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.
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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).
Roser, M. Link between health spending and life expectancy: US is an outlier. In Our World in Data https://ourworldindata.org/the-link-between-life-expectancy-and-health-spending-us-focus (2017).
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).
Berwick, D. M. & Hackbarth, A. D. Eliminating waste in US health care. JAMA 307, 1513–1516 (2012).
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).
Li, Z. et al. Thoracic disease identification and localization with limited supervision. Preprint at https://arxiv.org/abs/1711.06373 (2017).
Singh, R. et al. Deep learning in chest radiography: detection of findings and presence of change. PLoS ONE 13, e0204155 (2018).
Nam, J. G. et al. Development and validation of deep learning–based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology https://doi.org/10.1148/radiol.2018180237 (2018).
Lindsey, R., et al. Deep neural network improves fracture detection by clinicians. Proc. Natl. Acad. Sci. USA 115, 11591–11596 (2018).
Gale, W. et al. Detecting hip fractures with radiologist-level performance using deep neural networks. Preprint at https://arxiv.org/abs/1711.06504 (2017).
Rajpurkar, P. MURA dataset: towards radiologist-level abnormality detection in musculoskeletal radiographs. Preprint at https://arxiv.org/abs/1712.06957 (2017).
Ridley, E. L. Deep learning shows promise for bone age assessment. In Aunt Minnie https://www.auntminnie.com/index.aspx?sec=log&itemID=119011 (2017).
Lakhani, P. & Sundaram, B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284, 574–582 (2017).
Bar, A. et al. Compression fractures detection on CT. Preprint at https://arxiv.org/abs/1706.01671 (2017).
Ridley, E. L. Deep-learning algorithm can stratify lung nodule risk. In Aunt Minnie https://www.auntminnie.com/index.aspx?sec=rca&sub=rsna_2017&pag=dis&ItemID=119166 (2017).
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).
Liu, F. et al. Joint shape representation and classification for detecting PDAC in abdominal CT scans. Preprint at https://arxiv.org/abs/1804.10684 (2018).
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).
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).
Chilamkurthy, S. et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 392, 2388–2396 (2018).
Chilamkurthy, S. et al. Development and validation of deep learning algorithms for detection of critical findings in head CT ccans. Preprint at https://arxiv.org/abs/1803.05854 (2018).
Lieman-Sifry, J. et al. FastVentricle: cardiac segmentation with ENet. Preprint at https://arxiv.org/abs/1704.04296 (2017).
Madani, A.. et al. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit. Med. 1, 6 (2018).
Zhang, J. et al. Fully automated echocardiogram interpretation in clinical practice feasibility and diagnostic accuracy. Circulation 138, 1623–1635 (2018).
Yee, K. M. AI algorithm matches radiologists in breast screening exams. In Aunt Minnie https://www.auntminnie.com/index.aspx?sec=log&itemID=119385 (2017).
Lehman, C. D. et al. Mammographic breast density assessment using deep learning: clinical implementation. Radiology http://doi.org/10.1148/radiol.2018180694 (2018).
Titano, J. J. et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat. Med. 24, 1337–1341 (2018).
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).
Lobo, J. et al. AUC: a misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17, 145–151 (2007).
Keane, P. & Topol, E. With an eye to AI and autonomous diagnosis. NPJ Digit. Med. 1, 40 (2018).
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).
Kanagasingam, Y. et al. Evaluation of artificial intelligence–based grading of diabetic retinopathy in primary care. JAMA Netw. Open 1, e182665 (2018).
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).
Liu, Y. et al. Artificial intelligence–based breast cancer nodal metastasis detection. Arch. Pathol. Lab. Med. https://doi.org/10.5858/arpa.2018-0147-OA (2018).
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).
Mori, Y. et al. Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy. Ann. Intern. Med. 169, 357–366 (2018).
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).
Long, E. et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nat. Biomed. Eng. 1, 1–8 (2017).
Acs, B. & Rimm, D. L. Not just digital pathology, intelligent digital pathology. JAMA Oncol. 4, 403–404 (2018).
