Advances and perspective of artificial intelligence in clinical area

Yazhou WU; Xicheng CHEN; Dong YI

doi:10.16016/j.1000-5404.202112092

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Advances and perspective of artificial intelligence in clinical area

Biomedical Engineering | 更新时间：2026-01-09

- Advances and perspective of artificial intelligence in clinical area
- Journal of Army Medical University Vol. 44, Issue 1, Pages: 89-102(2022)
- 作者机构：
  
  400038 重庆，陆军军医大学（第三军医大学）军事预防医学系军队卫生统计学教研室
- 作者简介：
- 基金信息：
  
  the National Natural Science Foundation of China(81872716);the National Natural Science Foundation of China(82173621);the Natural Science Foundation of Chongqing(cstc2020jcyj-zdxmX0017);the Basic Research Project of Army Medical University(2021-2018-037)
- DOI：10.16016/j.1000-5404.202112092
  CLC： R319; R4
- Received：11 December 2021，
  
  Revised：2021-12-17，
  
  Published：15 January 2022
- 稿件说明：
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Yazhou WU, Xicheng CHEN, Dong YI. Advances and perspective of artificial intelligence in clinical area[J]. Journal of Army Medical University, 2022, 44(1): 89-102.
DOI：

Yazhou WU, Xicheng CHEN, Dong YI. Advances and perspective of artificial intelligence in clinical area[J]. Journal of Army Medical University, 2022, 44(1): 89-102. DOI： 10.16016/j.1000-5404.202112092.

摘要

人工智能（artificial intelligence，AI）的提出引发了医学领域的诸多技术创新，并彻底改变了传统医学模式。医学人工智能主要包括机器学习（machine learning

ML）、深度学习（deep learning

DL）、专家系统（expert systems

ES）、智能机器人（intelligent robots

IR）及医疗物联网（internet of medical things

IoMT）等常用和新兴AI技术方法。AI在医学领域的应用主要体现于智能筛查、智能诊断、风险预测和辅助治疗等方面。当前医学AI已经取得了重大突破，大数据质量治理、新技术赋能革新、多领域知识整合和个性化医疗决策等在临床领域中将展示出更为广阔的发展前景。

Abstract

Artificial intelligence (AI) has led to numerous technical innovations in medicine and revolutionized the conventional mode of medicine. AI in medicine mainly consists of machine learning (ML)

deep learning (DL)

expert systems (ES)

intelligent robots (IR)

and the internet of medical things (IoMT) and other emerging AI technology approaches. Intelligent screening

intelligent diagnosis

risk prediction

and adjuvant therapy are major applications of AI in medicine. Currently

AI in medicine has made a significant breakthrough

and quality governance of big data

innovation of new technology

integration of multi domain knowledge and personalized medicine decision-making will show a more promising development in the clinical field.

关键词

Keywords

references

PUPPE F. Introduction to knowledgesystems[J]. Artif Intell Med, 1997, 9(2): 201-203. DOI: 10.1016/S0933-3657(96)00372-7.

KAPLAN A, HAENLEIN M. Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence[J]. Bus Horizons, 2019, 62(1): 15-25. DOI: 10.1016/j.bushor.2018.08.004.

STEFANELLI M. The socio-organizational age of artificial intelligence in medicine[J]. Artif Intell Med, 2001, 23(1): 25-47.

SU M C. Use of neural networks as medical diagnosis expert systems[J]. Comput Biol Med, 1994, 24(6): 419-429. DOI: 10.1016/0010-4825(94)90040-x.

SAMUEL AL. Some studies in machine learning using the game of checkers[J]. IBM J Res Dev, 1959, 3(3): 211-229. DOI: 10.1147/rd.33.0210.

QUINLAN J R. An empirical comparison of genetic and decision-tree classifiers[C]//Machine Learning Proceedings, Amsterdam: Elsevier, 1988: 135-141. DOI: 10.1016/b978-0-934613-64-4.50019-0.

