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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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
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Central: PMC6241736].
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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. Greene A, Greene CC, Greene C. Artificial intelligence, 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. Sato F, Shimada Y, Selaru FM, Shibata D, Maeda M, Watanabe G, 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. Yang HX, Feng W, Wei JC, Zeng TS, Li ZD, Zhang LJ, et al. 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 using
Francisco, CA: Morgan Kaufmann Publishers; 1998. p. 513.
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. Greene A, Greene CC, Greene C. Artificial intelligence, 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. Sato F, Shimada Y, Selaru FM, Shibata D, Maeda M, Watanabe G, 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. Yang HX, Feng W, Wei JC, Zeng TS, Li ZD, Zhang LJ, et al. 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 using
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| 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 | ||
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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.
