? Nature 538, 20–23 (2016).

    CAS  PubMed  Google Scholar 

  192. Knight, W. The dark secret at the heart of AI. In MIT Technology Review (2017).

  193. Weinberger, D. Our machines now have knowledge we’ll never understand. In Backchannel (2017).

  194. Kuang, C. Can A.I. be taught to explain itself? In The New York Times (2017).

  195. Stringhini, S. et al. Socioeconomic status and the 25 × 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1.7 million men and women. Lancet 389, 1229–1237 (2017).

    PubMed  PubMed Central  Google Scholar 

  196. Wapner, J. Cancer scientists have ignored African DNA in the search for cures. In Newsweek (2018).

  197. Miller, A. P. Want less-biased decisions? Use algorithms. In Harvard Business Review (2018).

  198. Brundage, M. et al. The malicious use of artificial intelligence: forecasting, prevention, and mitigation. Preprint at (2018).

  199. Finlayson, S. et al. Adversarial attacks against medical deep learning systems. Preprint at (2018).

  200. Haun, K. & Topol, E. The health data conundrum. In The New York Times (2017).

  201. Kish, L. J. & Topol, E. J. Unpatients-why patients should own their medical data. Nat. Biotechnol. 33, 921–924 (2015).

    CAS  PubMed  Google Scholar 

  202. Heller, N. Estonia, the digital republic. In The New Yorker (2017).

  203. Shladover, S. The truth about “self-driving” cars. In Scientific American 314, 53–57 (2016).

  204. Turing, A. M. On computable numbers with an application to the Entscheidungsproblem. P. Lond. Match. Soc. s2-42, 230–265 (1936).

  205. Turing, A. M. Computing machinery and intelligence. Mind 59, 433–460 (1950).

  206. McCulloch, W. S. & Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943).

  207. Krizhevsky, A. et al. ImageNet classification with deep convolutional neural networks. In NIPS’12 Proceedings of the 25th International Conference on Neural Information Processing Systems 1097–1105 (NIPS, 2012).

  208. Hu, J. et al. Squeeze-and-excitation networks. Preprint at (2017).

  209. Russakovsky, O. et al. ImageNet Large Scale Visual Recognition Challenge. Preprint at (2014).

  210. Goodfellow, I et al. Deep Learning (MIT Press, Cambridge, MA, USA, 2016).

  211. Yu, K.-H. et al. Artificial intelligence in healthcare. Nature Biomed. Eng. 2, 719–731 (2018).

    Google Scholar 

  212. Korkinof, D. et al. High-resolution mammogram synthesis using progressive generative adversarial networks. Preprint at (2018).

  213. Baur, C. et al. Generating highly realistic images of skin lesions with GANs. Preprint at (2018).

  214. Kazeminia, S. et al. GANs for medical image analysis. Preprint at (2018).

  215. Harvey, H. FAKE VIEWS! Synthetic medical images for machine learning. In Towards Data Science (2018).

  216. Madani, A. et al. Deep echocardiography: data-efficient supervised and semisupervised deep learning towards automated diagnosis of cardiac disease. NPJ Digit. Med. 1, 59 (2018).

Download references


Funding was provided by the Clinical and Translational Science Award (CTSA) from the National Institute of Health (NIH) grant number UL1TR002550.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Eric J. Topol.

Ethics declarations

Competing interests

E.T. is on the scientific advisory board of Verily, Tempus Labs, Myokardia and Voxel Cloud and the board of directors of Dexcoman and is an advisor to Guardant Health, Blue Cross Blue Shield Association, and Walgreens.

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

Topol, E.J. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25, 44–56 (2019).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


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