HUANG S, CAI N, PACHECO P P, et al . Applications of support vector machine (SVM) learning in cancer genomics[J ] . Cancer Genomics Proteomics, 2018, 15(1): 41-51. DOI: 10.21873/cgp.20063.

HO T K. The random subspace method for constructing decision forests[J]. IEEE Trans Pattern Anal Mach Intell, 1998, 20(8): 832-844. DOI: 10.1109/34.709601.

BURTON W, MYERS C, RULLKOETTER P. Semi-supervised learning for automatic segmentation of the knee from MRI with convolutional neural networks[J]. Comput Methods Programs Biomed, 2020, 189: 105328. DOI: 10.1016/j.cmpb.2020.105328.

SCHMIDHUBER J. Deep learning in neural networks: an overview[J]. Neural Netw, 2015, 61: 85-117. DOI: 10.1016/j.neunet.2014.09.003.

SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Trans Pattern Anal Mach Intell, 2017, 39(4): 640-651. DOI: 10.1109/TPAMI.2016.2572683.

RONNEBERGER O, FISCHER P, BROX T. U-Net: Convolutional networks for biomedical image segmentation[C ] //NAVAB N, HORNEGGER J, WELLS W, et al . Medical image computing and computer-assisted intervention—MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Cham: Springer, 2015: 234-241. DOI: 10.1007/978-3-319-24574-4_28.

URREA C, MIGNOGNA A. Development of an expert system for pre-diagnosis of hypertension, diabetes mellitus type 2 and metabolic syndrome[J]. Health Informatics J, 2020, 26(4): 2776-2791. DOI: 10.1177/1460458220937095.

BEASLEY R A. Medical robots: current systems and research directions[J]. J Robotics, 2012, 2012: 1-14. DOI: 10.1155/2012/401613.

GAO Y, TAKAGI K, KATO T, et al . Continuum robot with follow-the-leader motion for endoscopic third ventriculostomy and tumor biopsy[J ] . IEEE Trans Biomed Eng, 2020, 67(2): 379-390. DOI: 10.1109/tbme.2019.2913752.

BUCKINGHAM S A, WILLIAMS A J, MORRISSEY K, et al . Mobile health interventions to promote physical activity and reduce sedentary behaviour in the workplace: a systematic review[J ] . Digit Health, 2019, 5: 2055207619839883. DOI: 10.1177/2055207619839883.

MRABET H, BELGUITH S, ALHOMOUD A, et al . A survey of IoT security based on a layered architecture of sensing and data analysis[J ] . Sensors (Basel), 2020, 20(13): E3625. DOI: 10.3390/s20133625.

WU L, ZHANG J, ZHOU W, et al . Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy[J ] . Gut, 2019, 68(12): 2161-2169. DOI: 10.1136/gutjnl-2018-317366.

CHEN D, WU L L, LI Y X, et al . Comparing blind spots of unsedated ultrafine, sedated, and unsedated conventional gastroscopy with and without artificial intelligence: a prospective, single-blind, 3-parallel-group, randomized, single-center trial[J ] . Gastrointest Endosc, 2020, 91(2): 332-339. DOI: 10.1016/j.gie.2019.09.016.

KIANI A, UYUMAZTURK B, RAJPURKAR P, et al . Impact of a deep learning assistant on the histopathologic classification of liver cancer[J ] . NPJ Digit Med, 2020, 3: 23. DOI: 10.1038/s41746-020-0232-8.

MORI Y, KUDO SE, MISAWA M, et al . Real-time use o f artificial intelligence in identification of diminutive polyps during colonoscopy: a prospective study[J ] . Ann Intern Med, 2018, 169(6): 357-366. DOI: 10.7326/M18-0249.

WANG P, BERZIN T M, GLISSEN BROWN J R, et al . Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study[J ] . Gut, 2019, 68(10): 1813-1819. DOI: 10.1136/gutjnl-2018-317500.

