Artificial Intelligence and Medicine
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
The introduction of artificial intelligence (AI) has resulted in numerous technological advancements in the medical profession and a radical transformation of the old medical model. Artificial intelligence in medicine consists mostly of machine learning, deep learning, expert systems, intelligent robotics, the internet of medical things, and other prevalent and new AI technology. The primary applications of AI in the medical industry are intelligent screening, intelligent diagnosis, risk prediction, and supplemental treatment. Presently, medical AI has achieved significant advances, and big data quality management, new technology empowerment innovation, multi-domain knowledge integration, and personalized medical decision-making will exhibit greater growth potential in the clinical arena.
##plugins.themes.bootstrap3.article.details##
Artificial Intelligence, Big Data, Machine Learning, Clinical Medicine
2. Haenlein M, Kaplan A. A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. Calif Manag Rev 2019; 61(4):5-14. DOI: https://doi.org/10.1177/0008125619864925
3. Macpherson T, Churchland A, Sejnowski T, DiCarlo J, Kamitani Y, Takahashi H, Hikida T. Natural and artificial intelligence: A brief introduction to the interplay between AI and neuroscience research. Neur Netw 2021; 144:603-613. DOI: https://doi.org/10.1016/j.neunet.2021.09.018
4. Amisha, Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care 2019; 8(7):2328-2331. DOI: https://doi.org/10.4103/jfmpc.jfmpc_440_19
5. Berghoff C, Neu M, von Twickel A. Vulnerabilities of connectionist ai applications: Evaluation and defense. Front Big Data 2020; 3:23. DOI: https://doi.org/10.3389/fdata.2020.00023
6. Daley B. What is machine learning? 2017; Last access: June 22, 2022. Available at: https://theconversation.com/what-is-machine-learning-76759
7. Quinlan JR. Introduction of decision tree. Mach Learn 1986; 1:81-106.
8. Stoean C, Stoean R. Support Vector Machines and Evolutionary Algorithms for Classification: Single or together. ISBN 978-3-319-06940-1. 2014. DOI: https://doi.org/https://doi.org/10.1007/978-3-319-06941-8
9. Jaiswal JK, Samikannu R. Application of random forest algorithm on feature subset selection and classification and regression. 2017 World Congress on Computing and Communication Technologies (WCCCT), 2017, pp. 65-68. DOI: https://doi.org/10.1109/WCCCT.2016.25
10. Aizenberg IN, Aizenberg NN, Vandewalle J. Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications. 2012; ISBN 978-0-7923-7824-2. DOI: https://doi.org/10.1007/978-1-4757-3115-6
11. Guo Z, Bai J, Lu Y, Wang X, Cao K, Song Q, Sonka M, Yin Y. Deep centerline: A multi-task fully convolutional network for centerline extraction. 2019. DOI: https://doi.org/10.1007/978-3-030-20351-1_34
12. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, LNCS, Vol.9351: 234--241, 2015, available at arXiv:1505.04597. DOI: https://doi.org/10.48550/arXiv.1505.04597
13. Javaid M, Haleem A, Singh RP, Suman R. Substantial capabilities of robotics in enhancing industry 4.0 implementation. Cog Robot 2021; 1:58-75. DOI: https://doi.org/10.1016/j.cogr.2021.06.001
14. Kelly JT, Campbell KL, Gong E, Scuffham P. The Internet of Things: Impact and implications for health care delivery. J Med Internet Res 2020; 22(11):e20135. DOI: https://doi.org/10.2196/20135
15. Dwivedi R, Mehrotra D, Chandra S. Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review. J Oral Biol Craniofac Res 2022; 12(2):302-318. DOI: https://doi.org/10.1016/j.jobcr.2021.11.010
16. Jiménez Pérez M, Grande RG. Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review. World J Gastroenterol 2020; 26(37):5617-5628. DOI: https://doi.org/10.3748/wjg.v26.i37.5617
17. McGill SK, Evangelou E, Ioannidis JP, Soetikno RM, Kaltenbach T. Narrow band imaging to differentiate neoplastic and non-neoplastic colorectal polyps in real time: A meta-analysis of diagnostic operating characteristics. Gut 2013; 62(12):1704-1713. DOI: https://doi.org/10.1136/gutjnl-2012-303965
18. Wang W, Tian J, Zhang C, Luo Y, Wang X, Li J. An improved deep learning approach and its applications on colonic polyp images detection. BMC Med Imaging 2020; 20:83. DOI: https://doi.org/10.1186/s12880-020-00482-3
