Abstract
The application of artificial intelligence (AI) technology in the medical field has experienced a long history of development. In turn, some long-standing points and challenges in the medical field have also prompted diverse research teams to continue to explore AI in depth. With the development of advanced technologies such as the Internet of Things (IoT), cloud computing, big data, and 5G mobile networks, AI technology has been more widely adopted in the medical field. In addition, the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective way. In this work, we examine the technical basis of IoT, cloud computing, big data analysis and machine learning involved in clinical medicine, combined with concepts of specific algorithms such as activity recognition, behavior recognition, anomaly detection, assistant decision-making system, to describe the scenario-based applications of remote diagnosis and treatment collaboration, neonatal intensive care unit, cardiology intensive care unit, emergency first aid, venous thromboembolism, monitoring nursing, image-assisted diagnosis, etc. We also systematically summarize the application of AI and IoT in clinical medicine, analyze the main challenges thereof, and comment on the trends and future developments in this field.
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References
GHWA/WHO. A Universal Truth: No Health Without a Workforce
WHO. State of the World’s Nursing Report 2020
Kruk ME, Gage AD, Joseph NT, et al. Mortality due to low-quality health systems in the universal health coverage era: A systematic analysis of amenable deaths in 137 countries. Lancet, 2018,392(10160):2146–2147
World Population Prospects 2019: Highlights[B]. ONU. United Nations. 2019
Healthy Aging Team. The Top 10 Most Common Chronic Conditions in Older Adults.National council on ageing. Available from: https://dailycaring.com/prevent-and-manage-the-10-most-common-chronic-diseases-in-older-adults/
Jaul E, Barron J. Age-Related Diseases and Clinical and Public Health Implications for the 85 Years Old and Over Population. Front Public Health, 2017,5:335–335
van den Bussche H, Koller D, Kolonko T, et al. Which chronic diseases and disease combinations are specific to multimorbidity in the elderly? Results of a claims data based cross-sectional study in Germany. BMC Public Health, 2011,11:101
Mofizul IM, Valderas JM, Laurann Y, et al. Multimorbidity and Comorbidity of Chronic Diseases among the Senior Australians: Prevalence and Patterns. Plos One, 2014,9(1):e83783
Zhao C, Liping W, Zhu Q, et al. Prevalence and correlates of chronic diseases in an elderly population: A community-based survey in Haikou. Plos One, 2018, 13(6):e0199006
Burroughs A. What Is a Tele-ICU and How Does It Work? Available from https://healthtechmagazine.net/article/2020/09/what-tele-icu-and-how-does-it-work
Fuller T, Fox B, Lake D, et al. Improving real-time vital signs documentation. Nurs Manage. 2018,49(1):28–33
Martine L. Measuring patient and clinical effectiveness. Microsoft Industry Blogs - United Kingdom Available from: https://cloudblogs.microsoft.com/industry-blog/en-gb/health/2020/07/03/measuring-patient-and-clinical-effectiveness/
Prajapati B, Parikh S, Patel J. An Intelligent Real Time IoT Based System (IRTBS) for Monitoring ICU Patien. International Conference on Information and Communication Technology for Intelligent Systems. Springer, Cham, 2017
Hka F, Swk B, Ep C, et al. The role of fifth-generation mobile technology in prehospital emergency care: An opportunity to support paramedics. Health Policy Technol, 2020, 9(1):109–114
Tang X. The role of artificial intelligence in medical imaging research. BJR Open, 2019,2(1):20190031