Yu, K. H. et al. Predicting non–small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat. Commun. 7, 12474 (2016).
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).
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).
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).
Wong, D. & Yip, S. Machine learning classifies cancer. Nature 555, 446–447 (2018).
Capper, D. et al. DNA methylation–based classification of central nervous system tumours. Nature 555, 469–474 (2018).
Yang, S. J. et al. Assessing microscope image focus quality with deep learning. BMC Bioinformatics 19, 77 (2018).
Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).
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).
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).
Wong, T. Y. & Bressler, N. M. Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. JAMA 316, 2366–2367 (2016).
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).
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).
Kermany, D. S. et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172, 1122–1131.e1129 (2018).
Ting, D. S. W. et al. AI for medical imaging goes deep. Nat. Med. 24, 539–540 (2018).
Rampasek, L. & Goldenberg, A. Learning from everyday images enables expert-like diagnosis of retinal diseases. Cell 172, 893–895 (2018).
De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342–1350 (2018).
Mutlu, U. et al. Association of retinal neurodegeneration on optical coherence tomography with dementia: a population-based study. JAMA Neurol. 75, 1256–1263 (2018).
Poplin, R. et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat. Biomed. Eng. 2, 158–164 (2018).
All eyes are on AI. Nat. Biomed. Eng. 2, 139 (2018).
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).
Willems, J. et al. The diagnostic performance of computer programs for the interpretation of electrocardiograms. N. Engl. J. Med. 325, 1767–1773 (1991).
Strodthoff, N. & Strodthoff, C. Detecting and interpreting myocardial infarctions using fully convolutional neural networks. Preprint at https://arxiv.org/abs/1806.07385 (2018).
Rajpurkar, P. et al. Cardiologist-level arrhythmia detection with convolutional neural networks. Preprint at https://arxiv.org/abs/1707.01836 (2017).
Holme, Ø. & Aabakken, L. Making colonoscopy smarter with standardized computer-aided diagnosis Ann. Intern. Med. 169, 409–410 (2018).
Petrone, J. FDA approves stroke-detecting AI software. Nat. Biotechnol. 36, 290 (2018).
Hsu, J. & Spectrum. AI could make detecting autism easier. In The Atlantic https://www.theatlantic.com/technology/archive/2018/07/ai-autism-diagnosis-screening-bottleneck/564890/ (2018).
Lundberg, S. et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat. Biomed. Eng. 2, 749–760 (2018).
Peters, A. Having a heart attack? This AI helps emergency dispatchers find out. In Fast Company https://www.fastcompany.com/40515740/having-a-heart-attack-this-ai-helps-emergency-dispatchers-find-out (2018).
Patel, N. M. et al. Enhancing next-generation sequencing-guided cancer care through cognitive computing. Oncologist 23, 179–185 (2018).
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 https://www.dailymail.co.uk/health/article-6257891/Study-finds-artificial-intelligence-better-doctor-crucial-stage-IVF.html (2018).
Gurovich, Y. et al. DeepGestalt—identifying rare genetic syndromes using deep learning. Preprint at https://arxiv.org/abs/1801.07637 (2017).
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).
Coiera, E. et al. The digital scribe. NPJ Digit. Med. 1, 58 (2018).
The burden of depression. Nature 515, 163 (2014).
Cao, B. et al. DeepMood: modeling mobile phone typing dynamics for mood detection. Preprint at https://arxiv.org/abs/1803.08986 (2018).
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).
Frankel, J. How artificial intelligence could help diagnose mental disorders. In The Atlantic https://www.theatlantic.com/health/archive/2016/08/could-artificial-intelligence-improve-psychiatry/496964/ (2016).
Barrett, P. M. et al. Digitising the mind. Lancet 389, 1877 (2017).
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).
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).
Eichstaedt, J. C. et al. Facebook language predicts depression in medical records. Proc. Natl. Acad. Sci. USA 115, 11203–11208 (2018).
Chekroud, A. M. et al. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 3, 243–250 (2016).
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).
Reece, A. G. & Danforth, C. M. Instagram photos reveal predictive markers of depression. EPJ Data Science 6, 15 (2017).