19. Lotter W, Diab AR, Haslam B, Kim JG, Grisot G, Wu E, Wu K, Onieva JO, Boyer Y, Boxerman JL, Wang M, Bandler M, Vijayaraghavan GR, Gregory Sorensen A. Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach. Nat Med 2021; 27(2):244-249. DOI: https://doi.org/10.1038/s41591-020-01174-9
20. Kanclerz P, Tuuminen R, Khoramnia R. Imaging modalities employed in diabetic retinopathy screening: A review and meta-analysis. Diagnostics (Basel) 2021; 11(10):1802. DOI: https://doi.org/10.3390/diagnostics11101802
21. Noriega A, Meizner D, Camacho D, Enciso J, Quiroz-Mercado H, Morales-Canton V, Almaatouq A, Pentland A. Screening diabetic retinopathy using an automated retinal image analysis system in independent and assistive use cases in Mexico: Randomized controlled trial. JMIR Form Res 2021; 5(8):e25290. DOI: https://doi.org/10.2196/25290
22. Wu X, Qiu Q, Liu Z, Zhao Y, Zhang B, Zhang Y, Wu X, Ren J. Hyphae detection in fungal keratitis images with adaptive robust binary pattern. IEEE Acc 2018; 6:13449-13460. DOI: https://doi.org/10.1109/access.2018.2808941
23. Shorfuzzaman M, Masud M, Alhumyani H, Anand D, Singh A. Artificial neural network-based deep learning model for COVID-19 patient detection using X-ray chest images. J Healthc Eng 2021; 2021:5513679. DOI: https://doi.org/10.1155/2021/5513679
24. Inamdar MA, Raghavendra U, Gudigar A, Chakole Y, Hegde A, Menon GR, Barua P, Palmer EE, Cheong KH, Chan WY, Ciaccio EJ, Acharya UR. A Review on Computer Aided Diagnosis of Acute Brain Stroke. Sensors (Basel) 2021; 21(24):8507. DOI: https://doi.org/10.3390/s21248507
25. Lin E, Yuh EL. Computational Approaches for Acute Traumatic Brain Injury Image Recognition. Front Neurol 2022; 13:791816. DOI: https://doi.org/10.3389/fneur.2022.791816
26. Zhu H, Jiang L, Zhang H, Luo L, Chen Y, Chen Y. An automatic machine learning approach for ischemic stroke onset time identification based on DWI and FLAIR imaging. Neuroimage Clin 2021; 31:102744. DOI: https://doi.org/10.1016/j.nicl.2021.102744
27. Bibi N, Sikandar M, Ud Din I, Almogren A, Ali S. IoMT-based automated detection and classification of leukemia using deep learning. J Healthc Eng 2020; 2020:6648574. DOI: https://doi.org/10.1155/2020/6648574
28. Yuan Q, Zhang H, Deng T, Tang S, Yuan X, Tang W, Xie Y, Ge H, Wang X, Zhou Q, Xiao X. Role of artificial intelligence in kidney disease. Int J Med Sci 2020; 17(7):970-984. DOI: https://doi.org/10.7150/ijms.42078
29. Bien N, Rajpurkar P, Ball RL, Irvin J, Park A, Jones E, Bereket M, Patel BN, Yeom KW, Shpanskaya K, Halabi S, Zucker E, Fanton G, Amanatullah DF, Beaulieu CF, Riley GM, Stewart RJ, Blankenberg FG, Larson DB, Jones RH, Langlotz CP, Ng AY, Lungren MP. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet. PLoS Med 2018; 15(11):e1002699. DOI: https://doi.org/10.1371/journal.pmed.1002699
30. Krogue JD, Cheng KV, Hwang KM, Toogood P, Meinberg EG, Geiger EJ, Zaid M, McGill KC, Patel R, Sohn JH, Wright A, Darger BF, Padrez KA, Ozhinsky E, Majumdar S, Pedoia V. Automatic hip fracture identification and functional subclassification with deep learning. Radiol Artif Intell 2020; 2(2):e190023. DOI: https://doi.org/10.1148/ryai.2020190023
31. Yang D, Kim J, Yoo J, Cha WC, Paik H. Identifying the risk of sepsis in patients with cancer using digital health care records: Machine learning-based approach. JMIR Med Inform 2022; 10(6):e37689. DOI: https://doi.org/10.2196/37689
32. Giannini HM, Ginestra JC, Chivers C, Draugelis M, Hanish A, Schweickert WD, Fuchs BD, Meadows L, Lynch M, Donnelly PJ, Pavan K, Fishman NO, Hanson CW 3rd, Umscheid CA. A machine learning algorithm to predict severe sepsis and septic shock: Development, implementation, and impact on clinical practice. Crit Care Med 2019; 47(11):1485-1492. DOI: https://doi.org/10.1097/CCM.0000000000003891
33. Ginestra JC, Giannini HM, Schweickert WD, Meadows L, Lynch MJ, Pavan K, Chivers CJ, Draugelis M, Donnelly PJ, Fuchs BD, Umscheid CA. Clinician perception of a machine learning-based early warning system designed to predict severe sepsis and septic shock. Crit Care Med 2019; 47(11):1477-1484. DOI: https://doi.org/10.1097/CCM.0000000000003803
34. Rghioui A, Lloret J, Sendra S, Oumnad A. A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms. Healthcare (Basel) 2020; 8(3):348. DOI: https://doi.org/10.3390/healthcare8030348
