The Scope of Artificial Intelligence Applications in Medicine: A Review Article
Abstract
Artificial intelligence (AI) is the high-tech discipline of employing computers to perform or potentially outperform human intelligence. With the deployment of AI systems, the traditional medical environment has already changed. For recent AI developments that have not yet been applied to medicine, as well as potential future developments, to be implementable in medicine, numerous considerations must be taken into account. In this article, we introduce fundamental AI-related concepts for researchers and administrators of healthcare systems. This article also discusses challenges with applications of AI in medicine, potential futures, and preparation strategies for the future of AI-enhanced medicine. In addition, a list of applications of AI in medicine is provided with a categorization that could help medical professionals to understand potential applications of AI systems in their fields of work.
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2. Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc. 2020;92(4):807-12. doi: 10.1016/j.gie.2020.06.040. [PubMed: 32565184].
3. Shalev-Shwartz S, Ben-David S. Understanding machine learning: From theory to algorithms. Cambridge, UK: Cambridge University Press; 2014. p. 415.
4. Ramesh AN, Kambhampati C, Monson JR, Drew PJ. Artificial intelligence in medicine. Ann R Coll Surg Engl. 2004;86(5): 334-8. doi: 10.1308/147870804290. [PubMed: 15333167]. [PubMed Central: PMC1964229].
5. Bostrom N. Superintelligence. Paris, France: Dunod; 2017. p. 464.
6. Holzinger A, Langs G, Denk H, Zatloukal K, Muller H. Causability and explainability of artificial intelligence in medicine. Wiley Interdiscip Rev Data Min Knowl Discov. 2019;9(4):e1312. doi: 10.1002/widm.1312. [PubMed: 32089788]. [PubMed Central: PMC7017860].
7. Gómez-González E. Artificial intelligence in medicine and healthcare: Applications, availability and societal impact. In: Gutiérrez EG, editor. EUR 30197 EN. Luxembourg: Publications Office of the European Union; 2020.
8. PARO Therapeutic Robot [Online]. [cited 2022 Aug 18]. Available from: URL: http://www.parorobots.com/
9. Mueller B, Kinoshita T, Peebles A, Graber MA, Lee S. Artificial intelligence and machine learning in emergency medicine: A narrative review. Acute Med Surg. 2022;9(1):e740. 10.1002/ams2.740. [PubMed: 35251669]. [PubMed Central: PMC8887797].
10. Shafaf N, Malek H. Applications of machine learning approaches in emergency medicine; a review article. Arch Acad Emerg Med. 2019;7(1):34. [PubMed: 31555764]. [PubMed Central: PMC6732202].
11. Arya R, Wei G, McCoy JV, Crane J, Ohman-Strickland P, Eisenstein RM. Decreasing length of stay in the emergency department with a split emergency severity index 3 patient flow model. Acad Emerg Med. 2013;20(11):1171-9. doi: 10.1111/acem.12249. [PubMed: 24238321].
12. Asheim A, Bache-Wiig Bjornsen LP, Naess-Pleym LE, Uleberg O, Dale J, Nilsen SM. Real-time forecasting of emergency department arrivals using prehospital data. BMC Emerg Med. 2019;19(1):42. doi: 10.1186/s12873-019-0256-z. [PubMed: 31382882]. [PubMed Central: PMC6683581].
13. Zhang Y, Zhang J, Tao M, Shu J, Zhu D. Forecasting patient arrivals at emergency department using calendar and meteorological information. Appl Intell (Dordr). 2022;52(10):11232-43. doi: 10.1007/s10489-021-03085-9. [PubMed: 35079202]. [PubMed Central: PMC8776398].
14. Lasalvia L, Merges R. Expanding Precision Medicine: How healthcare is deploying precision diagnosis and individualized treatment at scale. The Journal of Precision Medicine [Online]. [cited 2019 July]; Available from: URL: https://cdn0.scrvt.com/39b415fb07de4d9656c7b516d8e2d907/1800 000006640948/f86f5c003f29/Siemens-Healthineers-Article-for- Journal-of-Precsion-Medicine-July-2019_1800000006640948.pdf
15. Fogel AL, Kvedar JC. Artificial intelligence powers digital medicine. NPJ digital med. 2018;1:5. doi: 10.1038/s41746-017-0012-2. [PubMed: 31304291]. [PubMed Central: PMC6548340].
