Prospects of Artificial Intelligence in the Improvement of Healthcare Professions: A Review

Authors

  • Tanveer A. Khan Faculty of Pharmacy, Riyadh Elm University, Riyadh 13244, Saudi Arabia https://orcid.org/0000-0002-6050-3115
  • Muhammad Masood Ahmad Department of Pharmaceutics, College of Pharmacy, Jouf University, Sakaka 72388, Aljouf, Saudi Arabia
  • Muhammad Usman Munir Department of Pharmaceutical Chemistry, College of Pharmacy, Jouf University, Sakaka 72388, Aljouf, Saudi Arabia
  • Syed Nasir Abbas Bukhari Department of Pharmaceutical Chemistry, College of Pharmacy, Jouf University, Sakaka 72388, Aljouf, Saudi Arabia
  • Muhammad Ayub Naveed National Control Laboratory of Biological, Drug Regulatory Authority, Islamabad, Pakistan

DOI:

https://doi.org/10.31436/jop.v4i1.238

Keywords:

Artificial Intelligence (AI), robotics, medical diagnosis, pharmacy, drug discovery

Abstract

In 1956, the development of engineering science led to the birth of the first intelligent machines. This has led to the term Artificial Intelligence (AI) coined by a scientist named John McCarthy. The basic purpose of AI is to minimise human cognitive function. Advanced computer technology allows humans to do comparative critical thinking and simulate intelligent behaviour by producing intelligent modelling to solve boost and uplift cracking problems, imaging knowledge, and making a decision.  Consequently, rapid analytical technique progress, powered by the increasing data availability in healthcare, has directed a paradigm shift in the healthcare system, especially in the analysis of medical imaging in the disease of oncology by detection of brain tumours. It helps the diagnosis of cancer stages based on the abnormal cell growth in the brain. AI is also important in diagnosis and treatment in other medical departments like dermatology, nephrology, ophthalmology, pathology, pulmonary medicine, endocrinology, gastroenterology, and neurology.  In recent years, AI has played a key role in pharmacy, drug delivery, drug discovery, drug formulation development, hospital pharmacy, and poly-pharmacology. The term AI has a broad range of applications in medicine, medical statistics, medical diagnosis, human biology, pharmacy, clinical, and robotics. Automated selective medication uses the scientific task approach of pharmacists and is only possible by the use of AI. Algorithmic tasks reserved by using AI automation and such type of AI demonstration are better than pharmacists in comparison. In general terms of AI, the minimal intervention of humans implies intelligent behaviour through computer models. The invention of robots is deemed the starting point of the AI journey. It started with the introduction of robotic biosynthetic machines utilised to support medical personnel. In the meantime, an AI is capable of analysing complex clinical and medical data where a potentially significant data set relationship can be used for treatment and predicting outcomes in the case study and diagnosis.

References

Afzal, N., Sohn, S., Abram, S., Scott, C. G., Chaudhry, R., Liu, H., Kullo, I. J., & Arruda-Olson, A. M. (2017). Mining peripheral arterial disease cases from narrative clinical notes using natural language processing. Journal of Vascular Surgery, 65(6), 1753–1761.

Agatonovic-Kustrin, S., & Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis, 22(5), 717–727.

Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., & Zhavoronkov, A. (2016). Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Molecular Pharmaceutics, 13(7), 2524–2530.

Allen, R., & Smith, D. (2001). Neuro-fuzzy closed-loop control of depth of anesthesia. Artificial Intelligence in Medicine, 21(1–3), 185–191.

Altman, R. B. (2017). Artificial intelligence (AI) systems for interpreting complex medical datasets. Clinical Pharmacology & Therapeutics, 101(5), 585–586.

Bambauer, J. R. (2017). Dr. Robot. UCDL Rev., 51, 383.

Barnett, G. O., Cimino, J. J., Hupp, J. A., & Hoffer, E. P. (1987). DXplain: an evolving diagnostic decision-support system. Jama, 258(1), 67–74.

Behloul, F., Lelieveldt, B. P. F., Boudraa, A., Janier, M. F., Revel, D., & Reiber, J. H. C. (2001). Neuro-fuzzy systems for computer-aided myocardial viability assessment. IEEE Transactions on Medical Imaging, 20(12), 1302–1313.