35. Polu SK. IoMT based smart health care monitoring system. Int J Innov Res Sci Technol 2019; 5:11:58-64.
36. Romero-Brufau S, Wyatt KD, Boyum P, Mickelson M, Moore M, Cognetta-Rieke C. A lesson in implementation: A pre-post study of providers’ experience with artificial intelligence-based clinical decision support. Int J Med Inform 2020; 137:104072. DOI: https://doi.org/10.1016/j.ijmedinf.2019.104072
37. Boutilier JJ, Chan TCY, Ranjan M, Deo S. Risk stratification for early detection of diabetes and hypertension in resource-limited settings: Machine learning analysis. J Med Internet Res 2021; 23(1):e20123. DOI: https://doi.org/10.2196/20123
38. Wijnberge M, Geerts BF, Hol L, Lemmers N, Mulder MP, Berge P, Schenk J, Terwindt LE, Hollmann MW, Vlaar AP, Veelo DP. 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. JAMA 2020; 323(11):1052-1060. DOI: https://doi.org/10.1001/jama.2020.0592
39. van der Ven WH, Veelo DP, Wijnberge M, van der Ster BJP, Vlaar APJ, Geerts BF. One of the first validations of an artificial intelligence algorithm for clinical use: The impact on intraoperative hypotension prediction and clinical decision-making. Surgery 2021; 169(6):1300-1303. DOI: https://doi.org/10.1016/j.surg.2020.09.041
40. Alballa N, Al-Turaiki I. Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review. Inform Med Unlocked. 2021; 24:100564. DOI: https://doi.org/10.1016/j.imu.2021.100564
41. Yang Z, Olszewski D, He C, Pintea G, Lian J, Chou T, Chen RC, Shtylla B. Machine learning and statistical prediction of patient quality-of-life after prostate radiation therapy. Comput Biol Med 2021; 129:104127. DOI: https://doi.org/10.1016/j.compbiomed.2020.104127
42. Nicolae A, Semple M, Lu L, Smith M, Chung H, Loblaw A, Morton G, Mendez LC, Tseng CL, Davidson M, Ravi A. Conventional vs machine learning-based treatment planning in prostate brachytherapy: Results of a Phase I randomized controlled trial. Brachytherapy 2020; 19(4):470-476. DOI: https://doi.org/10.1016/j.brachy.2020.03.004
43. Awotunde JB, Folorunso S., Ajagbe SA, Garg J, Ajamu GJ. AiIoMT: IoMT-based system-enabled artificial intelligence for enhanced smart healthcare systems. In: Al-Turjman, F., Nayyar, A. (eds) Machine Learning for Critical Internet of Medical Things. Springer, Cham. ISBN 978-3-030-80927-0. 2022. DOI: https://doi.org/10.1007/978-3-030-80928-7_10
44. Rawson TM, Moore LSP, Hernandez B, Charani E, Castro-Sanchez E, Herrero P, Hayhoe B, Hope W, Georgiou P, Holmes AH. A systematic review of clinical decision support systems for antimicrobial management: Are we failing to investigate these interventions appropriately? Clin Microbiol Infect 2017; 23(8):524-532. DOI: https://doi.org/10.1016/j.cmi.2017.02.028
45. Segal G, Segev A, Brom A, Lifshitz Y, Wasserstrum Y, Zimlichman E. 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 Am Med Inform Assoc 2019; 26(12):1560-1565. DOI: https://doi.org/10.1093/jamia/ocz135
46. Xie X, Wu Y, Li K, Ai C, Wang Q, Wang C, Chen J, Xiang B. Preliminary experiences with robot-assisted choledochal cyst excision using the da vinci surgical system in children below the age of one. Front Pediatr 2021; 9:741098. DOI: https://doi.org/10.3389/fped.2021.741098
47. Garas G, Tolley N. Robotics in otorhinolaryngology - head and neck surgery. Ann R Coll Surg Engl 2018; 100(Suppl 7):34-41. DOI: https://doi.org/10.1308/rcsann.supp2.34
48. Yu S, Bo T, Hou B, Li J, Zhou X. Surgery strategy of 13 cases to control bleeding from the liver on laparoscopic repeat liver resection for recurrent hepatocellular carcinoma. J Minim Access Surg 2019; 15(3):214-218. DOI: https://doi.org/10.4103/jmas.JMAS_214_17
49. Vanschoren J. Meta-Learning. In: Hutter, F., Kotthoff, L., Vanschoren, J. (eds) Automated Machine Learning. The Springer Series on Challenges in Machine Learning. Springer, Cham. ISBN 978-3-030-05317-8. 2019. DOI: https://doi.org/10.1007/978-3-030-05318-5_2
50. Subramanian I, Verma S, Kumar S, Jere A, Anamika K. Multi-omics data integration, interpretation, and its application. Bioinform Biol Insights 2020; 14:1177932219899051. DOI: https://doi.org/10.1177/1177932219899051
51. Ye M, Lin Y, Pan S, Wang ZW, Zhu X. Applications of multi-omics approaches for exploring the molecular mechanism of ovarian carcinogenesis. Front Oncol 2021; 11:745808. DOI: https://doi.org/10.3389/fonc.2021.745808
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.