Artificial Intelligent in Healthcare

Anna Meiliana, Nurrani Mustika Dewi, Andi Wijaya


BACKGROUND: Giant transformations are going on currently in health care, and the greatest force behind this phenomenon is data.

CONTENT: Big data has arrived into medicine field, lead to potential enhancement in accountability, quality, efficiency, and innovation. Most updated, artificial intelligence (AI) and machine-learning (ML) techniques rapidly developed, bring forth the big data analysis into more useful applications, from resource allocation to complex disease diagnosis. To realize this, a very large set of health-care data is needed for algorithms training and evaluation, including patients’ treatment data, patients respond to treatment, and personal patient information, such as genetic data, family history, health behavior, and vital signs.

SUMMARY: Precision Health involving preventive, predictive, personalized and precise. The arrival of AI and ML will enhance and facilitates the improvement of this relationship through better accuracy, productivity, and workflow, thus develop a health system that will go beyond just curing disease, but further into wellness that preventing disease before it strikes, thus the patient–doctor bond is expected to be reformed and not be eroded.

KEYWORDS: artificial intelligence, machine learning, deep learning, electronic health records, big data

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Stanford Medicine. Stanford Medicine 2017 Health Trends Report: Harnessing the Power of Data in Health. Stanford: Stanford University School of Medicine; 2017, article.

Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019; 25: 44-56, CrossRef.

Singh H, Meyer AND, Thomas EJ. The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ Qual Saf. 2014; 23: 727-31, CrossRef.

Berwick DM, Hackbarth AD. Eliminating waste in US health care. JAMA. 2012; 307: 1513-6, CrossRef.

Rodriguez F, Scheinker D, Harrington RA. Promise and perils of big data and artificial intelligence in clinical medicine and biomedical research. Circ Res. 2018; 123: 1282-4, CrossRef.

Yeung S, Downing NL, Fei-Fei L, Milstein A. Bedside computer vision - moving artificial intelligence from driver assistance to patient safety. N Engl J Med. 2018; 378: 1271-3, CrossRef.

Castelvecchi D. Can we open the black box of AI? Nature. 2016; 538:20–3, CrossRef.

Executive Office of the President. Big data: seizing opportunities, preserving values. Washington D.C: The White House of Barack Obama; 2015, article.

Linked In [Internet]. Marr B. How Big Data Keeps Transforming Healthcare. Linkedin May 2016 [updated 2016 May 27; cited 2019 Jan 10]. Available from:

Price WN, Cohen IG. Privacy in the age of medical big data. Nat Med. 2019; 25: 37-43, CrossRef.

Hoffman S. Electronic Health Records and Medical Big Data. New York: Cambridge University Press; 2016, CrossRef.

Kohn LT, Corrigan JM, Donaldson MS, eds. To Err is Human: Building a Safer Health System. Washington DC: National Academies Press; 2000, CrossRef.

Centers for Medicare and Medicaid Services [Internet]. Hospital Inpatient Quality Reporting Program [updated 2017 Sep 19; cited 2019 Jan 10]. Available from:

Kohane IS. Using electronic health records to drive discovery in disease genomics. Nat Rev Genet. 2011; 12: 417-28, CrossRef.

Behrman RE, Benner JS, Brown JS, McClellan M, Woodcock J, Platt R. Developing the sentinel system—a national resource for evidence development. N Engl J Med. 2011; 364: 498-9, CrossRef.

Price WN. II Black-box medicine. Harv JL Tech. 2016; 28: 419-67.

Terry NP. Appification, AI, & healthcare’s new iron triangle. J. Health Law Policy. 2018; 21: 3020784, CrossRef.

Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542: 115-8, CrossRef.

Liu NT, Holcomb JB, Wade CE, Batchinsky AI, Cancio LC, Darrah MI, et al. Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patients. Med Biol Eng Comput. 2014; 52: 193-203, CrossRef.

Avati A, Jung K, Harman S, Downing L, Ng A, Shah NH. Improving palliative care with deep learning. BMC Med Inform Decis Mak. 2018 Dec 12;18(Suppl 4):122, CrossRef.

Data Center Knowledge [Internet]. Riccio K. Big Data Experts in Big Demand. [updated 2017 May 30; cited 2019 Jan 15]. Available from:

Birkhead GS, Klompas M, Shah NR. Uses of electronic health records for public health surveillance to advance public health. Annu Rev Public Health. 2015; 36: 345-59, CrossRef.

Botsis T, Hartvigsen G, Chen F, Weng C. Secondary use of EHR: data quality issues and informatics opportunities. Summit Transl Bioinform. 2010; 2010: 1-5, PMID.

Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet. 2012; 13: 395-405, CrossRef.

The Office of the National Coordinator for Health Information Technology [Internet]. Henry J, Pylypchuk Y, Searcy T, Patel V. Adoption of Electronic Health Record Systems among U.S. Non-Federal Acute Care Hospitals: 2008-2015 [updated 2016 May; cited 2019 Jan 18]. Available from:

Yang N, Hing E. National Electronic Health Records Survey: 2015 Specialty and Overall Physicians Electronic Health Record Adoption Summary Tables. Atlanta: Centers for Disease Control and Prevention; 2017, article.

Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018; 562: 203-9, CrossRef.

Mincholé A, Rodriguez B. Artificial intelligence for the electrocardiogram. Nat Med. 2019; 25: 22-3, CrossRef.

Meystre SM, Savova GK, Kipper-Schuler KC, Hurdle JF. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform. 2008; 47: 128-44, PMID.

Jiang M, Chen Y, Liu M, Rosenbloom ST, Mani S, Denny JC, et al. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. J Am Med Inform Assoc. 2011; 18: 601-6, CrossRef.

Ebadollahi S, Sun J, Gotz D, Hu J, Sow D, Neti C. Predicting patient’s trajectory of physiological data using temporal trends in similar patients : a system for near-term prognostics. AMIA Annu Symp Proc. 2010; 2010: 192-6, PMID.

Zhao D, Weng C. Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer prediction. J Biomed Inform. 2011; 44: 859-68, CrossRef.

Austin PC, Tu JV, Ho JE, Levy D, Lee DS. Using methods from the data-mining and machine-learning literature for disease classification and prediction: A case study examining classification of heart failure subtypes. J Clin Epidemiol. 2013; 66: 398-407, CrossRef.

Kuperman GJ, Bobb A, Payne TH, Avery AJ, Gandhi TK, Burns G, et al. Medication-related clinical decision support in computerized provider order entry systems: a review. J Am Med Inform Assoc. 2007; 14: 29-40, CrossRef.

Knake LA, Ahuja M, McDonald EL, Ryckman KK, Weathers N, Burstain T. Quality of EHR data extractions for studies of preterm birth in a tertiary care center: guidelines for obtaining reliable data. BMC Pediatr. 2016; 16: 59, CrossRef.

Editor of Nature Medicine. Medicine in the digital age. Nat Med. 2019; 25: 1, CrossRef.

Noreot B, Glicksberg BS, Butte AJ. A call for deep-learning healthcare. Nat Med. 2019; 25: 14-5, CrossRef.

Gottesman O, Johansson F, Meier J, Dent J, Lee D, Srinivasan S, et al. Evaluating reinforcement learning algorithms in observational health settings. New York: Cornell University; 2018, article.

Domingos P. A few useful things to know about machine learning. Commun ACM. 2012; 55: 78-87, CrossRef.

Esteva A, Robicquet A, Ramsudar B, Kuleshov V, DePristo M, et al. A guide to deep learning in healthcare. Nat Med. 2019; 25: 24-9, CrossRef.

Goodfellow I, Bengio Y, Courville A. Deep Learning. Cambridge: The MIT Press; 2016.

Miotto R, Li L, Kidd BA, Dudley JT. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep. 2016; 6: 26094, CrossRef.

Jagannatha AN, Yu H. Structured prediction models for RNN based sequence labeling in clinical text. Proc Conf Empir Methods Nat Lang Process. 2016; 2016: 856-65, PMID.

Jagannatha AN, Yu H. Bidirectional RNN for medical event detection in electronic health records. Proc Conf. 2016; 2016: 473-82, PMID.

Shickel B, Tighe PJ, Bihorac A, Rashidi P. Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis. New York: Cornell University; 2018, article.

Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. Imagenet large scale visual recognition challenge. Int J Compute Vis. 2015; 115: 211-52, CrossRef.

Hirschberg J, Manning CD. Advances in natural language processing. Science. 2015; 349: 261-6, CrossRef.

Hinton G, Deng L, Yu D, Dahl G, Mohamed A, Jaitly N, et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag. 2012; 29: 82–97, CrossRef.

Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017; 60: 84-90, CrossRef.

Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. Lect Notes Comput Sci. 2014; 8689: 818-33, CrossRef.

Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018; 172: 1122-31, CrossRef.

Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014; 46: 310-5, CrossRef.

Quang D, Chen Y, Xie X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics. 2015; 31: 761-3, CrossRef.

Dudley JT, Listgarten J, Stegle O, Brenner SE, Parts L. Personalized medicine: from genotypes, molecular phenotypes and the quantified self, towards improved medicine. Biocomputing. 2014; 2015: 342-6, CrossRef.