Boyd, A. M., & Chaffee, B. W. (2019). Critical evaluation of pharmacy automation and robotic systems: a call to action. Hospital Pharmacy, 54(1), 4.

Chan, H., Sahiner, B., Lam, K. L., Petrick, N., Helvie, M. A., Goodsitt, M. M., & Adler, D. D. (1998). Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces. Medical Physics, 25(10), 2007–2019.

De Dombal, F. T., Leaper, D. J., Staniland, J. R., McCann, A. P., & Horrocks, J. C. (1972). Computer-aided diagnosis of acute abdominal pain. Br Med J, 2(5804), 9–13.

Deo, R. C. (2015). Machine learning in medicine. Circulation, 132(20), 1920–1930.

Deshmukh, R. D., & Jadhav, C. (2014). Study of different brain tumor MRI image segmentation techniques. International Journal of Science, Engineering and Computer Technology, 4(4), 133.

Devunooru, S., Alsadoon, A., Chandana, P. W. C., & Beg, A. (2021). Deep learning neural networks for medical image segmentation of brain tumors for diagnosis: a recent review and taxonomy. Journal of Ambient Intelligence and Humanized Computing, 12(1), 455–483.

Dignum, V. (2018). Ethics in artificial intelligence: introduction to the special issue. In Ethics and Information Technology (Vol. 20, Issue 1, pp. 1–3). Springer.

Dogra, J., Jain, S., Sharma, A., Kumar, R., & Sood, M. (2020). Brain tumor detection from MR images employing fuzzy graph cut technique. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), 13(3), 362–369.

Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., Van Der Kouwe, A., Killiany, R., Kennedy, D., & Klaveness, S. (2002). Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3), 341–355.

Gill, N. S. (2017). Overview of Artificial Neural Networks and their Applications. Xenonstack: A Stack Innovator.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

Gordillo, N., Montseny, E., & Sobrevilla, P. (2013). State-of-the-art survey on MRI brain tumor segmentation. Magnetic Resonance Imaging, 31(8), 1426–1438.

Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, S36–S40.

Handels, H., Roß, T., Kreusch, J., Wolff, H. H., & Poeppl, S. J. (1999). Feature selection for optimized skin tumor recognition using genetic algorithms. Artificial Intelligence in Medicine, 16(3), 283–297.

Holland, J. H., Mahajan, M., Kumar, S., & Porwal, R. (1975). Adaptation in Natural and Artificial Systems, the University of Michigan Press, Ann Arbor, MI. 1975. In Applying the genetic algorithm to increase the efficiency of a data flow-based test data generation approach (pp. 1–5).

Jefferson, M. F., Pendleton, N., Lucas, S. B., & Horan, M. A. (1997). Comparison of a genetic algorithm neural network with logistic regression for predicting outcome after surgery for patients with non-small cell lung carcinoma. Cancer: Interdisciplinary International Journal of the American Cancer Society, 79(7), 1338–1342.

Jha, S., & Topol, E. J. (2016). Adapting to artificial intelligence: radiologists and pathologists as information specialists. Jama, 316(22), 2353–2354.

Kantor, P. (2001). Foundations of statistical natural language processing. Information Retrieval, 4(1), 80.

Khanna, V., Ahuja, R., & Popli, H. (2020). Role of Artificial Intelligence in Pharmaceutical Marketing: A comprehensive review. Journal of Advanced Scientific Research, 11(3).

Khatib, M. M. El, & Ahmed, G. (2020). Robotic pharmacies potential and limitations of artificial intelligence: a case study. International Journal of Business Innovation and Research, 23(3), 298–312.

Klopman, G. (1984). Artificial intelligence approach to structure-activity studies. Computer-automated structure evaluation of the biological activity of organic molecules. Journal of the American Chemical Society, 106(24), 7315–7321.

Knebel, E., & Greiner, A. C. (2003). Health professions education: A bridge to quality.

Liew, C. (2018). The future of radiology augmented with artificial intelligence: a strategy for success. European Journal of Radiology, 102, 152–156.

Liu, R., Chen, P., Li, X., Wu, Z., Gao, X., Chen, X., Zhang, L., Wang, Q., & Li, Z. (2017). Artificial intelligence sense technology: new technology in pharmaceutical sciences?. Chinese Journal of Pharmaceutical Analysis, 37(4), 559–567.