SU J R, LI Z, SHAO X J, et al . Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with videos)[J ] . Gastrointest Endosc, 2020, 91(2): 415-424. DOI: 10.1016/j.gie.2019.08.026.

LIU W N, ZHANG Y Y, BIAN X Q, et al . Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy[J ] . Saudi J Gastroenterol, 2020, 26(1): 13-19. DOI: 10.4103/sjg.sjg_377_19.

WANG P, LIU X G, BERZIN T M, et al . Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study[J ] . Lancet Gastroenterol Hepatol, 2020, 5(4): 343-351. DOI: 10.1016/S2468-1253(19)30411-X.

VANDENBERGHE M E, SCOTT M L, SCORER P W, et al . Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer[J ] . Sci Rep, 2017, 7: 45938. DOI: 10.1038/srep45938.

STEINER D F, MACDONALD R, LIU Y, et al . Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer[J ] . Am J Surg Pathol, 2018, 42(12): 1636-1646. DOI: 10.1097/PAS.0000000000001151.

BARINOV L, JAIRAJ A, BECKER M, et al . Impact of data presentation on physician performance utilizing artificial intelligence-based computer-aided diagnosis and decision support systems[J ] . JDigit Imaging, 2019, 32(3): 408-416. DOI: 10.1007/s10278-018-0132-5.

MANGO V L, SUN M, WYNN R T, et al . Should we ignore, follow, or biopsy? impact of artificial intelligence decision support on breast ultrasound lesion assessment[J ] . AJR Am J Roentgenol, 2020, 214(6): 1445-1452. DOI: 10.2214/ajr.19.21872.

LOTTER W, DIAB A R, HASLAM B, et al . Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach[J ] . Nat Med, 2021, 27(2): 244-249. DOI: 10.103 8/s41591-020-01174-9.

YOO Y J, HA E J, CHO Y J, et al . Computer-aided diagnosis of thyroid nodules via ultrasonography: initial clinical experience[J ] . Korean J Radiol, 2018, 19(4): 665-672. DOI: 10.3348/kjr.2018.19.4.665.

MASOOD A, SHENG B, LI P, et al . Computer-Assisted Decision Support System in Pulmonary Cancer detection and stage classification on CT images[J ] . J Biomed Inform, 2018, 79: 117-128. DOI: 10.1016/j.jbi.2018.01.005.

SIM Y, CHUNG M J, KOTTER E, et al . Deep convolutional neural network-based software improves radiologist detection of malignant lung nodules on chest radiographs[J ] . Radiology, 2020, 294(1): 199-209. DOI: 10.1148/radiol.2019182465.

URUSHIBARA A, SAIDA T, MORI K, et al . Diagnosing uterine cervical cancer on a single T2-weighted image: Comparison between deep learning versus radiologists[J ] . Eur J Radiol, 2021, 135: 109471. DOI: 10.1016/j.ejrad.2020.109471.

ESTEVA A, KUPREL B, NOVOA R A, et al . Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks[J ] . Nature, 2017, 546(76 60): 686. DOI: 10.1038/nature22985.

ABRÀMOFF M D, LAVIN P T, BIRCH M, et al . Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices[J ] . NPJ Digit Med, 2018, 1: 39. DOI: 10.1038/s41746-018-0040-6.

KANAGASINGAM Y, XIAO D, VIGNARAJAN J, et al . Evaluation of artificial intelligence-based grading of diabetic retinopathy in primary care[J ] . JAMA Netw Open, 2018, 1(5): e182665. DOI: 10.1001/jamanetworkopen.2018.2665.

KEEL S, LEE P Y, SCHEETZ J, et al . Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study[J ] . Sci Rep, 2018, 8(1): 4330. DOI: 10.1038/s41598-018-22612-2.