Wager, T. D. & Woo, C. W. Imaging biomarkers and biotypes for depression. Nat. Med. 23, 16–17 (2017).
Walsh, C. G. et al. Predicting risk of suicide attempts over time through machine learning. Clin. Psychol. Sci. 5, 457–469 (2017).
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).
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).
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).
Yang, Z. et al. Clinical assistant diagnosis for electronic medical record based on convolutional neural network. Sci. Rep. 8, 6329 (2018).
Avati, A. et al. Improving palliative care with deep learning. Preprint at https://arxiv.org/abs/1711.06402 (2017).
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).
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).
Bennington-Castro, J. AI can predict when we’ll die—here’s why that’s a good thing. In NBC News https://www.nbcnews.com/mach/science/ai-can-predict-when-we-ll-die-here-s-why-ncna844276 (2018).
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).
Rajkomar, A. et al. Scalable and accurate deep learning with electronic health records. NPJ Digit. Med. 1, 18 (2018).
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).
Mathotaarachchi, S. et al. Identifying incipient dementia individuals using machine learning and amyloid imaging. Neurobiol. Aging. 59, 80–90 (2017).
Yoon, J. et al. Personalized survival predictions via Trees of Predictors: an application to cardiac transplantation. PLoS ONE 13, e0194985 (2018).
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).
Alaa, A. M. & van der Schaar, M. Prognostication and risk factors for cystic fibrosis via automated machine learning. Sci. Rep. 8, 11242 (2018).
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).
Henry, K. E. et al. A targeted real-time early warning score (TREWScore) for septic shock. Sci. Transl. Med. 7, 299ra122 (2015).
Culliton, P. et al. Predicting severe sepsis using text from the electronic health record. Preprint at https://arxiv.org/abs/1711.11536 (2017).
Razavian, N. et al. Multi-task prediction of disease onsets from longitudinal lab tests. PMLR 56, 73–100 (2016).
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).
Bhagwat, N. et al. Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease using longitudinal data. PLoS Comput. Biol. 14, e1006376 (2018).
Komorowski, M. et al. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. Nat. Med. 24, 1716–1720 (2018).
Zaidi, D. AI is transforming medical diagnosis, prosthetics, and vision aids. In Venture Beat https://venturebeat.com/2017/10/30/ai-is-transforming-medical-diagnosis-prosthetics-and-vision-aids/ (2017).
Putin, E. et al. Deep biomarkers of human aging: application of deep neural networks to biomarker development. Aging 8, 1021–1033 (2016).
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).
Horvath, S. & Raj, K. DNA methylation–based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 19, 371–384 (2018).
Rose, S. Machine Learning for Prediction in Electronic Health Data. JAMA Netw. Open 1, e181404(2018).
Haque, A. et al. Towards vision-based smart hospitals: a system for tracking and monitoring hand hygiene compliance. Preprint at https://arxiv.org/abs/1708.00163 (2017).
Suresh, H. et al. Clinical intervention prediction and understanding with deep neural networks. Preprint at https://arxiv.org/abs/1705.08498 (2017).
Kwolek, B. & Kepski, M. Human fall detection on embedded platform using depth maps and wireless accelerometer. Comput. Methods Programs Biomed. 117, 489–501 (2014).
Prasad, N. et al. A reinforcement learning approach to weaning of mechanical ventilation in intensive care units. Preprint at https://arxiv.org/abs/1704.06300 (2018).
Maier-Hein, L. et al. Surgical data science for next-generation interventions. Nat. Biomed. Eng. 1, 691–696 (2017).
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).
Gehlbach, P. L. Robotic surgery for the eye. Nat. Biomed. Eng. 2, 627–628 (2018).
Nikolov, S. et al. Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy. Preprint at https://arxiv.org/abs/1809.04430 (2018).
Zhu, B. et al. Image reconstruction by domain-transform manifold learning. Nature 555, 487–492 (2018).
