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.
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Simonite, T. Google’s AI eye doctor gets ready to go to work in India. WIRED (6 August 2017).
Lee, R., Wong, T. Y. & Sabanayagam, C. Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss. Eye Vis. 2, 17 (2015).
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).
Zheng, Y., He, M. & Congdon, N. The worldwide epidemic of diabetic retinopathy. Indian J. Ophthalmol. 60, 428–431 (2012).
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).
Poplin, R. et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat. Biomed. Eng. 2, 158–164 (2018).
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).
Russell, S. J. & Norvig, P. Artificial Intelligence: A Modern Approach (Prentice Hall, New Jersey, 2010).
Krizhevsky, A., Sutskever, I. & Hinton, G. E. in Advances in Neural Information Processing Systems 1097–1105 (Curran Associates, Nevada, 2012).
Lewis-Kraus, G. The great A.I. awakening. The New York Times Magazine (14 December 2016).
Kundu, M., Nasipuri, M. & Basu, D. K. Knowledge-based ECG interpretation: a critical review. Pattern Recognit. 33, 351–373 (2000).
Jha, S. & Topol, E. J. Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA 316, 2353–2354 (2016).
Golub, T. R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999).
Wang, Y. et al. Gene selection from microarray data for cancer classification—a machine learning approach. Comput. Biol. Chem. 29, 37–46 (2005).
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).
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).
Check Hayden, E. The automated lab. Nature 516, 131–132 (2014).
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).
Musen, M. A., Middleton, B. & Greenes, R. A. in Biomedical Informatics (eds Shortliffe, E. H. & Cimino, J. J.) 643–674 (Springer, London, 2014).
Shortliffe, E. Computer-Based Medical Consultations: MYCIN Vol. 2 (Elsevier, New York, 2012).
Szolovits, P., Patil, R. S. & Schwartz, W. B. Artificial intelligence in medical diagnosis. Ann. Intern. Med. 108, 80–87 (1988).
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).
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).
Barnett, G. O., Cimino, J. J., Hupp, J. A. & Hoffer, E. P. DXplain. An evolving diagnostic decision-support system. JAMA 258, 67–74 (1987).
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).
Berner, E. S. et al. Performance of four computer-based diagnostic systems. N. Engl. J. Med. 330, 1792–1796 (1994).
Szolovits, P. & Pauker, S. G. Categorical and probabilistic reasoning in medical diagnosis. Artif. Intell. 11, 115–144 (1978).
Deo, R. C. Machine learning in medicine. Circulation 132, 1920–1930 (2015).
Yu, K. H. & Snyder, M. Omics profiling in precision oncology. Mol. Cell. Proteomics 15, 2525–2536 (2016).
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).
Cloud AutoML ALPHA (Google Cloud); https://cloud.google.com/automl/
Goodfellow, I., Bengio, Y., Courville, A. & Bengio, Y. Deep Learning 1 (MIT Press, Cambridge, 2016).
Gill, N. S. Overview and applications of artificial neural networks. Xenonstack https://www.xenonstack.com/blog/data-science/artificial-neural-networks-applications-algorithms/ (2017).
TOP500 List – November 2006 (TOP500); https://www.top500.org/list/2006/11/
Beam, A. L. & Kohane, I. S. Translating artificial intelligence into clinical care. JAMA 316, 2368–2369 (2016).
Kamentsky, L. et al. Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software. Bioinformatics 27, 1179–1180 (2011).
Ching, T. et al. Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15, 20170387 (2018).
Tomczak, K., Czerwinska, P. & Wiznerowicz, M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol. 19, 68–77 (2015).
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).
Ljosa, V., Sokolnicki, K. L. & Carpenter, A. E. Annotated high-throughput microscopy image sets for validation. Nat. Methods 9, 637 (2012).
Williams, E. et al. The image data resource: a bioimage data integration and publication platform. Nat. Methods 14, 775–781 (2017).
DesRoches, C. M. et al. Electronic health records in ambulatory care–a national survey of physicians. N. Engl. J. Med. 359, 50–60 (2008).
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).
Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).
Beck, A. H. et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci. Transl. Med. 3, 108ra113 (2011).
Yu, K. H. et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat. Commun. 7, 12474 (2016).
Shademan, A. et al. Supervised autonomous robotic soft tissue surgery. Sci. Transl. Med. 8, 337ra364 (2016).
Reed, J. C. Chest Radiology: Plain Film Patterns and Differential Diagnoses (Elsevier Health Sciences, Philadelphia, 2010).
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).
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).
Lakhani, P. & Sundaram, B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284, 574–582 (2017).
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).
Yao, L. et al. Learning to diagnose from scratch by exploiting dependencies among labels. Preprint at https://arxiv.org/abs/1710.10501 (2017).
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).
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).
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).
510(k) Premarket Notification (FDA, 2017); https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K163253
Marr, B. First FDA approval for clinical cloud-based deep learning in healthcare. Forbes (20 January 2017).
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).
Thomas, L. et al. Semiological value of ABCDE criteria in the diagnosis of cutaneous pigmented tumors. Dermatology 197, 11–17 (1998).
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).
Wolf, J. A. et al. Diagnostic inaccuracy of smartphone applications for melanoma detection. JAMA Dermatol. 149, 422–426 (2013).
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).
American Diabetes Association. 10. Microvascular complications and foot care. Diabetes Care 40, 88–98 (2017).
