Impact of Artificial Intelligence in Disease prediction and Biomedical Research Rising opportunities in Health care Industry
Main Article Content
Abstract
Artificial intelligence has proved to play an important role in health care industry due to its primary capability behind development of precision medicine widely agreed to be direly needed advancements in care. Even though early efforts of providing diagnosis, management and treatment recommendations have proven challenging, the opportunities are high as AI shall ultimately master that domain as well. AI has proven to be very beneficial for radiological analysis of brain, speech and hearing, biomedical information processing, biomedical research, natural language processing, diagnosis and treatment of blood borne bacterial infections, bladder volume prediction, epileptic seizures, and management of dementia. It is interesting to understand that AI will not replace human
clinicians completely, but may augment their efforts to take proper care of patients. In future, health care providers may move towards tasks and human skills that are required like empathy, persuasion and big picture integration, else they may lose their jobs over time, if do not work alongside artificial intelligence
References
Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H.
eDoctor: machine learning and the future of medicine. J Intern Med. 2018;284(6):603–619.
Minsky M.
Steps toward artificial intelligence. Proc IRE. 1961;49(1):8–30.
Weng J, McClelland J, Pentland A, Sporns O, Stockman I, Sur M, et al.
Autonomous mental development by robots and animals. Science. 2001;291(5504):599–600.
Wooldridge M, Jennings NR.
Intelligent agents: theory and practice. Knowl Eng Rev. 1995;10(2):115–152.
Huang G, Huang GB, Song S, You K.
Trends in extreme learning machines: a review. Neural Netw. 2015;61:32–48.
Hopfield JJ.
Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA. 1982;79(8):2554–2558.
Watts DJ, Strogatz SH.
Collective dynamics of “small-world” networks. Nature. 1998;393(6684):440–442.
Zucker RS, Regehr WG.
Short-term synaptic plasticity. Annu Rev Physiol. 2002;64:355–405.
Dahmani K, Tahiri A, Habert O, Elmeftouhi Y.
An intelligent model of home support for people with loss of autonomy: a novel approach. In: Proc. International Conference on Control, Decision and Information Technologies; 2016. p. 182–185.
Rabhi Y, Mrabet M, Fnaiech F.
A facial expression controlled wheelchair for people with disabilities. Comput Methods Programs Biomed. 2018;165:89–105.
Hudec M, Smutny Z.
RUDO: a home ambient intelligence system for blind people. Sensors. 2017;17(8):1926.
Tumpa SN, Islam AB, Ankon MTM.
Smart care: an intelligent assistant for pregnant mothers. In: Proc. International Conference on Advances in Electrical Engineering; 2017. p. 754–759.
Wu Q, Zhang YD, Tao W, Amin MG.
Radar-based fall detection using Doppler time–frequency signatures. IET Radar Sonar Navig. 2015;9(2):164–172.
Lloret J, Canovas A, Sendra S, Parra L.
A smart communication architecture for ambient assisted living. IEEE Commun Mag. 2015;53(1):26–33.
Ben Abacha A, Zweigenbaum P.
MEANS: a medical question-answering system combining NLP and semantic web technologies. Inf Process Manage. 2015;51(5):570–594.
Sarrouti M, Ouatik El Alaoui S.
Machine learning-based classification in biomedical QA. Methods Inf Med. 2017;56(3):209–216.
Sarrouti M, El Alaoui SO.
Document retrieval framework using UMLS similarity. In: KES Intelligent Decision Technologies Conference; 2016. p. 207–216.
Sarrouti M, El Alaoui SO.
Yes/no answer generator for biomedical QA. Int J Healthc Inf Syst Inform. 2017;12(3):62–74.
Almeida H, Meurs MJ, Kosseim L, Tsang A.
Data sampling for HIV literature screening. IEEE Trans Nanobioscience. 2016;15(4):354–361.
Névéol A, Shooshan SE, Humphrey SM, Mork JG, Aronson AR.
Automatic indexing of biomedical literature. J Biomed Inform. 2009;42(5):814–823.
Choi BK, Dayaram T, Parikh N, et al.
Discovery of tumor suppressor mechanisms. Proc Natl Acad Sci USA. 2018;115(42):10666–10671.
Yang Z, Tang N, Zhang X, et al.
Protein–protein interaction extraction using machine learning. Artif Intell Med. 2011;51(3):163–173.