16. Huang Z, Chan TM, Dong W. MACE prediction of acute coronary syndrome via boosted resampling classification using electronic medical records. J Biomed Inform. 2017;66: 161-70. doi: 10.1016/j.jbi.2017.01.001. [PubMed: 28065840].
17. Mortazavi BJ, Downing NS, Bucholz EM, Dharmarajan K, Manhapra A, Li SX, et al. Analysis of machine learning techniques for heart failure readmissions. Circ Cardiovasc Qual Outcomes. 2016;9(6):629-40. doi: 10.1161/CIRCOUTCOMES.116.003039. [PubMed: 28263938]. [PubMed Central: PMC5459389].
18. Ferraro JP, Ye Y, Gesteland PH, Haug PJ, Tsui FR, Cooper GF, et al. The effects of natural language processing on cross- institutional portability of influenza case detection for disease surveillance. Appl Clin Inform. 2017;8(2):560-80. doi: 10.4338/ACI-2016-12-RA-0211. [PubMed: 28561130]. [PubMed Central: PMC6241736].
19. Arterys. Medical imaging cloud AI for Radiology | Arterys [Online]. [cited 2022 Aug 18]. Available from: URL: https://arterys.com/
20. Topalovic M, Das N, Burgel PR, Daenen M, Derom E, Haenebalcke C, et al. Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. Eur Respir J. 2019;53(4). doi: 10.1183/13993003.01660-2018. [PubMed: 30765505].
21. Xu Y, Hosny A, Zeleznik R, Parmar C, Coroller T, Franco I, et al. Deep learning predicts lung cancer treatment response from serial medical imaging. Clin Cancer Res. 2019;25(11):3266-75. doi: 10.1158/1078-0432.CCR-18-2495. [PubMed: 31010833]. [PubMed Central: PMC6548658].
22. GreeneA,GreeneCC,GreeneC.Artificialintelligence,chatbots, and the future of medicine. Lancet Oncol . 2019;20(4):481-2. doi: 10.1016/S1470-2045(19)30142-1. [PubMed: 30942174].
23. Niel O, Boussard C, Bastard P. Artificial intelligence can predict GFR decline during the course of ADPKD. Am J Kidney Dis. 2018;71(6):911-2. doi: 10.1053/j.ajkd.2018.01.051. [PubMed: 29609979].
24. Geddes CC, Fox JG, Allison ME, Boulton-Jones JM, Simpson K. An artificial neural network can select patients at high risk of developing progressive IgA nephropathy more accurately than experienced nephrologists. Nephrol Dial Transplant. 1998;13(1):67-71. doi: 10.1093/ndt/13.1.67. [PubMed: 9481717].
25. Pace F, Buscema M, Dominici P, Intraligi M, Baldi F, Cestari R, et al. Artificial neural networks are able to recognize gastro- oesophageal reflux disease patients solely on the basis of clinical data. Eur J Gastroenterol Hepatol. 2005;17(6):605-10. doi: 10.1097/00042737-200506000-00003. [PubMed: 15879721].
26. Lahner E, Grossi E, Intraligi M, Buscema M, Corleto VD, Delle FG, et al. Possible contribution of artificial neural networks and linear discriminant analysis in recognition of patients with suspected atrophic body gastritis. World J Gastroenterol. 2005;11(37):5867-73. doi: 10.3748/wjg.v11.i37.5867. [PubMed: 16270400]. [PubMed Central: PMC4479691].
27. Das A, Ben-Menachem T, Cooper GS, Chak A, Sivak MV, Gonet JA, et al. Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: Internal and external validation of a predictive model. Lancet. 2003;362(9392):1261-6. doi: 10.1016/S0140-6736(03)14568-0. [PubMed: 14575969].
28. SatoF,ShimadaY,SelaruFM,ShibataD,MaedaM,WatanabeG, et al. Prediction of survival in patients with esophageal carcinoma using artificial neural networks. Cancer. 2005;103(8):1596-605. doi: 10.1002/cncr.20938. [PubMed: 15751017].