NATARAJAN S, JAIN A, KRISHNAN R, et al . Diagnostic accuracy of community-based diabetic retinopathy screening with an offline artificial intelligence system on a smartphone[J ] . JAMA Ophthalmol, 2019, 137(10):1182-1188. DOI: 10.1001/jamaophthalmol.2019.2923.

WU X, HUANG Y, LIU Z, et al . Universal a rtificial intelligence platform for collaborative management of cataracts[J ] . Br J Ophthalmol, 2019, 103(11): 1553-1560. DOI: 10.1136/bjophthalmol-2019-314729.

LIN H, LI R,LIU Z, et al . Diagnostic efficacy and therapeutic decision-making capacity of an artificial intelligence platform for childhood cataracts in eye clinics: a multicentre randomized controlled trial[J ] . EClinicalMedicine, 2019, 9: 52-59. DOI: 10.1016/j.eclinm.2019.03.001.

WU X L, TAO Y, QIU Q C, et al . Application of image recognition-based automatic hyphae detection in fungal keratitis[J ] . Australas Phys Eng Sci Med, 2018, 41(1): 95-103. DOI: 10.1007/s13246-017-0613-8.

ESHEL Y, HOURI-YAFIN A, BENKUZARI H, et al . Evaluation of the parasight platform for malaria diagnosis[J ] . J Clin Microbiol, 2017, 55(3): 768-775. DOI: 10.1128/JCM.02155-16.

TURBÉ V, HERBST C, MNGOMEZULU T, et al . Deep learning of HIV field-based rapid tests[J ] . Nat Med, 2021, 27(7): 1165-1170. DOI: 10.1038/s41591-021-01384-9.

MINAEE S, KAFIEH R, SONKA M, et al . Deep-COVID: Predicting COVID-19 from ch est X-ray images using deep transfer learning[J ] . Med Image Anal, 2020, 65: 101794. DOI: 10.1016/j.media.2020.101794.

HUANG L, HAN R, AI T, et al . Serial quantitative chest CT assessment of COVID-19: a deep learning approach[J ] . Radiol Cardiothorac Imaging, 2020, 2(2): e200075. DOI: 10.1148/ryct.2020200075.

AHUJA S, PANIGRAHI B K, DEY N, et al . Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices[J ] . Appl Intell (Dordr), 2020: 1-15. DOI: 10.1007/s10489-020-01826-w.

SHORFUZZAMAN M, MASUD M, ALHUMYANI H, et al . Artificial neural network-based deep learning model for COVID-19 patient detection using X-ray chest images[J ] . J Healthc Eng, 2021, 2021: 5513679. DOI: 10.1155/2021/5513679.

QUIROZ J C, FENG Y Z, CHENG Z Y, et al . Development and validation of a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data: retrospective study[J ] . JMIR Med Inform, 2021, 9(2): e24572. DOI: 10.2196/24572.

ARBABSHIRANI M R, FORNWALT B K, MONGELLUZZO G J, et al . Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration[J ] . NPJ Digit Med, 2018, 1: 9. DOI: 10.1038/s41746-017-0015-z.

TITANO J J, BADGELEY M, SCHEFFLEIN J, et al . Automated deep-neural-network surveillance of cranial images for acute neurologic events[J ] . Nat Med, 2018, 24(9): 1337-1341. DOI: 10.1038/s41591-018-0147-y.

NAGARATNAM K, HARSTON G, FLOSSMANN E, et al . Innovative use of artificial intelligence and digital communication in acute stroke pathway in response to COVID-19[J ] . Future Healthc J, 2020, 7(2): 169-173. DOI: 10.7861/fhj.2020-0034.

LO C M, HUNG P H, LIN D T. Rapid assessment of acute ischemic stroke by computed tomography using deep convolutional neural networks[J]. J Digit Imaging, 2021, 34(3): 637-646. DOI: 10.1007/s10278-021-00457-y.