Leung MKK, Delong A, Alipanahi B, Frey BJ. Machine learning in genomic medicine: a review of computational problems and data sets. Proc IEEE. 2016; 104: 176-97, CrossRef.

Xiong HY, Alipanahi B, Lee LJ, Bretschneider H, Merico D, Yuen RKC, et al. The human splicing code reveals new insights into the genetic determinants of disease. Science. 2015; 347: 1254806, CrossRef.

Alipanahi B, Delong A, Weirauch MT, Frey BJ. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nature Biotechnol. 2015; 33: 831-8, CrossRef.

Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA. 2013; 309: 1351-2, CrossRef.

Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017; 21: 230-43, CrossRef.

Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, et al. Artificial intelligence in cardiology. J Am Coll Cardiol. 2018; 71: 2668-79, CrossRef.

Tang A, Tam R, Cadrin-Chênevert A, Guest W, Chong J, Barfett J, et al. Canadian association of radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J. 2018; 69: 120-35, CrossRef.

De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018; 24: 1342-50, CrossRef.

Slomka PJ, Dey D, Sitek A, Motwani M, Berman DS, Germano G. Cardiac imaging: working towards fully-automated machine analysis & interpretation. Expert Rev Med Devices. 2017; 14: 197-212, CrossRef.

Wang D, Khosla A, Gargeya R, Irshad H, Beck AH. Deep Learning For Identifying Metastatic Breast Cancer. New York: Cornell University; 2016, article.

Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017; 284: 574-82, CrossRef.

Sitapati A, Kim H, Berkovich B, Marmor R, Singh S, El-Kareh R, et al. Integrated precision medicine: the role of electronic health records in delivering personalized treatment. Wiley Interdiscip Rev Syst Biol Med. 2017; 9: e1378, CrossRef.

Petrone J. FDA approves stroke-detecting AI software. Nat Biotechnol. 2018; 36: 290, CrossRef.

The Atlantic [Internet]. Hsu J, Spectrum. AI could make detecting autism easier. In The Atlantic [updated 2018 Jul 14; cited 2019 Jan 18]. Available from:

Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng. 2018; 2: 749-60, CrossRef.

Fast Company [Internet]. Peters A. Having a heart attack? This AI helps emergency dispatchers find out. In Fast Company [updated 2018 Nov 1; cited 2019 Jan 18]. Available from:

Patel NM, Michelini VV, Snell JM, Balu S, Hoyle AP, Parker JS, et al. Enhancing next-generation sequencing-guided cancer care through cognitive computing. Oncologist. 2018; 23: 179–85, CrossRef.

Dailymail Online [Internet]. 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 [updated 2018 Oct 10; cited 2019 Jan 18]. Available from:

Gurovich Y, Hanani Y, Bar O, Fleischer N, Gelbman D, Basel-Salmon L, et al. DeepGestalt—identifying rare genetic syndromes using deep learning. New York: Cornell University; 2017, article.

Bahl M, Barzilay R, Yedidia AB, Locascio NJ, Yu L, Lehman CD. High-risk breast lesions: a machine learning model to predict pathologic upgrade and reduce unnecessary surgical excision. Radiology. 2018; 286: 810-8, article.

Beam AL, Kohane IS. Translating artificial intelligence into clinical care. JAMA. 2016; 316: 2368-9, article.

Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019; 25: 659, article.

Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, et al. Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nat Med. 2019; 25: 70–4, article.

Health News Review [Internet]. Victory J. What did journalists overlook about the Apple Watch ‘heart monitor’ feature? [updated 2018 Sep; cited 2019 Jan 25]. Available from:

Apple Insider [Internet]. Fingas R. Apple Watch Series 4 EKG tech got FDA clearance less than 24hours before reveal [updated 2018 Sep 18; cited 2019 Jan 25]. Available from:

diatribe Learn [Internet]. Levine B, Brown A. Onduo delivers diabetes clinic and coaching to your smartphone. [updated 2018 March 12; cited 2019 Jan 25]. Available from:

Han Q, Ji M, de Rituerto de Troya IM, Gaur M, Zejnilovic L. A hybrid recommender system for patient–doctor matchmaking in primary care. New York: Cornell University; 2018, article.

Zmora N, Zeevi D, Korem T, Segal E, Elinav E. Taking it personally: personalized utilization of the human microbiome in health and disease. Cell Host Microbe. 2016; 19: 12-20, CrossRef.