Harvey, H. Can AI enable a 10 minute MRI? In Towards Data Science https://towardsdatascience.com/can-ai-enable-a-10-minute-mri-77218f0121fe (2018).
Ridley, E. L. Artificial intelligence guides lower PET tracer dose. In Aunt Minnie https://www.auntminnie.com/index.aspx?sec=log&itemID=119572 (2018).
Beam, A. L. & Kohane, I. S. Translating artificial intelligence into clinical care. JAMA 316, 2368–2369 (2016).
Tuegel, E. J. et al. Reengineering aircraft structural life prediction using a digital twin. Int. J. Aerosp. 2011, 154798 (2011).
Tarassenko, L. & Topol, E. Monitoring the health of jet engines and people. JAMA https://doi.org/10.1001/jama.2018.16558 (2018).
Buhr, S. FDA clears AliveCor’s Kardiaband as the first medical device accessory for the Apple Watch. In TechCrunch https://techcrunch.com/2017/11/30/fda-clears-alivecors-kardiaband-as-the-first-medical-device-accessory-for-the-apple-watch/ (2017).
Victory, J. What did journalists overlook about the Apple Watch ‘heart monitor’ feature? In HealthNewsReview https://www.healthnewsreview.org/2018/09/what-did-journalists-overlook-about-the-apple-watch-heart-monitor-feature/ (2018).
Fingas, R. Apple Watch Series 4 EKG tech got FDA clearance less than 24 hours before reveal. In AppleInsider https://appleinsider.com/articles/18/09/18/apple-watch-series-4-ekg-tech-got-fda-clearance-less-than-24-hours-before-reveal (2018).
Carroll, A. E. That new apple watch EKG feature? There are more downs than ups. In The New York Times https://www.nytimes.com/2018/10/08/upshot/apple-watch-heart-monitor-ekg.html (2018).
Levine, B. & Brown, A. Onduo delivers diabetes clinic and coaching to your smartphone. In Diatribe https://diatribe.org/onduo-delivers-diabetes-clinic-and-coaching-your-smartphone (2018).
Han, Q. et al. A hybrid recommender system for patient–doctor matchmaking in primary care. Preprint at https://arxiv.org/abs/1808.03265 (2018).
Zmora, N. et al. Taking it personally: personalized utilization of the human microbiome in health and disease. Cell. Host. Microbe. 19, 12–20 (2016).
Korem, T. et al. Bread affects clinical parameters and induces gut microbiome–associated personal glycemic responses. Cell. Metab. 25, 1243–1253 e1245 (2017).
Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).
Hall, H. et al. Glucotypes reveal new patterns of glucose dysregulation. PLoS Biol. 16, e2005143 (2018).
Albers, D. J. et al. Personalized glucose forecasting for type 2 diabetes using data assimilation. PLoS. Comput. Biol. 13, e1005232 (2017).
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).
Thaiss, C. A. et al. Hyperglycemia drives intestinal barrier dysfunction and risk for enteric infection. Science 359, 1376–1383 (2018).
Wu, D. et al. Glucose-regulated phosphorylation of TET2 by AMPK reveals a pathway linking diabetes to cancer. Nature 559, 637–641 (2018).
Bally, L. et al. Closed-loop insulin delivery for glycemic control in noncritical care. N. Engl. J. Med. 379, 547–556 (2018).
Christiansen, E. M. et al. In silico labeling: predicting fluorescent labels in unlabeled images. Cell 173, 792–803 e719 (2018).
Sullivan, D. P. & Lundberg, E. Seeing more: a future of augmented microscopy. Cell 173, 546–548 (2018).
Ounkomol, C. et al. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nat. Methods 15, 917–920 (2018).
Ota, S. et al. Ghost cytometry. Science 360, 1246–1251 (2018).
Nitta, N. et al. Intelligent image-activated cell sorting. Cell 175, 266–276 e213 (2018).
Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Preprint at https://doi.org/10.1101/236463 (2017).
Gut, G. et al. Multiplexed protein maps link subcellular organization to cellular states. Science 361, eaar7042 (2018).
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).