Yu W, Clyne M, Dolan SM, et al.
GAPscreener for genetic association literature. BMC Bioinformatics. 2008;9(1):205.
Plaza L, Díaz A, Gervás P.
Semantic graph-based biomedical summarization. Artif Intell Med. 2011;53(1):1–14.
Liu F, Yu H.
Learning to rank figures in biomedical articles. PLoS One. 2014;9(3):e61567.
Sajda P.
Machine learning for disease detection. Annu Rev Biomed Eng. 2006;8:537–565.
Bou Assi E, Nguyen DK, Rihana S, Sawan M.
Prediction of epileptic seizures: review. Biomed Signal Process Control. 2017;34:144–157.
Tran BX, Vu GT, Ha GH, et al.
Global research trends in AI in healthcare. J Clin Med. 2019;8(3):360.
Tantin A, Bou Assi E, van Asselt E, et al.
Predicting urinary bladder voiding. Biomed Signal Process Control. 2020;55:101667.
Bou Assi E, Gagliano L, Rihana S, et al.
Seizure prediction using neural networks. Sci Rep. 2018;8(1):15491.
Sakai K, Yamada K.
Machine learning in brain diseases. Jpn J Radiol. 2019;37(1):34–72.
Martens FMJ, van Kuppevelt HJM, et al.
Bladder neurostimulation research. Neurourol Urodyn. 2010;29(3):395–400.
Mendez A, Sawan M, Minagawa T, Wyndaele JJ.
Estimation of bladder volume using neural activity. IEEE Trans Neural Syst Rehabil Eng. 2013;21(5):704–715.
Mendez A, Belghith A, Sawan M.
DSP for bladder sensing. IEEE Trans Biomed Circuits Syst. 2014;8(4):552–564.
Wiebe S, Eliasziw M, Bellhouse DR, Fallahay C.
Burden of epilepsy. Can J Neurol Sci. 1999;26(4):263–270.
Fisher RS, van Emde Boas W, Blume W, et al.
Definition of epileptic seizures. Epilepsia. 2005;46(4):470–472.
Mormann F, Andrzejak RG, Elger CE, Lehnertz K.
Seizure prediction overview. Brain. 2007;130(2):314–333.
Richardson A, Robbins CB, Wisely CE, et al.
AI in dementia diagnosis. Curr Opin Ophthalmol. 2022;33(5):425–431.
Yilmaz A, Ustun I, Ugur Z, et al.
AI biomarkers for Alzheimer’s disease. J Alzheimers Dis. 2020;78(4):1381–1392.
Tagliaferri SD, Angelova M, Zhao X, et al.
AI for back pain outcomes. NPJ Digit Med. 2020;3(1).
Tran BX, Vu GT, Ha GH, et al.
Bibliometric study of AI in healthcare. J Clin Med. 2019;8(3):360.
Hamid S.
Opportunities and risks of AI in healthcare (Internet source).
Panch T, Szolovits P, Atun R.
AI and health systems. J Glob Health. 2018;8(2):020303.
Yang X, Wang Y, Byrne R, Schneider G, Yang S.
AI in drug discovery. Chem Rev. 2019;119(18):10520–10594.
Burton RJ, Albur M, Eberl M, Cuf SM.
AI in urinary infection diagnosis. BMC Med Inform Decis Mak. 2019;19(1):171.
Meskò B, Drobni Z, Bényei E, et al.
Digital health transformation. Mhealth. 2017;3:38.
Cho BJ, Choi YJ, Lee MJ, et al.
Deep learning for cervical cancer detection. Sci Rep. 2020;10(1):13652.
Doyle OM, Leavitt N, Rigg JA.
AI in hepatitis C detection. Sci Rep. 2020;10(1):10521.
Shortliffe EH, Sepúlveda MJ.
Clinical decision support in AI era. JAMA. 2018;320(21):2199–2200.
Massaro M, Dumay J, Guthrie J.
Structured literature review method. Account Audit Account J. 2016;29(5):767–801.
Junquera B, Mitre M.
Bibliometric analysis for research policy. Scientometrics. 2007;71(3):443–454.
Casadesus-Masanell R, Ricart JE.
Business model design. Harvard Business Review. 2011.
Aria M, Cuccurullo C.
bibliometrix: science mapping tool. J Informetr. 2017;11(4):959–975.