Chamberlin J, Kocher MR, Waltz J, et al. Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value. BMC Med, 2021,19(1):55
Wang XN, Dai L, Li ST, et al. Automatic Grading System for Diabetic Retinopathy Diagnosis Using Deep Learning Artificial Intelligence Software. Curr Eye Res, 2020,45:1550–1555
Dey D, Slomka PJ, Leeson P, et al. Artificial Intelligence in Cardiovascular Imaging. J Am Coll Cardiol, 2019, 73(11):1317–1335
Alkhatib H, Faraboschi P, Frachtenberg E, et al. IEEE CS 2022 Report. IEEE Computer Society, 2014:25–27
Kosmatos EA, Tselikas ND, Boucouvalas AC. Integrating RFIDs and Smart Objects into a Unified-Internet of Things Architecture. Adv Internet Things, 2011,1(1):5–12
Madakam S, Ramaswamy R, Tripathi S. Internet of Things (IoT): A Literature Review. J Comp Commun, 2015,3(3):164–173
Gubbi J, Buyya R, Marusic S, et al. Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions. Future Gener Comp Syst, 2013,29(7):1645–1660
Haider F. Cellular architecture and key technologies for 5G wireless communication networks. J Chongqing Univ Posts Telecommun, 2014,52(2):122–130
Joyia GJ, Liaqat RM, Farooq A, et al. Internet of medical things (IOMT): Applications, benefits and future challenges in healthcare domain. J Commun, 2017,12(4):240–247
Hingmire M, Bagjilewale M, Dakhole M. What is Cloud Computing. Springer Verlag Ny, 2017,17(1): 3–20
Sultan N. Making use of cloud computing for healthcare provision: Opportunities and challenges. Int J Inform Manage, 2014,34(2):177–184
Wang L, von Laszewski G, Younge A, et al. Cloud Computing: a Perspective Study. New Generat Comput, 2010,28(2):137–146
Ahuja SP, Sindhu M, Jesus Z. A Survey of the State of Cloud Computing in Healthcare. Network Commun Technol, 2012,1(2)12–19
Marjani M, Nasaruddin F, Gani A, et al. Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges. IEEE Access, 2017,5(99):5247–5261
Kufrin R. Decision trees on parallel processors. Machine Intelligence Pattern Recognition, 1997,20:279–306
Gondy LA, Thomas C, Bayes N. Programs for machine learning. Advances in Neural Inform Proc Syst, 1993,79(2):937–944
Judith E, James M. Artificial neural networks. Cancer, 2001,91(S8):1615–1635
Krallinger M, Leitner F, Vazquez M, et al. Text Mining. Compr Biomed Phys, 2014,6(10 Supplement):51–66
Quan XX, Yang J F, Luo Z. Models in digital business and economic forecasting based on big data IoT data visualization technology. Pers Ubiquit Comput, 2021 (https://doi.org/10.1007/s00779-021-01603-7)
Hua X, Aldrich MC, Chen Q, et al. Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality. J Am Med Inform Assoc, 2015(1):179–191
Dash S, Shakyawar SK, Sharma M, et al. Big data in healthcare: management, analysis and future prospects. J Big Data, 2019,6(1):54
Bhardwaj R, Nambiar AR, Dutta D. A Study of Machine Learning in Healthcare. 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC). July 4–7, 2017, Turin, Italy
Abramson N, Braverman DJ, Sebestyen GS. Pattern Recognition and Machine Learning. Public Am Statist Assoc, 2006,103(4):886–887
Avci A, Bosch S, Marin-Perianu M, et al. Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey. ARCS’10 - 23th International Conference on Architecture of Computing Systens 2010, Workshop Proceedings, February 22–23, 2010, Hannover, Germany. VDE, 2010
Ijjina EP, Mohan CK. Hybrid deep neural network model for human action recognition. Appl Soft Comput, 2016:936–952
Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge: MIT Press, 2016:367–415.
Liu X, Yang XD. Multi-stream with deep convolutional neural networks for human action recognition in videos. Neural Information Processing. Cham: Springer International Publishing, 2018:251–262.
