Impact Artificial Intelligence in the Pharmaceutical Industry on Working Culture: A Review

DOI:

https://doi.org/10.37285/ijpsn.2022.15.1.5

Authors

  • Devendra Singh Lodhi Gyan Ganga Institute of Technology & Sciences, Bargi hills, Jabalpur M.P-482003
  • Megha Verma Gyan Ganga Institute of Technology & Sciences, Bargi hills, Jabalpur M.P-482003
  • Pradeep Golani Gyan Ganga Institute of Technology & Sciences, Bargi hills, Jabalpur M.P-482003
  • Akash Singh Pawar Institute of Pharmaceutical Sciences, SAGE University Kailod-Kartal Indore by Road Indore (MP) 452027
  • Sanjay Nagdev Gyan Ganga Institute of Technology & Sciences, Bargi hills, Jabalpur M.P-482003

Abstract

The pharmaceutical and healthcare industries have benefited greatly from artificial  intelligence in recent years. A wide range of pharmaceutical fields, such as this novel  approach, showed potential in drug discovery, continuous manufacturing (CM),  dosage form design, and quality control. This article focuses on the use of artificial  intelligence in the pharmaceutical sector. Before all else, the film sheds light on how  AI will be implemented into health care, as well as its potential benefits. To  conclude, there are several hurdles to overcome in the project implementation. At  present, it's no secret that artificial intelligence (AI) and genetic algorithms (ANNs)  are becoming increasingly popular in the pharmaceutical industry. In the  pharmaceutical industry, artificial intelligence (AI) has shown promise, and it can be  used in combination with robotics. Physical robots could revolutionize the  healthcare industry. To keep their minds sharp and alert, it's used as a social  interaction guide with elderly patients. In the pharmaceutical industry, artificial  intelligence (AI) will help reduce costs and time. 

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Keywords:

Artificial Intelligence, Machine Learning, Pharmaceutical Industry, Product Development, Dosage form design

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Published

2022-02-28

How to Cite

1.
Lodhi DS, Verma M, Golani P, Pawar AS, Nagdev S. Impact Artificial Intelligence in the Pharmaceutical Industry on Working Culture: A Review. Scopus Indexed [Internet]. 2022 Feb. 28 [cited 2024 May 19];15(1):5771-80. Available from: https://ijpsnonline.com/index.php/ijpsn/article/view/2539

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Review Articles

References

Ahuja A(2019). The impact of artificial intelligence in medicine on the future role of the physician,PeerJ7: 7702. 2. Albert E (2019). AI in talent acquisition: a review of AI applications used in recruitment and selection,” Strategy. HR Rev.5: 215– 221.

Bates D, Auerbach A, Schulman P, Wright A, and Saria S (2020) Reporting and implementing interventions involving machine learning and artificial intelligence,Ann. Intern. Med.172(11): S137–S144.

Borges A, Laurindo F, Spínola M, Gonçalves R, and Mattos C (2020) The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions,Int. J. Inf. Manage.2: 102225.

Davenport T and Kalakota R (2019).“The potential for artificial intelligence in healthcare,” Futur. Healthc. J, 6(2): 94.

Dwivedi Y(2019) Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy.Int. J. Inf. Manage 57:101994.

Freedman D (2019) Hunting for new drugs with AI,Nature, 576: S49–S53.

Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta R, and Kumar P (2021) Artificial intelligence to deep learning: Machine intelligence approach for drug discovery,Mol. Divers,25: 1–46.

Harrer S, Shah P, Antony B, and Hu J (2019). “Artificial intelligence for clinical trial design,” Trends Pharmacol. Sci 40 (8): 577–591,

Hamet P, Tremblay J (2017) Artificial intelligence in medicine. Metabolism, 69S: S36-S40.

Huang S, Yang J, Fong S, and Zhao Q (2020).“Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges,” Cancer Lett, 471: 61–71.

Jarrett D, Stride E, Vallis K, and Gooding M (2019) Applications and limitations of machine learning in radiation oncology,Br. J. Radiol 92:1-12,

Jiang F (2017). Artificial intelligence in healthcare: past, present, and future,Stroke Vasc. Neurol., 2:4.

Kalyani D (2020) Artificial intelligence in the pharmaceutical sector: current scene and prospect, The Future of Pharmaceutical Product Development and Research, Elsevier, pp. 73–107.

Kelley K, Fontanetta L, Heintzman M, and Pereira N (2018). Artificial intelligence: Implications for social inflation and insurance,Risk Manag. Insur. 21(3): 373–387.

Larizgoitia A (2020)Assessing the urinary lithogenic risk by multivariate data analysis using analytical results and historic archives (Thesis).

McCoubrey L, Gaisford S, Orlu M, and Basit A(2021). Predicting drug-microbiome interactions with machine learning,Biotechnol. Adv 1: 107797.

Mitchell M (2019).Artificial intelligence: A guide for thinking humans. Penguin UK, pp 1-448.

Nielsen M(2015).Neural networks and deep learning, Determination press San Francisco, CA, pp1-211.

Patel V( 2009).“The coming of age of artificial intelligence in medicine,” Artif. Intell. Med 46(1): 5–17,

Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, and Tekade R (2021) Artificial intelligence in drug discovery and development,Drug Discov. Today26(1):80-93

Shabbir J and Anwer T (2015). Artificial intelligence and its role in near future,J of Latex Class Files, 14 (8): 1-11

Sharma D, Bhargava S, and Singhal K (2020) Internet of Things applications in the pharmaceutical industry,An Industrial IoT Approach for Pharmaceutical Industry Growth, Elsevier, pp. 153–190.

Shieh J, Wu H, and Huang K(2010). A DEMATEL method in identifying key success factors of hospital service quality,Knowledge-Based Syst23 (3): 277–282.

Shin D(2021) Embodying algorithms, enactive artificial intelligence, and the extended cognition: You can see as much as you know about the algorithm,J. Inf. Sci1:

Siddique S and Chow J (2021). Machine learning in healthcare communication,Encyclopedia, 1(1): 220–239,

Ting D(2019).Deep learning in ophthalmology: the technical and clinical considerations, Prog. Retin. Eye Res72: 100759.

Tolios A, DeLas R, Hovig E, Trouillas P, Scorilas A, and Mohr T, (2020) “Computational approaches in cancer multidrug resistance research: Identification of potential biomarkers, drug targets and drug-target interactions,” Drug Resist. Update., 48: 100662.

Topol E (2019) Deep medicine: how artificial intelligence can make healthcare human again. Basic Books Inc New York, pp 1-400.

Waymond R(2020)Artificial intelligence in a throughput model: Some major algorithms. CRC Press London pp 1-220.

Woo M (2019), An AI boost for clinical trials Nature, 573: S10.

Wu Z, Lawrence P, Ma A, Zhu J, Xu D, and Ma Q(2020) Single-cell techniques and deep learning in predicting drug response,Trends Pharmacol. Sci.,41(12):1050-1065.