Miller, R. A. (1994). Medical diagnostic decision support systems—past, present, and future: a threaded bibliography and brief commentary. Journal of the American Medical Informatics Association, 1(1), 8–27.

Miller, T. P., Li, Y., Getz, K. D., Dudley, J., Burrows, E., Pennington, J., Ibrahimova, A., Fisher, B. T., Bagatell, R., & Seif, A. E. (2017). Using electronic medical record data to report laboratory adverse events. British Journal of Haematology, 177(2), 283–286.

Mukherjee, S. (2017). AI versus MD: What happens when the diagnosis is automated? The New Yorker, 3.

Myronenko, A. (2018). 3D MRI brain tumor segmentation using autoencoder regularization. International MICCAI Brainlesion Workshop, 311–320.

Narayanan, M. N., & Lucas, S. B. (1993). A genetic algorithm to improve a neural network to predict a patient’s response to warfarin. Methods of Information in Medicine, 32(01), 55–58.

Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016). Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Transactions on Medical Imaging, 35(5), 1240–1251.

Roberts, K., Boland, M. R., Pruinelli, L., Dcruz, J., Berry, A., Georgsson, M., Hazen, R., Sarmiento, R. F., Backonja, U., & Yu, K.-H. (2017). Biomedical informatics advancing the national health agenda: the AMIA 2015 year-in-review in clinical and consumer informatics. Journal of the American Medical Informatics Association, 24(e1), e185–e190.

Ross, C., & Swetlitz, I. (2017). IBM pitched its Watson supercomputer as a revolution in cancer care. It’s nowhere close. Stat.

Rouse, M. (2017). IBM Watson supercomputer.

Salem, W. S., Seddik, A. F., & Ali, H. F. (2013 n.d.). A Review on Brain MRI Image Segmentation.

Shortliffe, E. (2012). Computer-based medical consultations: MYCIN (Vol. 2). Elsevier.

The Coca-Cola Company. (2017). 2016 sustainability report. Retrieved from The Coca Cola Companywebsite:http://www.cocacolacompany.com/content/dam/journey/us/en/private/fileassets/pdf/2017/2016-sustainability–update/2016-Sustainability-Report-The-Coca-ColaCompany.pdf

Tustison, N. J., Shrinidhi, K. L., Wintermark, M., Durst, C. R., Kandel, B. M., Gee, J. C., Grossman, M. C., & Avants, B. B. (2015). Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics, 13(2), 209–225.

Ulfa, A. M., Afandi Saputra, Y., & Nguyen, P. T. (2019). Role of artificial intelligence in pharma science. Journal of Critical Reviews, 7(1), 2020.

Verma, B., & Zakos, J. (2001). A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE Transactions on Information Technology in Biomedicine, 5(1), 46–54.

Vyas, M., Thakur, S., Riyaz, B., Bansal, K. K., Tomar, B., & Mishra, V. (2018). Artificial intelligence: the beginning of a new era in the pharmacy profession. Asian J Pharm, 12(2), 72–76.

Wadhwa, A., Bhardwaj, A., & Verma, V. S. (2019). A review on brain tumor segmentation of MRI images. Magnetic Resonance Imaging, 61, 247–259.

Xuan, X., & Liao, Q. (2007). Statistical structure analysis in MRI brain tumor segmentation. Fourth International Conference on Image and Graphics (ICIG 2007), 421–426.

Yang, Q., & Harris, J. G. (2010a). Dynamic range control for audio signals using fourth-order level estimation. Paper presented at the 129th Audio Engineering Society Convention, San Francisco, CA.

Yang, Q., & Harris, J. G. (2010b). A higher-order spectro-temporal integration model for predicting signal audibility. Paper presented at the International Conference on Acoustics, Speech, and Signal Processing, Dallas, TX.

Yu, K.-H., & Snyder, M. (2016). Omics profiling in precision oncology. Molecular & Cellular Proteomics, 15(8), 2525–2536.

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Published

2024-01-31

How to Cite

Khan, D. T., Ahmad, M. M., Munir, M. U., Bukhari, S. N. A. ., & Naveed, M. A. (2024). Prospects of Artificial Intelligence in the Improvement of Healthcare Professions: A Review. Journal of Pharmacy, 4(1), 129–137. https://doi.org/10.31436/jop.v4i1.238

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Section

Review Articles