29. Ichimasa K, Kudo SE, Mori Y, Misawa M, Matsudaira S, Kouyama Y, et al. Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer. Endoscopy. 2018;50(3):230-40. doi: 10.1055/s-0043-122385. [PubMed: 29272905].
30. YangHX,FengW,WeiJC,ZengTS,LiZD,ZhangLJ,etal.Support vector machine-based nomogram predicts postoperative distant metastasis for patients with oesophageal squamous cell carcinoma. Br J Cancer. 2013;109(5):1109-16. doi: 10.1038/bjc.2013.379. [PubMed: 23942069]. [PubMed Central: PMC3778272].
31. Fernandez-Esparrach G, Bernal J, Lopez-Ceron M, Cordova H, Sanchez-Montes C, Rodriguez de MC, et al. Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps. Endoscopy. 2016;48(9):837-42. doi: 10.1055/s-0042-108434. [PubMed: 27285900].
32. Regalia G, Onorati F, Lai M, Caborni C, Picard RW. Multimodal wrist-worn devices for seizure detection and advancing research: Focus on the Empatica wristbands. Epilepsy Res. 2019;153:79-82. doi: 10.1016/j.eplepsyres.2019.02.007. [PubMed: 30846346].
33. Bruno E, Simblett S, Lang A, Biondi A, Odoi C, Schulze-Bonhage A, et al. Wearable technology in epilepsy: The views of patients, caregivers, and healthcare professionals. Epilepsy Behav. 2018;85:141-9. doi: 10.1016/j.yebeh.2018.05.044. [PubMed: 29940377].
34. Lakhani P, Sundaram B. Deep Learning at Chest Radiography: Automated classification of pulmonary tuberculosis by usingconvolutional neural networks. Radiology. 2017;284(2):574-82.
doi: 10.1148/radiol.2017162326. [PubMed: 28436741].
35. Halabi SS, Prevedello LM, Kalpathy-Cramer J, Mamonov AB, Bilbily A, Cicero M, et al. The RSNA pediatric bone age machine learning challenge. Radiology. 2018;290(2):498-503. doi:
10.1148/radiol.2018180736.
36. Thian YL, Li Y, Jagmohan P, Sia D, Chan VEY, Tan RT. Convolutional neural networks for automated fracture detection and localization on wrist radiographs. Radiol Artif Intell. 2019;1(1):e180001. doi: 10.1148/ryai.2019180001. [PubMed: 33937780]. [PubMed Central: PMC8017412].
37. Gong E, Pauly JM, Wintermark M, Zaharchuk G. Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI. J Magn Reson Imaging. 2018;48(2):330-40. doi: 10.1002/jmri.25970. [PubMed: 29437269].
38. Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019;25(6):954-61. doi: 10.1038/s41591-019-0447-x. [PubMed: 31110349].
39. Hyun CM, Kim HP, Lee SM, Lee S, Seo JK. Deep learning for undersampled MRI reconstruction. Phys Med Biol. 2018;63(13):135007. doi: 10.1088/1361-6560/aac71a. [PubMed: 29787383].
40. Abramoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1:39. doi: 10.1038/s41746-018-0040-6. [PubMed: 31304320]. [PubMed Central: PMC6550188].
41. 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(9): 1342-50. doi: 10.1038/s41591-018-0107-6. [PubMed: 30104768].
42. 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(7639):115-8. doi: 10.1038/nature21056. [PubMed: 28117445]. [PubMed Central: PMC8382232].
43. Han SS, Kim MS, Lim W, Park GH, Park I, Chang SE. Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. J Invest Dermatol. 2018;138(7):1529-38. doi: 10.1016/j.jid.2018.01.028. [PubMed: 29428356].
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| Files | ||
| Issue | Vol 9, No 2 (2023) | |
| Section | Review Article | |
| DOI | https://doi.org/10.18502/jost.v9i2.12621 | |
| Keywords | ||
| Artificial Intelligence Machine Learning Deep Learning | ||
| Rights and permissions | |
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Foroughmand-Araabi M-H. The Scope of Artificial Intelligence Applications in Medicine: A Review Article. J Orthop Spine Trauma. 2023;9(2):53-8.
