The Role of Artificial Intelligence in Medicine Applications
DOI:
https://doi.org/10.29304/jqcm.2023.15.1.1163Keywords:
Artificial intelligence, Medicine, Education, Machine learningAbstract
The goal of research on artificial intelligence aims to make machines and software similar to human performance, so measuring the degree to which an artificial intelligence system can resemble human capabilities is used to determine the types of artificial intelligence. Thus, by comparing the machine with humans in terms of versatility and performance, it becomes possible to categorize artificial intelligence, with multiple types of artificial intelligence, where artificial intelligence that can perform human-like functions with equal levels of efficiency will be considered as a sophisticated type of artificial intelligence, while Artificial intelligence with limited functionality and performance is considered a simpler and less sophisticated type Based on this scale, and in general, artificial intelligence can be classified. Depending on the species classification of AI-enabled devices based on their similarity to the human brain, and their ability to "think" and possibly "feel" like humans. According to this classification system, there are four types of artificial intelligence systems: “interactive machines, limited memory machines, theory of mind, and self-aware artificial intelligence
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References
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[2] Singh H. The frequency of diagnostic errors in outpatient care. 2014;23(9):727-31.
[3] Balogh EP. Improving diagnosis in health care. Washington (DC):; 2015.
[4] Choy G,Current applications and future impact of machine learning. 2018;288(2):318-28.
[5] Yala A. A deep learning mammography-based. Radiology. 2019;292(1):60-6.
[6] Bera K. Madabhushi A. Artificial intelligence in digital pathology. Nat Rev Clin Oncol. 2019;16(11):703-15.
[7] Gadepalli K,. An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis. 2019;25(9):1453-7.
[8] Wang Z, et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy a multicentre, case-control, diagnostic study. Lancet Oncol. 2019.
[9] Hotta K, et al. Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy: a prospective study. Ann Intern Med 2018
[10] Smail-Tabbone M, et al. Application of artificial intelligence to gastroenterology and hepatology. Gastroenterology. 2020;158(1):76-94.
[11] Petretta M. Current applications of big data. J Geriatr Cardiol. 2019
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[13] Fu DJ, Bruynseels A, et al. A comparison of deep learning performance against health-care professionals. Lancet Digit Health. 2019;1(6):e271-97.
[14] Ardila D, 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.
[15] Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94.
[16] Bui P, et al. A deep learning system for differential diagnosis of skin diseases. Nat Med. 2020 May 18
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[18] Kang JH, et al. Deep learning for chest radiograph diagnosis in the emergency department. Radiology. 2019;293(3):573-80.
[19] Hsieh TC, et al. PEDIA: prioritization of exome data by image analysis. Genet Med 2019.
[20] Chakravarty MM. Alzheimer’s Disease Neuroimaging Initiative. Modeling and prediction of clinical symptom trajectories. PLoS Comput Biol. 2018;14(9):e1006376.
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[22] Corrado GS., et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018;2(3):158-64.
[23] Beaulieu S., et al. Vascular retinal biomarkers improves the detection of the likely cerebral amyloid status Alzheimers Dement (N Y). 2019;5:610-7.
[24] Peng L., et al. Detection of anaemia from retinal fundus images via deep learning. Nat Biomed Eng. 2020;4(1):18-27.
[25] Fenyo D., et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med 2018.
[26] Loosen SH., et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med. 2019;25(7):1054-6.
[27] Kumar A., et al. Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study. PLoS Med. 2018;15(11):e1002711.
[28] Curry E., et al. A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecularphenotypes of epithelial ovarian cancer. Nat Commun. 2019;10(1):764.
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[32] Liu G, et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med. 2019;25(3):433-8.
[33] Tomasev S S., et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature. 2019;572(7767):116-9.
[34] Zanger J, et al. Development and validation of a machine learning model to aid discharge processes for inpatient surgical care. JAMA Netw Open. 2019;2(12):e1917221.
[35] De Turck F., et al. Accurate prediction of blood culture outcome in the intensive care unit. Artif Intell Med. 2019;97:38-43.
[36] Alaa AM. Cardiovascular disease risk prediction using automated machine learning: a prospective study of 423,604 UK Biobank participants. PLoS One. 2019;14(5):e0213653.
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[38] Brealey DA., et al. Optimal intensive care outcome prediction over time using machine learning. PLoS One. 2018;13(11):e0206862.
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[41] Kent J. Amazon introduces machine learning medical transcription service; 2019
[42] Maier-Hein L., et al. Surgical data science for next-generation interventions. Nat Biomed Eng. 2017;1(9):691-6.
[43] Zhu H. Big data and artificial intelligence modeling for drug discovery. Annu Rev Pharmacol Toxicol. 2020;60:573-89.
[44] Basile AO., et al Artificial intelligence for drug toxicity and safety. Trends Pharmacol Sci. 2019;40(9):624-35.
[45] Harrer S, et al., Artificial intelligence for clinical trial design. Trends Pharmacol Sci. 2019;40(8):577-91.
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[47] Stokes JM., et al. A deep learning approach to antibiotic discovery. Cell2020
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[50] Korot E., et al. Automated deep learning design for medical image classification by health-care. 2019;1(5):e232-42.
[51] Matheny M., et al. Artificial intelligence in health care: the hope, the hype, the promise, the peril National Academy of Medicine; 2019
[52] Wartman SA, Combs CD. Medical education must move from the information age to the age of artificial intelligence. Acad Med. 2018
[53] Murphy B. AMA: take extra care when applying AI in medical education Chicago (IL): American Medical Association; 2019.
[54] NHS England. The Topol Review Leeds: NHS England.
[55] American Medical Association. Innovations & outcomes of the consortium Chicago (IL): American Medical A
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Published
2023-04-03
How to Cite
Betti, A. A. (2023). The Role of Artificial Intelligence in Medicine Applications. Journal of Al-Qadisiyah for Computer Science and Mathematics, 15(1), Comp Page 158–168. https://doi.org/10.29304/jqcm.2023.15.1.1163
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Computer Articles