BIBI N, SIKANDAR M, UD DIN I, et al . IoMT-based automated detection and classification of leukemia using deep learning[J ] . J Healthc Eng, 2020, 2020: 6648574. DOI: 10.1155/2020/6648574.

HAMEDAN F, OROOJI A, SANADGOL H, et al . Clinical decision support system to predict chronic kidney disease: a fuzzy expert system approach[J ] . Int J Med Inform, 2020, 138: 104134. DOI: 10.1016/j.ijmedinf.2020.104134.

PARK A, CHUTE C, RAJPURKAR P, et al . Deep learning-assisted diagnosis of cerebral aneurysms using the HeadXNet model[J ] . JAMA Netw Open, 2019, 2(6): e195600. DOI: 10.1001/jamanetworkopen.2019.5600.

WONG G L, MA A J, DENG H, et al . Machine learning model to predict recurrent ulcer bleeding in patients with history of idiopathic gastroduodenal ulcer bleeding[J ] . Aliment Pharmacol Ther, 2019, 49(7): 912-918. DOI: 10.1111/apt.15145.

BIEN N, RAJPURKAR P, BALL R L, et al . Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet[J ] . PLoS Med, 2018, 15(11): e1002699. DOI: 10.1371/journal.pmed.1002699.

LINDSEY R, DALUISKI A, CHOPRA S, et al . Deep neural network improves fracture detection by clinicians[J ] . Proc Natl Acad Sci USA, 2018, 115(45): 11591-11596. DOI: 10.1073/pnas.1806905115.

FUY C, LIU R, LIU Y , et al . Intertrochanteric fracture visualization and analysis using a map projection technique[J ] . Med Biol Eng Comput, 2019, 57(3): 633-642. DOI: 10.1007/s11517-018-1905-1.

YAO C H, WU S D, LIU Z, et al . A deep learning model for predicting chemical composition of gallstones with big data in medical Internet of Things[J ] . Future Gener Comput Syst, 2019, 94: 140-147. DOI: 10.1016/j.future.2018.11.011.

MCCOY A, DAS R. Reducing patient mortality, length of stay and readmissions through machine learning-based Sepsis prediction in the emergency department, intensive care unit and hospital floor units[J]. BMJ Open Qual, 2017, 6(2): e000158. DOI: 10.1136/bmjoq-2017-000158.

SHIMABUKURO D W, BARTON C W, FELDMAN M D, et al . Effect of a machine learning-based severe Sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial[J ] . BMJ Open Respir Res, 2017, 4(1): e000234. DOI: 10.1136/bmjresp-2017-000234.

GIANNINI H M, GINESTRA J C, CHIVERS C, et al . A machine learning algorithm to predict severe Sepsis and septic shock: development, implementation, and impact on clinical practice[J ] . Crit Care Med, 2019, 47(11): 1485-1492. DOI: 10.1097/CCM.0000000000003891.

GINESTRA J C, GIANNINI H M, SCHWEICKERT W D, et al . Clinician perception of a machine learning-based early warning system designed to predict severe Sepsis and septic shock[J ] . Crit Care Med, 2019, 47(11): 1477-1484. DOI: 10.1097/CCM.0000000000003803.

CHEN M, YANG J, ZHOU J H, et al . 5G-smart diabetes: toward personalized diabetes diagnosis with healthcare big data clouds[J ] . IEEE Commun Mag, 2018, 56(4): 16-23. DOI: 10.1109/MCOM.2018.1700788.

KUMAR P M, LOKESH S, VARATHARAJAN R, et al . Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier[J ] . Future Gener Comput Syst, 2018, 86(1): 527-534. DOI: 10.1016/j.future.2018.04.036.

ROMERO-BRUFAU S, WYATT K D, BOYUM P, et al . A lesson in implementation: a pre-post study of providers' experience with artificial intelligence-based clinical decision support[J ] . Int J Med Inform, 2020, 137(1): 104072. DOI: 10.1016/j.ijmedinf.2019.104072.