Wang LL, Ge LZ, Li RF, et al. Three-stream CNNs for action recognition. Pattern Recog Lett, 2017,92:33–40
Tran D, Bourdev L, Fergus R, et al. Learning Spatiotemporal Features with 3D Convolutional Networks. 2015 IEEE International Conference on Computer Vision (ICCV), December 7–13, 2015, Santiago, Chile, IEEE, 2015:4489–4497
Qiu Z, Yao T, Mei T. Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks. 2017 IEEE International Conference on Computer Vision (ICCV), October 22–29, 2017, Venice, IEEE, 2017:5533–5541
Zhou Y, Sun X, Zha ZJ, et al. MiCT: Mixed 3D/2D Convolutional Tube for Human Action Recognition. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 18–23 2018, Salt Lake City, UT, IEEE, 2018:449–458
Ng YH, Hausknecht M, Vijayanarasimhan S, et al. Beyond short snippets: Deep networks for video classification. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7–12, 2015, Boston, MA, USA, IEEE, 2015:4694–4702
Du W, Wang Y, Yu Q. RPAN: An End-to-End Recurrent Pose-Attention Network for Action Recognition in Videos. 2017 IEEE International Conference on Computer Vision (ICCV), October 22–29, 2017, Venice, IEEE, 2017:3725–3734
Ren ZH, Xu HY, Feng SL, et al. Sequence labeling Chinese word segmentation method based on LSTM networks. Comput Appl Res, 2017,34(5):1321–1324
Wsy A, Syh B. A process-mining framework for the detection of healthcare fraud and abuse. Exp Syst Appl, 2006,31(1):56–68
Alabdulkarim A, Al-Rodhaan M, Al-Dhelaan TA. A Privacy-Preserving Algorithm for Clinical Decision-Support Systems Using Random Forest. Comput Mater Contin, 2019,58(3):585–601
Tama BA, Lim S. A Comparative Performance Evaluation of Classification Algorithms for Clinical Decision Support Systems. Mathematics, 2020(8):1814
Patil K, Mohammad S. Big data privacy: A technological perspective and review. SSRN Electr J, 2017,4(11):159–162
Abouelmehdi K, Beni-Hessane A, Khaloufi H. Big healthcare data: preserving security and privacy. J Big Data, 2018,5(1):1
Zhang DW, Li X, Jiang LX. New medical hotspot: remote collaborative diagnosis and treatment. Sci Technol Rev, 2017,35(10):26–31
Kulkarni A, Sathe S, Healthcare applications of the Internet of Things: A Review. Int J Comput Sci Inform Technol, 2014,5(5):6229–6232
Lu D, Tao L, The application of IOT in medical system. 2011 IEEE International Symposium on IT in Medicine and Education, December 9–11, 2011, Guangzhou, China, 272–275
Zhou WH, Xiao TT. Digital future of neonatal critical care medicine. Chin J Pediat (Chinese), 2021,59(4):261–263
Barker DJ. Human growth and chronic disease: a memorial to Jim Tanner. Ann Hum Biol, 2012,39(5):335–341
Yang L, Liu X, Li Z, et al. Genetic aetiology of early infant deaths in a neonatal intensive care unit. J Med Genet, 2020,57:169–177
Yang L, Kong Y, Dong X, et al. Clinical and genetic spectrum of a large cohort of children with epilepsy in China. Genet Med, 2019,21(3):564–571
Pavel AM, Rennie JM, de Vries LS, et al. A machine learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial. Lancet Child Adolesc Health, 2020,4(10):740–749
Olga BL, Gao XM, Ehsan Y, et al. E-Healthcare: Remote Monitoring, Privacy, and Security. Microwave Symposium IEEE, December 12–14, 2014, Marrakech, Morocco
Masino AJ, Harris MC, Forsyth D, et al. Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data. PLoS One, 2019,14(2):e0212665
Sanchez Pinto LN, Stroup EK, Pendergrast T, et al. Derivation and validation of novel phenotypes of multiple organ dysfunction syndrome in critically ill children. JAMA NetwOpen, 2020,3(8):e209271
Kannathal N, Acharya UR, Lim CM, et al. Classification of cardiac patient states using artificial neural network. Exp Clin Cardiol, 2003,8(4):206–211
Sengupata PP, Huang YM, Bansal M, et al. Cognitive machine-learning algorithm for cardiac imaging: a pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy. Cire Cardiovasc Imaging, 2016,9(6):e004330
Schoenrath F, Markendorf S, Brauchlin AE, et al. Robotassisted training early after cardiac surgery. J Card Surg, 2015,30(7):574–58
Ottavinano M, Vera-Munoz C, Arredondo MT, et al. Innovative self management system for guided cardiac rehabilitation. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society, August 30–September 3, 2011, Boston, USA, 2011:1559-1562
Chen AY, Lu TY, Ma MH, et al. Demand Forecast Using Data Analytics for the Preallocation of Ambulances. IEEE J Biomed Health Inform, 2016,20(4):1178–1187
Tsien CL, Fraser HS, Long WJ, et al. Using classification tree and logistic regression methods to diagnose myocardial infarction. Stud Health Technol Inform, 1998,52(1):493–497