Poplin, R. et al. Creating a universal SNP and small indel variant caller with deep neural networks. Preprint at https://doi.org/10.1101/092890 (2016).
Sundaram, L. et al. Predicting the clinical impact of human mutation with deep neural networks. Nat. Genet. 50, 1161–1170 (2018).
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).
Zhou, J. & Troyanskaya, O. G. Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12, 931–934 (2015).
Luo, R., et al. Clairvoyante: a multi-task convolutional deep neural network for variant calling in single molecule sequencing. Preprint at https://doi.org/10.1101/310458 (2018).
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).
Poplin, R. et al. A universal SNP and small-indel variant caller using deep neural networks. Nat. Biotechnol. 36, 983–987 (2018).
Riesselman, A. et al. Deep generative models of genetic variation capture the effects of mutations. Nat. Methods 15, 816–822 (2018).
Wood, D. E. et al. A machine learning approach for somatic mutation discovery. Sci. Transl. Med. 10, eaar7939 (2018).
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).
Lin, C. et al. Using neural networks for reducing the dimensions of single-cell RNA-seq data. Nucleic Acids Res. 45, e156 (2017).
Angermueller, C. et al. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome. Biol. 18, 67 (2017).
AlQuraishi, M. End-to-end differentiable learning of protein structure. Preprint at https://doi.org/10.1101/265231 (2018).
Espinoza, J. L. Machine learning for tackling microbiota data and infection complications in immunocompromised patients with cancer. J. Intern. Med. https://doi.org/10.1111/joim.12746 (2018).
van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729.e727 (2018).
Zitnik, M. et al. Machine learning for integrating data in biology and medicine: principles, practice, and opportunities. Preprint at https://doi.org/10.1111/joim.12746 (2018).
Camacho, D. M. et al. Next-generation machine learning for biological networks. Cell 173, 1581–1592 (2018).
Kim, H. K. et al. Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity. Nat. Biotechnol. 36, 239–241 (2018).
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).
Caravagna, G. et al. Detecting repeated cancer evolution from multi-region tumor sequencing data. Nat. Methods 15, 707–714 (2018).
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).
Hassabis, D. et al. Neuroscience-inspired artificial intelligence. Neuron 95, 245–258 (2017).
Robie, A. A. et al. Mapping the neural substrates of behavior. Cell 170, 393–406 e328 (2017).
Dasgupta, S. et al. A neural algorithm for a fundamental computing problem. Science 358, 793–796 (2017).
Januszewski, M. et al. High-precision automated reconstruction of neurons with flood-filling networks. Nat. Methods 15, 605–610 (2018).
Savelli, F. & Knierim, J. J. AI mimics brain codes for navigation. Nature 557, 313–314 (2018).
Banino, A. et al. Vector-based navigation using grid-like representations in artificial agents. Nature 557, 429–433 (2018).
Adam, G. C. Two artificial synapses are better than one. Nature 558, 39–40 (2018).
Wright, C. D. Phase-change devices: crystal-clear neuronal computing. Nat. Nanotechnol. 11, 655–656 (2016).
Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).
Smalley, E. AI-powered drug discovery captures pharma interest. Nat. Biotechnol. 35, 604–605 (2017).
Schneider, G. Automating drug discovery. Nat. Rev. Drug. Discov. 17, 97–113 (2018).
Chakradhar, S. Predictable response: finding optimal drugs and doses using artificial intelligence. Nat. Med. 23, 1244–1247 (2017).
Lowe, D. AI designs organic syntheses. Nature 555, 592–593 (2018).
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).
Hie, B. et al. Realizing private and practical pharmacological collaboration. Science 362, 347–350 (2018).
Bilsland, E. et al. Plasmodium dihydrofolate reductase is a second enzyme target for the antimalarial action of triclosan. Sci. Rep. 8, 1038 (2018).
Artificially-intelligent robot scientist ‘Eve’ could boost search for new drugs. In University of Cambridge Research https://www.cam.ac.uk/research/news/artificially-intelligent-robot-scientist-eve-could-boost-search-for-new-drugs (2015).