BOUTILIER J J, CHAN T C Y, RANJAN M, et al . Risk stratification for early detection of diabetes and hypertension in resource-l imited settings: machine learning analysis[J ] . J Med Internet Res, 2021, 23(1): e20123. DOI: 10.2196/20123.

CONNELL A, MONTGOMERY H, MARTIN P, et al . Evaluation of a digitally-enabled care pathway for acute kidney injury management in hospital emergency admissions[J ] . NPJ Digit Med, 2019, 2(1): 67. DOI: 10.1038/s41746-019-0100-6.

AOKI T, YAMADA A, AOYAMA K, et al . Clinical usefulness of a deep learning-based system as the first screening on small-bowel capsule endoscopy reading[J ] . Dig Endosc, 2020, 32(4): 585-591. DOI: 10.1111/den.13517.

BRENNAN M, PURI S, OZRAZGAT-BASLANTI T, et al . Comparing clinical judgment with the MySurgeryRisk algorithm for preoperative risk assessment: a pilot usability study[J ] . Surgery, 2019, 165(5): 1035-1045. DOI: 10.1016/j.surg.2019.01.002.

WIJNBERGE M, GEERTS B F, HOL L, et al . Effect of a machine learning-derived early warning system for intraoperative hypotension vs standard care on depth and duration of intraoperative hypotension during elective noncardiac surgery: the HYPE randomized clinical trial[J ] . JAMA, 2020, 323(11): 1052-1060. DOI: 10.1001/jama.2020.0592..

ZHOU B, SHE J, WANG Y, et al . Utility of ferritin, procalcitonin, and C-reactive protein in severe patients with 2019 novel coronavirus disease[J ] . Res Sq, 2020. DOI: 10.21203/rs.3.rs-18079/v1.

BOOTH A L, ABELS E, MCCAFFREY P. Development of a prognostic model for mortality in COVID-19 infection using machine learning[J]. Mod Pathol, 2021, 34(3): 522-531. DOI: 10.1038/s41379-020-00700-x.

BIRD D, NIX M G, MCCALLUM H, et al . Multicentre, deep learning, synthetic-CT generation for ano-rectal MR-only radiotherapy treatment planning[J ] . Radiother Oncol, 2021, 156(1): 23-28. DOI: 10.1016/j.radonc.2020.11.027.

YANG Z J, OLSZEWSKI D, HE C J, et al . Machine learning and statistical prediction of patient quality-of-life after prostate radiation therapy[J ] . Comput Biol Med, 2021, 129(1): 104127. DOI: 10.1016/j.compbiomed.2020.104127.

NICOLAE A, SEMPLE M, LU L, et al . Conventional vs machine learning-based treatment planning in prostate brachytherapy: Results of a Phase I randomized controlled trial[J ] . Brachytherapy, 2020, 19(4): 470-476. DOI: 10.1016/j.brachy.2020.03.004.

KATZMAN J L, SHAHAM U, CLONINGER A, et al . DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network[J ] . BMC Med Res Methodol, 2018, 18(1): 24. DOI: 10.1186/s12874-018-0482-1.

MCNAMARA D M, GOLDBERG S L, LATTS L, et al . Differential impact of cognitive computing augmented by real world evidence on novice and expert oncologists[J ] . Cancer Med, 2019, 8(15): 6578-6584. DOI: 10.1002/cam4.2548.

KHOZEIMEH F, ALIZADEHSANI R, ROSHANZAMIR M, et al . An expert system for selecting wart treatment method[J ] . Comput Biol Med, 2017, 81: 167-175. DOI: 10.1016/j.compbiomed.2017.01.001.

VOERMANS A M, MEWES J C, BROYLES M R, et al . Cost-effectiveness analysis of a procalcitonin-guided decision algorithm for antibiotic stewardship using real-world US hospital data[J ] . OMICS, 2019, 23(10): 508-515. DOI: 10.1089/omi.2019.0113.