Korem T, Zeevi D, Zmora N, Weissbrod O, Bar N, Lotan-Pompan M, et al. Bread affects clinical parameters and induces gut microbiome–associated personal glycemic responses. Cell Metab. 2017; 25: 1243-53.e5, CrossRef.

Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015; 163: 1079-94, CrossRef.

Hall H, Perelman D, Breschi A, Limcaoco P, Kellogg R, McLaughlin T, et al. Glucotypes reveal new patterns of glucose dysregulation. PLoS Biol. 2018; 16: e2005143, CrossRef.

Albers DJ, Levine M, Gluckman B, Ginsberg H, Hripcsak G, Mamykina L. Personalized glucose forecasting for type 2 diabetes using data assimilation. PLoS Comput Biol. 2017; 13: e1005232, CrossRef.

Thaiss CA, Levy M, Grosheva I, Zheng D, Soffer E, Blacher E, et al. Hyperglycemia drives intestinal barrier dysfunction and risk for enteric infection. Science. 2018; 359: 1376-83, CrossRef.

Wu D, Hu D, Chen H, Shi G, Fetahu IS, Wu F, et al. Glucose-regulated phosphorylation of TET2 by AMPK reveals a pathway linking diabetes to cancer. Nature. 2018; 559: 637-41, CrossRef.

Poplin R, Chang PC, Alexander D, Schwartz S, Colthurst T, Ku A, et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018; 36: 983-7, CrossRef.

Sundaram L, Gao H, Padigepati SR, McRae JF, Li Y, Kosmicki JA, et al. Predicting the clinical impact of human mutation with deep neural networks. Nat Genet. 2018; 50: 1161-70, CrossRef.

Zhou J, Theesfeld CL, Yao K, Chen KM, Wong AK, Troyanskaya OG. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat Genet. 2018; 50: 1171-9, CrossRef.

Behravan H, Hartikainen JM, Tengström M, Pylkäs K, Winqvist R, Kosma V, et al. Machine learning identifies interacting genetic variants contributing to breast cancer risk: a case study in Finnish cases and controls. Sci Rep. 2018; 8: 13149, CrossRef.

Lin C, Jain S, Kim H, Bar-Joseph Z. Using neural networks for reducing the dimensions of single-cell RNA-seq data. Nucleic Acids Res. 2017; 45: e156, CrossRef.

Angermueller C, Lee HJ, Reik W, Stegle O. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol. 2017; 18: 67, CrossRef.

AlQuraishi M. End-to-end differentiable learning of protein structure. Cell Syst. 2019; 8: 292-301.e3, CrossRef.

Espinoza JL. Machine learning for tackling microbiota data and infection complications in immunocompromised patients with cancer. J Intern Med. 2018; 284: 189-92, CrossRef.

Van Dijk D, Sharma R, Nainys J, Yim K, Kathail P, Carr AJ, et al. Recovering gene interactions from single-cell data using data diffusion. Cell. 2018; 174: 716-29.e727, CrossRef.

Zitnik M, Nguyen F, Wang B, Leskovec J, Goldenberg A, Hoffman MM, et al. Machine learning for integrating data in biology and medicine: principles, practice, and opportunities. New York: Cornell University; 2018, article.

Listgarten J, Weinstein M, Kleinstiver BP, Sousa AA, Joung JK, Crawford J, et al. Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs. Nat Biomed Eng. 2018; 2: 38-47, CrossRef.

Smalley E. AI-powered drug discovery captures pharma interest. Nat Biotechnol. 2017; 35: 604-5, CrossRef.

Schneider G. Automating drug discovery. Nat Rev Drug Discov. 2018; 17; 97-113, CrossRef.

Chakradhar S. Predictable response: finding optimal drugs and doses using artificial intelligence. Nat Med. 2017; 23: 1244-7, CrossRef.

Lowe D. AI designs organic syntheses. Nature. 2018; 555: 592-3, CrossRef.

Luechtefeld T, Marsh D, Rowlands C, Hartung T. Machine learning of toxicological big data enables read-across structure activity relationships (RASAR) outperforming animal test reproducibility. Toxicol Sci. 2018; 165: 198-212, CrossRef.

Hie B, Cho H, Berger B. Realizing private and practical pharmacological collaboration. Science. 2018; 362: 347-50, CrossRef.

STAT+ [Internet]. Ross C, Swetlitz I. IBM’s Watson supercomputer recommended ‘unsafe and incorrect’ cancer treatments, internal documents show [updated 2018 Jul 25; cited 2019 Jan 25]. Available from:

Healthcare IT News [Internet]. Miliard M. As FDA signals wider AI approval, hospitals have a role to play [updated 2018 May 31; cited 2019 Jan 25]. Available from:


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