From Algorithms to Outcomes: Transforming Modern Healthcare through Artificial Intelligence
Keywords:
gastrostomy, Complications, Percutaneous, endoscopic, keratoconus, omega-3, cornea, pentacam, astigmatism. wartime., Hearing loss., Chronic rhinosinusitis, leiomyoma, Immunohistochemistry, Urology, Renal Cyst, Radiology, Kidney Neoplasm, medicine, future, healthcareAbstract
Artificial Intelligence (AI) refers to the utilisation of computers and advanced technologies to simulate intelligent behaviour and critical thinking comparable to that of humans. The term was first described by John McCarthy in 1956 as the science and engineering of creating intelligent machines. [1,2]. Previously considered a concept of science fiction, AI is now a tangible reality and is widely represented within academic discussion and mainstream applications. Machine Learning (ML), which is a subset of AI, enables machines to learn from patient data and generate predictions by pattern recognition, thereby empowering healthcare providers in delivering better care through accurate diagnosis and treatments. Although current technologies and AI models have not yet advanced to a stage where they may replace a doctor, they hold considerable promise as valuable diagnostic tools in healthcare. [1,3] While the likelihood of AI assuming a significant role in healthcare seems imminent, its evolution is currently tempered by concerns regarding ethical challenges and patient safety. This literature review aims to examine the contemporary applications of AI in healthcare, its potential advantages for both patients and healthcare professionals, and the existing challenges and limitations that may hinder its continued progression. [1,2]
References
Amisha, Malik P, Pathania M, Rathaur VK (2019) Overview of artificial intelligence in medicine. 8(7), 2328-2331. https://doi.org/10.4103/jfmpc.jfmpc_440_19
S Alowais, S Alghamdi, N Alsuhebany (2023) Revolutionizing healthcare: the role of artificial intelligence in clinical practice. 23, 689. https://doi.org/10.1186/s12909-023-04698-z
Amin A, Cardoso S Future of Artificial Intelligence in Surgery: A Narrative Review. https://www.cureus.com/articles/204683-future-of-artificial-intelligence-in-surgery-a-narrative-review#!/
Matheny ME, Whicher D, Thadaney Israni S (2020) Artificial Intelligence in Health Care: a Report from the National Academy of Medicine. (6), 509-10. https://doi.org/10.1001/jama.2019.21579
T Davenport, Kalakota R (2019) The potential for artificial intelligence in Healthcare. (2), 94-8. https://doi.org/10.7861/futurehosp.6-2-94
Vanlehn K (2011) The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. 46(4), 197-221. https://doi.org/10.1080/00461520.2011.611369
Jordan MI, Mitchell TM (2015) Machine learning: Trends, perspectives, and prospects. 349(6245), 255-60. https://doi.org/10.1126/science.aaa8415
Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. 25(1), 44-56.
J Cruz, D Wishart (2006) Applications of machine learning in cancer prediction and prognosis cancer informatics. 2(0).
Mintz Y, Brodie R (2019) Introduction to artificial intelligence in medicine. 28, 73-81. https://doi.org/10.1080/13645706.2019.1575882
Berlyand Y, Raja AS, Dorner SC, Prabhakar AM, Sonis JD, Gottumukkala RV (2018) How artificial intelligence could transform emergency department operations. 36(8), 1515-1517. https://doi.org/10.1016/j.ajem.2018.01.017
P Rajpurkar, et al (2017) CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv 2017
Esteva A, et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. 542(7639), 115-118.
Haug CJ, Drazen JM (2023) Artificial Intelligence and Machine Learning in Clinical Medicine. 388(13), 1201-1208. https://doi.org/10.1056/NEJMra2302038
Hamlet P, J Tremblay (2017) Artificial intelligence in medicine. 69S, S36–40. https://doi.org/10.1016/j.metabol.2017.01.011
L Clark (2012) Google's Artificial Brain Learns to Find Cat Videos. http://www.wired.com/2012/06/google-xneural-network
(2012) How Many Computers to Identify Cat? 16,000. http://www.nytimes.com/2012/06/26/technology/in-a-big-network-of-computers-evidence-of-machine-learning.html
Mayo RC, J Leung (2018) Artificial intelligence and deep learning-Radology's next frontier?. 49, 87–8. https://doi.org/10.1016/j.clinimag.2017.11.007
Fenton JJ, N Taplin (2007) Influence of computer-aided detection on performance of screening mammography. 356, 1399–409. https://doi.org/10.1056/NEJMoa066099
M Alcusky, L Philpotts, M Bonafede, J Clarke, A Skoufalos (2014) The patient burden of screening mammography recall. 23((Suppl 1)), S11–9. https://doi.org/10.1089/jwh.2014.1511
S London (1998) DXplain: A web-based diagnostic decision support system for medical students. 17, 17–28. https://doi.org/10.1300/J115v17n02_02
M G Kahn, S A Steib, V J Fraser, W C Dunagan (1993) An expert system for culture-based infection control surveillance. 171–5.