RAWSON T M, HERNANDEZ B, MOORE L S P, et al . A real-world evaluation of a case-based reasoning algorithm to support antimicrobial prescribing decisions in acute care[J ] . Clin Infect Dis, 2021, 72(12): 2103-2111. DOI: 10.1093/cid/ciaa383.

SEGAL G, SEGEV A, BROM A, et al . Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting[J ] . J Am Med Inform Assoc, 2019, 26(12): 1560-1565. DOI: 10.1093/jamia/ocz135.

WANG S V, ROGERS J R, JIN Y Z, et al . Stepped-wedge randomised trial to evaluate population health intervention designed to increase appropriate anticoagulation in patients with atrial fibrillation[J ] . BMJ Qual Saf, 2019, 28(10): 835-842. DOI: 10.1136/bmjqs-2019-009367.

HOOSHMAND S A, ZAREI GHOBADI M, HOOSHMAND S E, et al . A multimodal deep learning-based drug repurposing approach for treatment of COVID-19[J ] . Mol Divers, 2021, 25(3): 1717-1730. DOI: 10.1007/s11030-020-10144-9.

KE Y Y, PENG T T, YEH T K, et al . Artificial intelligence approach fighting COVID-19 with repurposing drugs[J ] . Biomed J, 2020, 43(4): 355-362. DOI: 10.1016/j.bj.2020.05.001.

ZHANG Q, HAN X G, XU Y F, et al . Robotic navigation during spine surgery[J ] . Expert Rev Med Devices, 2020, 17(1): 27-32. DO I: 10.1080/17434440.2020.1699405.

XIE X L, WU Y, LI K W, et al . Preliminary experiences with robot-assisted choledochal cyst excision using the da vinci surgical system in children below the age of one[J ] . Front Pediatr, 2021, 23(9): 741098. DOI: 10.3389/fped.2021.741098.

MATTHEIS S, HUSSAIN T, HÖING B, et al . Robotics in laryngeal surgery[J ] . Oper Tech Otolaryngol Head Neck Surg, 2019, 30(4): 284-288. DOI: 10.1016/j.otot.2019.09.012.

TROISI R I, PEGORARO F, GIGLIO M C, et al . Robotic approach to the liver: Open surgery in a closed abdomen or laparoscopic surgery with technical constraints?[J ] . Surg Oncol, 2020, 33(1): 239-248. DOI: 10.1016/j.suronc.2019.10.012.

ZHOU S K, LE H N, LUU K, et al . Deep reinforcement learning in medical imaging: a literature review[J ] . Med Image Anal, 2021, 73(1): 102193. DOI: 10.1016/j.media.2021.102193.

ALTON S. Learning how to learn: Meta-learning strategies for the challenges of learning pharmacology[J]. Nurse Educ Today, 2016, 38(1): 2-4.DOI: 10.1016/j.nedt.2016.01.003.

BALDWIN E, HAN J L, LUO W T, et al . On fusion methods for knowledge discovery from multi-omics datasets[J ] . Comput Struct Biotechnol J, 2020, 5(18): 509-517. DOI: 10.1016/j.csbj.2020.02.011.

TRAN B X, VU G T, HA G H, et al . Global evolution of research in artificial intelligence in health and medicine: a bibliometric study[J ] . J Clin Med, 2019, 8(3): E360. DOI:10.3390/jcm8030360.

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National Key Laboratory of Trauma and Chemical Poisoning， Department of War Trauma， Shock and Blood Transfusion， Daping Hospital， Army Medical University（Third Military Medical University）

Department of Diagnostic Radiology， Eastern Theatre General Hospital

Department of Radiology， Daping Hospital， Army Medical University

Infervision Medical Technology Co.， Ltd

Department of Radiology，First Affiliated Hospital， Army Medical University （Third Military Medical University）

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