Green M, Bjrk J, Forberg J, et al. Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room. Artif Intell Med, 2006,38(3):305–318
Bentley P, Ganesalingam J, Carlton Jones AL, et al. Prediction of stroke thrombolysis outcome using CT brain machine learning. Neuroimage Clin, 2014,4: 635–640
Toltzis P, Soto-Campos G, Shelton C, et al. Evidence Based Pediatric Outcome Predictors to Guide the Allocation of Critical Care Resources in a Mass Casualty Event. Pediatr Crit Care Med, 2015,16(7):e207–e216
Franc JM, Ingrassia PL, Verde M, et al. A simple graphical method for quantification of disaster management surge capacity using computer simulation and process-control tools. Prehosp Disaster Med, 2015,30(1):9–15
Zhai Z, Kan Q, Li W, et al. VTE risk profiles and prophylaxis in medical and surgical inpatients: The identification of Chinese hospitalized patients’ risk profile for venous thromboembolism(DissolVE-2)-a cross-sectional study. Chest, 2019,155(1):114
Cohen AT, Tapson VF, Bergmann JF, et al. Venous thromboembolism risk and prophylaxis in the acute hospital care setting (ENDORSE study): a multinational cross-sectional study. Lancet, 2008,371(9610):387–394
Wang LJ, Pang J, Wang D, et al. FX. Design and construction of intelligent early warning system for venous thrombosis risk under big data technology. Chin Digit Med (Chinese), 2020,15(9):27–29
Meng Y, Li XY, Su JF, et al. Design and implementation of prevention and treatment system for venous thromboembolism (VTE). Chin Digit Med (Chinese), 2020,15(12):21–23
Integrated Care Platform[DB/OL]. [2021-09-22] https://www.vitalerter.com/
ECRI Institute. Top 10 health technology hazards for 2020[EB/OL]. (2019-12-20)[2020-01-01] http://www.ecri.org
AACN. Practice alert: alarm management [EB/OL]. (2017-11-22). [2020-01-01] http://ccn.aacnjournals.org
Siebig S, Sieben W, Kollmann F, et al. Users’opinions on intensive care unit alarms-a survey of German intensive care units. Anaesth Intensive Care, 2009, 37(1): 112–116
Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. J Am Med Assoc, 2016,316(22):2402–2410
Esteva A, Kuprel B, Novoa RA, et al. Dermatologistlevel classification of skin cancer with deep neural networks. Nature, 2017,542:115–118
Zhang K, Liu XH, Shen J, et al. Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell, 2020,181(6):1423–1433
Soualmi A, Alti A, Laouamer L. Medical Data Protection Using BlindWatermarking Technique. Enabl AI Appl Data Sci, 2020:557
Tuli S, Tuli S, Wander G, et al. Next Generation Technologies for Smart Healthcare: Challenges, Vision, Model, Trends and Future Directions. Intern Technol Let, 2020,3:e145
Ahamed F, Farid F. Applying Internet of Things and Machine-Learning for Personalized Healthcare: Issues and Challenges. 2018 International Conference on Machine Learning and Data Engineering (iCMLDE), IEEE Computer Society, December 03–07, 2018, Sydney, Australia
Tuya Inc., Gartner Group. 2021 Global AIoT Developers Ecosystem White Paper. Tech Show Developers Conference, December 29, 2020, Hangzhou, China
Bangui H, Rakrak S, Raghay S, et al. Moving to the Edge-Cloud-of-Things: Recent Advances and Future Research Directions. Electronics, 2018,7(11):309
Alaybeyi S, Lheureux B. Survey Analysis: Artificial Intelligence Establishes a Foothold in IoT Projects. Gartner, Research, September 20, 2019. https://www.gartner.com/en/documents/3968034/survey-analysis-artificial-intelligence-establishes-a-fo
Zhou Z, Shuai YU, Chen X. Edge intelligence:a new nexus of edge computing and artificial intelligence. Big Data Res, 2019,5(2):53–63
Ferdinand AS, Kelaher M, Lane CR, et al. An implementation science approach to evaluating pathogen whole genome sequencing in public health. Genome Med, 2021,13(1):121
European Centre for Disease Prevention and Control. Monitoring the use of whole-genome sequencing in infectious disease surveillance in Europe. Stockholm: ECDC; 2018
Qiu T, Yang Y, Qiu J, et al. CE-BLAST makes it possible to compute antigenic similarity for newly emerging pathogens. Nat Commun, 2018,9(1):1772
World Robotics 2020 Report [DB/OL]. [2020-09-24] https://ifr.org/news/record-2.7-million-robots-work-in-factories-around-the-globe
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Lu, Zx., Qian, P., Bi, D. et al. Application of AI and IoT in Clinical Medicine: Summary and Challenges. CURR MED SCI 41, 1134–1150 (2021). https://doi.org/10.1007/s11596-021-2486-z
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DOI: https://doi.org/10.1007/s11596-021-2486-z