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).
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).
Rorke, L. B. Pathologic diagnosis as the gold standard. Cancer 79, 665–667 (1997).
Lakhani, S. R. & Ashworth, A. Microarray and histopathological analysis of tumours: the future and the past? Nat. Rev. Cancer 1, 151–157 (2001).
Rubegni, P. et al. Automated diagnosis of pigmented skin lesions. Int. J. Cancer 101, 576–580 (2002).
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).
Yu, K. H. et al. Association of omics features with histopathology patterns in lung adenocarcinoma. Cell Syst. 5, 620–627 (2017).
Litjens, G. et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6, 26286 (2016).
Bejnordi, B. E. et al. Machine learning detection of breast cancer lymph node metastases. JAMA 318, 2199–2210 (2017).
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).
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).
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).
Litjens, G. et al. A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017).
Quang, D., Chen, Y. & Xie, X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics 31, 761–763 (2015).
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).
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).
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).
Kamps, R. et al. Next-generation sequencing in oncology: genetic diagnosis, risk prediction and cancer classification. Int. J. Mol. Sci. 18, 308 (2017).
He, Z. & Yu, W. Stable feature selection for biomarker discovery. Comput. Biol. Chem. 34, 215–225 (2010).
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).
Wallden, B. et al. Development and verification of the PAM50-based Prosigna breast cancer gene signature assay. BMC Med. Genomics 8, 54 (2015).
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).
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).
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).
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).
Altman, R. B. Artificial intelligence (AI) systems for interpreting complex medical datasets. Clin. Pharmacol. Ther. 101, 585–586 (2017).
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).
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).
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).
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).
Churpek, M. M. et al. Predicting cardiac arrest on the wards: a nested case-control study. Chest 141, 1170–1176 (2012).
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).
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).
Li, X. et al. Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biol. 15, e2001402 (2017).
Majumder, S., Mondal, T. & Deen, M. J. Wearable sensors for remote health monitoring. Sensors 17, 130 (2017).
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).
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).
Takacs, J. et al. Validation of the Fitbit One activity monitor device during treadmill walking. J. Sci. Med. Sport 17, 496–500 (2014).
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).
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).
Herz, J. C. Wearables are totally failing the people who need them most. Wired (11 June 2014).
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).
Wheeler, M. J. Overview on robotics in the laboratory. Ann. Clin. Biochem. 44, 209–218 (2007).
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).
Gomes, P. Surgical robotics: reviewing the past, analysing the present, imagining the future. Robot. Comput. Integr. Manuf. 27, 261–266 (2011).
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).
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).
Liew, C. The future of radiology augmented with artificial intelligence: a strategy for success. Eur. J. Radiol. 102, 152–156 (2018).
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).
Obermeyer, Z. & Emanuel, E. J. Predicting the future — big data, machine learning, and clinical medicine. N. Engl. J. Med. 375, 1216–1219 (2016).
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).
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).
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).
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).
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).
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
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).
Zoph, B. & Le, Q. V. Neural architecture search with reinforcement learning. Preprint at https://arxiv.org/abs/1611.01578 (2016).
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).
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).
Dolin, R. H. et al. HL7 Clinical Document Architecture, Release 2. J. Am. Med. Inform. Assoc. 13, 30–39 (2006).
Mandl, K. D. & Kohane, I. S. Escaping the EHR trap—the future of health IT. N. Engl. J. Med. 366, 2240–2242 (2012).
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).
All eyes are on AI. Nat. Biomed. Eng. 2, 139 (2018).
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).
Dignum, V. Ethics in artificial intelligence: introduction to the special issue. Ethics Inf. Technol. 20, 1–3 (2018).
Price, I. & Nicholson, W. Artificial Intelligence in Health Care: Applications and Legal Implications (Univ. Michigan Law School, 2017).
Mukherjee, S. A.I. versus M.D. What happens when diagnosis is automated? The New Yorker (3 April 2017).
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).
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).
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).
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).
Lehman, C. D. et al. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern. Med. 175, 1828–1837 (2015).
Koppel, R. et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA 293, 1197–1203 (2005).
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).
Gottlieb, S. Twitter (12 April 2018); https://twitter.com/SGottliebFDA/status/984378648781312002
Digital Health Software Precertification (Pre-Cert) Program (FDA); https://www.fda.gov/MedicalDevices/DigitalHealth/DigitalHealthPreCertProgram/default.htm
Estrin, D. & Sim, I. Open mHealth architecture: an engine for health care innovation. Science 330, 759–760 (2010).
Shortliffe, E. H. Computer programs to support clinical decision making. JAMA 258, 61–66 (1987).
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).
Rosenfeld, L. A golden age of clinical chemistry: 1948–1960. Clin. Chem. 46, 1705–1714 (2000).
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).
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).
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).
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).
Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Preprint at https://arxiv.org/abs/1409.1556 (2014).
Murphy, K. P. & Bach F. Machine Learning: A Probabilistic Perspective (MIT Press, Cambridge, 2012).
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.
Harvard Medical School (K.-H.Y.) submitted a provisional patent application on digital pathology profiling to the United States Patent and Trademark Office (USPTO).
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Yu, K., Beam, A.L. & Kohane, I.S. Artificial intelligence in healthcare. Nat Biomed Eng 2, 719–731 (2018) doi:10.1038/s41551-018-0305-z
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