H C McCall, C G Richardson, F D Helgadottir, F S Chen (2018) Evaluating a web-based social anxiety intervention: A randomized controlled trial among university students?. 20, e91. https://doi.org/10.2196/jmir.8630
J Barlett (2018) Buoy health has announced that it will broaden its self-diagnostic tool into pediatric illnesses through a partnership with Boston Children's Hospital. https://www.bizjournals.com/boston/news/2018/08/22/boston-childrens-website-to-feature-self.html
D L Labovitz, L Shafner, M Reyes Gil, D Virmani, A Hanina (2017) Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. 48, 1416–9. https://doi.org/10.1161/strokeaha.116.016281
G Pusiol, A Esteva, S S Hall, M Frank, A Milstein, L Fei-Fei (2016) Classification of developmental disorders using eye-movements. https://med.stanford.edu/cerc/research/new-pac.html
G M Bianconi, R Mehra, S Yeung, F Salipur, J Jopling, L Downing (2017) Vision-based prediction of ICU mobility care activities using recurrent neural networks. https://med.stanford.edu/cerc/research/new-pac.html
A Haque, M Guo, A Alahi, S Yeung, Z Luo, A Rege (2017) Towards vision-based smart hospitals: A system for tracking and monitoring hand hygiene compliance. https://med.stanford.edu/cerc/research/new-pac.html
C Sinsky, L Colligan, L Li, M Prgomet, S Reynolds, L Goeders (2016) Allocation of physician time in ambulatory practice: A time and motion study in 4 specialities. 165, 753–60. https://doi.org/10.7326/M16-0961
T Sorlie, C M Perou, R Tibshirani, T Aas, S Geisler, H Johnsen (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. 98, 10869–74. https://doi.org/10.1073/pnas.191367098
E Widen, T G Raben, L Lello, S D H Hsu (2021) Machine learning prediction of biomarkers from SNPs and of Disease risk from biomarkers in the UK Biobank. 12(7), 991. https://doi.org/10.3390/genes12070991
J T Leek, R B Scharpf, H C Bravo, D Simcha, B Langmead, W E Johnson (2010) Tackling the widespread and critical impact of batch effects in high-throughput data. 11, 733–9. https://doi.org/10.1038/nrg2825
O Yersal (2014) Biological subtypes of breast cancer: prognostic and therapeutic implications. 5(3), 412–24.
T T V Tran, A Surya Wibowo, H Tayara, K T Chong (2023) Artificial Intelligence in Drug Toxicity Prediction: recent advances, Challenges, and future perspectives. 63(9), 2628–43. https://doi.org/10.1021/acs.jcim.3c00200
A Blanco-González, A Cabezón, A Seco-González, D Conde-Torres, P Antelo-Riveiro, Á Piñeiro, et al. (2023) The role of AI in drug discovery: Challenges, opportunities, and strategies. 16(6), 891. https://doi.org/10.3390/ph16060891
K Han, P Cao, Y Wang, F Xie, J Ma, M Yu, J Wang, Y Xu, Y Zhang, J Wan (2022) A review of approaches for Predicting Drug-Drug interactions based on machine learning. 12, 814858. https://doi.org/10.3389/fphar.2021.814858
W E Hautz, J E Kämmer, S C Hautz (2022) d Disease Risk Prediction. 2, 927312. https://doi.org/10.3389/fbinf.2022.927312
S Abubaker Bagabir, N K Ibrahim, H Abubaker Bagabir, R Hashem Ateeq (2022) Covid-19 and Artificial Intelligence: genome sequencing, drug development and vaccine discovery. 15(2), 289–96. https://doi.org/10.1016/j.jiph.2022.01.011
A Esteva, B Kuprel, R A Novoa, J Ko, S M Swetter, H M Blau, et al. (2017) Dermatologist-level classification of skin cancer with deep neural networks. 542, 115–8. https://doi.org/10.1038/nature21056
P Lakhani, B Sundaram (2017) Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. 284, 574–82. https://doi.org/10.1148/radiol.2017162326
The digital mammography DREAM challenge. https://www.synapse.org/#!Synapse:syn4224222/wiki/401744
I E Suleimenov, Y S Vitulyova, A S Bakirov, O A Gabrielyan (2020) Artificial Intelligence: what is it?. 22–5. https://doi.org/10.1145/3397125.3397141
O Vandenberg, G Durand, M Hallin, A Diefenbach, V Gant, P Murray, et al. (2020) Consolidation of clinical Microbiology Laboratories and introduction of Transformative Technologies. 33(2). https://doi.org/10.1128/cmr.00057-19
T Go, J H Kim, H Byeon, S J Lee (2018) Machine learning-based in-line holographic sensing of unstained malaria-infected red blood cells. 11(9), e201800101. https://doi.org/10.1002/jbio.201800101
KP Smith, AD Kang, JE Kirby (2018) Automated interpretation of Blood Culture Gram Stains by Use of a deep convolutional neural network. 56(3), e01521–17. https://doi.org/10.1128/JCM.01521-17
N Peiffer-Smadja, S Dellière, C Rodriguez, G Birgand, FX Lescure, S Fourati (2020) Machine learning in the clinical microbiology laboratory: has the time come for routine practice?. 26(10), 1300–9. https://doi.org/10.1016/j.cmi.2020.02.006
TR Undru, Uday Uday, JT Lakshmi (2022) Integrating Artificial Intelligence for Clinical and Laboratory diagnosis - a review. 17(2), 420–6. https://doi.org/10.26574/maedica.2022.17.2.420
MM Mijwil, K Aggarwal (2022) A diagnostic testing for people with appendicitis using machine learning techniques. 81(5), 7011–23. https://doi.org/10.1007/s11042-022-11939-8
J Becker, JA Decker, C Römmele, M Kahn, H Messmann, M Wehler (2022) Artificial intelligence-based detection of pneumonia in chest radiographs. 12(6), 1465. https://doi.org/10.3390/diagnostics12061465
A Blasiak, A Truong, W Jeit, L Tan, KS Kumar, SB Tan (2022) Precise Curate. Ai: a prospective feasibility trial to dynamically modulate personalized chemotherapy dose with artificial intelligence. 40(16suppl), 1574–4. https://doi.org/10.1200/JCO.2022.40.16_suppl.1574
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