THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN MEDICINE
Keywords:
Medical diagnosis, Disease detection, Treatment planning, Personalized medicine, Drug discovery, Machine learning, Deep learning, High-Performance Computing (HPC), Pharmaceutical companies Digital dentistry, Image-based disease detection, Diagnosis support systems, Robotic support in dentistry, Oral healthcare.Abstract
This article delves into the transformative role of Artificial Intelligence (AI) in reshaping healthcare, with a focus on its historical roots, current applications, and future potential. It navigates through the evolution of AI, from its conceptualization in the 1950s to its contemporary applications in medicine, particularly in drug discovery, medical diagnosis, and dentistry. The narrative highlights the challenges and ethical considerations accompanying AI integration in healthcare and emphasizes the collaborative relationship between human expertise and machine precision. The comprehensive exploration of AI’s impact in diverse medical domains underscores its pivotal role in advancing patient care and medical innovation.
References
Rantanen, J,Khinast, J. (2015). The future of pharmaceutical manufacturing sciences. Pharmaceutical Sciences, 3612–3638.
Xu X, Liu C,Zheng Y. (2018). 3D tooth segmentation and labeling using deep convolutional neural networks. IEEE Transactions on Visualization and Computer Graphics , 2336-2348.
al., S. K. (2020). Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives. IEEE Access.
Allen Newell and H. A. Simon. (1963). "GPS: A Program that Simulates Human Thought". New York: McGraw-Hill.
Alvarez-Machancoses, O. and Fernandez-Martinez, J.L. (2019.). Using artificial intelligence methods to speed up drug discovery. Expert Opinion on Drug Discovery., 769–777.
Buchanan, B. G. ( 2005). A (Very) Brief History of Artificial Intelligence. AI Magazine, 53–60.
C.-H. Weng, T. C.-K. Huang, R.-P. Han. (2016). Disease prediction with different types of neural network classifiers. Telematics and Informatics, 277–292.
Chan, H. ( 2019.). Advancing drug discovery via artificial intelligence.". Trends in Pharmacological Sciences, 592–604.
Chen, W. (2016). Mathematical model-based accelerated development of extended-release metformin hydrochloride tablet formulation. AAPS PharmSciTech, 1007–1013.
Ciallella, H.L. Zhu, H. (2019). Advancing computational toxicology in the big data era by artificial intelligence: data-driven and mechanism-driven modeling for chemical toxicity. Chemical Research in Toxicology, 536–547.
Crevier, D. (1993). AI: The Tumultuous Search for Artificial Intelligence. New York, NY: BasicBooks.
D.H. Kim, S.N. Jeong, and S.H. Choi. (2018). Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of Dentistry.
Dana, D. e. (2018). Deep learning in drug discovery and medicine; scratching the surface. Molecules.
Fetzer, J. (1990). What is artificial Intelligence? Springer Netherlands, pp. 3-27.
Friedman J, Hastie T, Tibshirani R. (2001). The Elements of Statistical Learning. New York: Springer Series in Statistics.
Grischke J, Johannsmeier L, Eich L, Griga L, Haddadin S, . (2020). Dentronics: towards robotics and artificial intelligence in dentistry. Dent Materials.
Guo, M. (2002). A prototype intelligent hybrid system for hard gelatin capsule formulation development. Pharmaceutical Technology., 44–52.
Hatvani J, Horváth A, Michetti J. (2018). Deep learning-based super-resolution applied to dental computed tomography. IEEE Transactions on Radiation and Plasma Medical Sciences., 120-128.
Hung M, Voss MW, Rosales MN,. (2019). Application of machine learning for diagnostic prediction of root caries. Gerodontology, 395-404.
J. Yen R. Langari. (1999). Fuzzy Logic: Intelligence, Control, and Information. Upper Saddle River, NJ, USA: Prentice-Hall.
J.K. Ruffle, A.D. Farmer, Q. Aziz. (2019). Artificial intelligence-assisted gastroenterology—promises and pitfalls. The American Journal of Gastroenterology, 422-428.
Ketterhagen, W. (2009). Process modeling in the pharmaceutical industry using the discrete element method. Pharmaceutical Sciences, 442–470.
Lee, C. C. (1990). Fuzzy logic in control systems: Fuzzy logic controller. I. IEEE Transactions on Systems, Man, and Cybernetics, 404–418.
M. Chen, Y. Hao, K. Hwang, L. Wang, L. Wang. (2017). Disease prediction by machine learning over big data from healthcare communities. IEEE Access, 8869–8879.
Mak, K.-K. Pichika, M.R. (2019). Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today, 773–780.
McCorduck, P. (2004). Machines Who Think (2nd ed.). A. K. Peters, Ltd.
Mehta, C. e. (2019). Computational modeling for formulation design. Drug Discovery Today, 781–788.
Newquist, H. (1994). The Brain Makers: Genius, Ego, And Greed in the Quest For Machines That Think. New York: Macmillan/SAMS.
Newquist, H. (1994). The Brain Makers: Genius, Ego, And Greed in the Quest For Machines That Think. New York: Macmillan/SAMS.
Nicolas SabouretLizete De Assis. (2021). Understanding artificial intelligence. CRC Press, Taylor & Francis Group, LLC.
P. Croft, D. G. Altman, J. J. Deeks. (2015). The science of clinical practice: Disease diagnosis or patient prognosis? Evidence about ‘what is likely to happen’ should shape clinical practice. BMC Medicine, 20.
R. H. Scheuermann, W. Ceusters, B. Smith. ( 2009). Toward an ontological treatment of disease and diagnosis. Summit on Translational Bioinformatics, 116.
Schwendicke F, Singh T, Lee JH. (2021). Artificial intelligence in dental research. Journal of Dentistry.
Scully, J. L. (2004). What is a disease? EMBO Reports, 650–653.
Sellwood, M. e. (2018). Artificial intelligence in drug discovery. Future Science, 2025–2028.
Stuart J. Russell and Peter Norvig. (2003). Artificial Intelligence: A Modern Approach (2nd ed.). Upper Saddle River, New Jersey: Prentice Hall.
Tian S, Dai N, Zhang B, Yuan F, Yu Q,Cheng X. (2019). Automatic classification and segmentation of teeth on 3D dental model using hierarchical deep learning networks. IEEE Access, 84817-84828.
Vyas, M. e. (2018). Artificial intelligence: the beginning of a new era in pharmacy profession. Asian Journal of Pharmacy, 72–76.
Zhao, C. (2006). Toward intelligent decision support for pharmaceutical product development. Journal of Pharmaceutical Innovation, 23–35.
Zhu, H. (2020). Big data and artificial intelligence modeling for drug discovery. Annual Review of Pharmacology and Toxicology, 573–589.
J. Betancur and F. Commandeur, ‘‘Deep learning for prediction of obstruc tive disease from fast myocardial perfusion SPECT: A multicenter study,’’
JACC: Cardiovascular Imag., vol. 11, no. 11, pp. 1654–1663, 2018
E. Choi, M. T. Bahadori, A. Schuetz, W. F. Stewart, and J. Sun, ‘‘Doctor ai:
Predicting clinical events via recurrent neural networks,’’ in Proc. Mach.
Learn. Healthcare Conf., 2016, pp. 301–318
Deep Blue". IBM Research. Retrieved 10 September 2010.
"DARPA Grand Challenge – home page". Archived from the original on 31 October 2007.
Welcome". Archived from the original on 5 March 2014. Retrieved 25 October 2011