Ross, C. & Swetlitz, I. IBM’s Watson supercomputer recommended ‘unsafe and incorrect’ cancer treatments, internal documents show. In Stat News https://www.statnews.com/2018/07/25/ibm-watson-recommended-unsafe-incorrect-treatments/ (2018).
Miliard, M. As FDA signals wider AI approval, hospitals have a role to play. In Healthcare IT News https://www.healthcareitnews.com/news/fda-signals-wider-ai-approval-hospitals-have-role-play (2018).
Castelvecchi, D. Can we open the black box of AI? Nature 538, 20–23 (2016).
Knight, W. The dark secret at the heart of AI. In MIT Technology Review https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/ (2017).
Weinberger, D. Our machines now have knowledge we’ll never understand. In Backchannel https://www.wired.com/story/our-machines-now-have-knowledge-well-never-understand/ (2017).
Kuang, C. Can A.I. be taught to explain itself? In The New York Times https://www.nytimes.com/2017/11/21/magazine/can-ai-be-taught-to-explain-itself.html (2017).
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).
Wapner, J. Cancer scientists have ignored African DNA in the search for cures. In Newsweek https://www.newsweek.com/2018/07/27/cancer-cure-genome-cancer-treatment-africa-genetic-charles-rotimi-dna-human-1024630.html (2018).
Miller, A. P. Want less-biased decisions? Use algorithms. In Harvard Business Review https://hbr.org/2018/07/want-less-biased-decisions-use-algorithms (2018).
Brundage, M. et al. The malicious use of artificial intelligence: forecasting, prevention, and mitigation. Preprint at https://arxiv.org/ftp/arxiv/papers/1802/1802.07228.pdf (2018).
Finlayson, S. et al. Adversarial attacks against medical deep learning systems. Preprint at https://arxiv.org/abs/1804.05296 (2018).
Haun, K. & Topol, E. The health data conundrum. In The New York Times https://www.nytimes.com/2017/01/02/opinion/the-health-data-conundrum.html (2017).
Kish, L. J. & Topol, E. J. Unpatients-why patients should own their medical data. Nat. Biotechnol. 33, 921–924 (2015).
Heller, N. Estonia, the digital republic. In The New Yorker https://www.newyorker.com/magazine/2017/12/18/estonia-the-digital-republic (2017).
Shladover, S. The truth about “self-driving” cars. In Scientific American 314, 53–57 (2016).
Turing, A. M. On computable numbers with an application to the Entscheidungsproblem. P. Lond. Match. Soc. s2-42, 230–265 (1936).
Turing, A. M. Computing machinery and intelligence. Mind 59, 433–460 (1950).
McCulloch, W. S. & Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943).
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).
Hu, J. et al. Squeeze-and-excitation networks. Preprint at https://arxiv.org/abs/1709.01507 (2017).
Russakovsky, O. et al. ImageNet Large Scale Visual Recognition Challenge. Preprint at https://arxiv.org/abs/1409.0575 (2014).
Goodfellow, I et al. Deep Learning (MIT Press, Cambridge, MA, USA, 2016).
Yu, K.-H. et al. Artificial intelligence in healthcare. Nature Biomed. Eng. 2, 719–731 (2018).
Korkinof, D. et al. High-resolution mammogram synthesis using progressive generative adversarial networks. Preprint at https://arxiv.org/abs/1807.03401 (2018).
Baur, C. et al. Generating highly realistic images of skin lesions with GANs. Preprint at https://arxiv.org/abs/1809.01410 (2018).
Kazeminia, S. et al. GANs for medical image analysis. Preprint at https://arxiv.org/abs/1809.06222 (2018).
Harvey, H. FAKE VIEWS! Synthetic medical images for machine learning. In Towards Data Science https://towardsdatascience.com/harnessing-infinitely-creative-machine-imagination-6801a9fb4ca9 (2018).
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).
Funding was provided by the Clinical and Translational Science Award (CTSA) from the National Institute of Health (NIH) grant number UL1TR002550.
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.
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Topol, E.J. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25, 44–56 (2019). https://doi.org/10.1038/s41591-018-0300-7
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