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  • A Overview On Role Of Artificial Intelligence In Future Of Pharmacy
  • 1Department of pharmaceutical quality assurance technique, JSPM College of Pharmacy, Wagholi Pune  India 412207
    2Department of pharmaceutical quality assurance technique, Sinhgad College of Pharmacy Vadgaon, Pune India 412105
    3Department of pharmaceutical quality assurance technique, JSPM College of Pharmacy, Wagholi Pune  India 412207
     

Abstract

This systematic review examines how artificial intelligence (AI) is developing to address issues facing the pharmaceutical sector, with a particular emphasis on drug discovery, supply chain disruptions, clinical trials, and trial operations. According to the research, AI technologies might speed up medication discovery, simplify clinical trial operations, and completely change supply chain management. Technology that is transforming several industries, including healthcare, is artificial intelligence (AI). AI has the ability to greatly enhance patient care and drug management in pharmacy practice. Several AI applications in pharmacy practice are examined in this paper. There are many of chances to apply AI in education, but there are also a lot of unanswered questions about how AI will affect healthcare in the future. Thus, it may be said that Artificial Intelligence is rapidly evolving in all fields, including pharmacy. Further research and development are needed to keep up with the current state of the field.

Keywords

Drug discovery, Extracellular matrix, Fibrosis, Inflammation, TGF-?.

Introduction

To find subjects of interest for this narrative review, the major databases (PubMed, Google Scholar, and Scopus) were searched for related papers. A subfield of computer science known as artificial intelligence (AI) studies intelligent machines that solve issues through symbolic programming. It has been actively engaged in the field of issue solution science by utilizing pharmaceutical engineering and workplace health care.[1] Artificial intelligence produces findings comparable to human attentional processes.[2] Creating effective systems that apply or utilize training data, exhibit explicit or approximate fusion, and engage in self-correction and adaptability are often steps in this process. [3] The traditional pharmaceutical system is mostly dependent on human knowledge and manual procedures, which can cause delays, mistakes, and inefficiencies. For instance, filling a prescription requires multiple manual procedures, including reading the prescription, delivering the drug, and double-checking the dosage and timing. [4-6] AI in pharmacy apps has enormous ramifications for people living at home, offering many benefits to consumers. Users can obtain drug information, medical advice, and instructions on dosage and usage from the comfort of their homes with pharmacy apps that incorporate AI. Individuals who live in distant places or have mobility impairments may find this extremely helpful. AI is also capable of reviewing a user's medical history and assisting in the creation of customized drug schedules that include dosage, frequency, and timing in order to make sure users are taking their drugs as directed. Users can get round-the-clock assistance for any questions or concerns regarding medications by integrating AI into pharmacy apps, which eliminates the need to wait for a pharmacist's office hours.[7-10] With artificial intelligence's assistance Industrialization produced automation because of the necessity to boost output, produce goods of a consistent high quality, and relieve laborers of heavy and dangerous tasks. Nowadays, technological advancements make up the fundamental components of automation. The majority of pharmaceutical companies see the advantages of implementing new technology, but there is still a persistent and concerning disconnect between strategy and an organization's capacity to implement and use a functional data analytics solution. [11-14] The pharmaceutical business may now use artificial intelligence to tackle issues that were previously intractable with straightforward data analysis. [15] Although the application of AI in pharmacy systems is gaining popularity, it is crucial to remember that this technology should only be used after thorough planning and consideration. While AI has the ability to completely transform the business by offering patients 24/7 support and tailored medication management, it is crucial to make sure that it is applied responsibly and that any potential drawbacks or difficulties are taken care of. Consequently, to guarantee that AI is applied in the pharmacy system in a way that is both responsible and successful, a careful and evidence-based strategy is required. There are numerous ways in which integrating AI into the pharmacy system might improve the planet. First first, by giving patients 24/7 support and individualized medication management, it can increase access to healthcare services. [16, 17]

APPLICATION

The pharmaceutical sector can use technology improvements to speed up innovation. Artificial intelligence comes to mind as a recent advancement. Artificial intelligence (AI) has the potential to significantly improve decision-making by analyzing data and providing findings that save money, time, and human labor—thereby potentially saving lives. [18] Repositioning Drugs To determine which molecular starting points are most suitable for resuming a project using Repurposing a well-known medication or combination to see if it can be used to treat similar or unrelated disorders based on the way it works, its targets, Proteomic or genomic fingerprinting

Identification of Alternative Indications Which novel, encouraging uses exist for a certain class of inhibitors? By examining all of the information related to indications and classifying it according to published research and trials' quality, quantity, and relevance [19, 20] AI in Medical AI systems must be taught using data generated by clinical activities, such as screening, diagnosis, therapy, and so forth, before they can be used in health care applications. A significant amount of the work on AI specifically examines data from diagnosis imaging, genetic testing, and electrodiagnosis at the diagnosis stage. [21] AI in medical settings Data collection, archiving, normalization, and tracing are important uses of AI in the healthcare industry. Deep genomics looks for mutations and connections to the disease by identifying patterns in massive databases of genetic data and medical records.  Drug-drug interactions (DDIs) have been found to be a major contributor to adverse drug reactions (ADRs), which drive up healthcare expenses. [22-24] Social issues The use of AI in healthcare will replace jobs, rendering healthcare personnel redundant, which is one of the main social worries. AI-based healthcare interventions are met with mistrust and resistance because to the fear of replacement. But a lot of this notion is based on a misinterpretation of AI in all of its manifestations. [25] AI-Based Diagnosis A software tool that allows medical professionals to perform cardiovascular ultrasound imaging without the need for specialized training has received approval from the Food and Drug Administration. It operates under the AI concept and provides continuous guidance in addition to the ability to store images of the diagnostic area. It provides the user with general information, including how to operate, and it responds instantly with a picture. It may be referred to as "co-pilot" because those without such expertise can do ultrasonography without the assistance of a professional. Developing Patient Treatment Plans The most well-known AI-based idea is IBM Watson, which helps oncologists analyze patient data to determine if it is appropriately arranged [26, 27]

Application and of disadvantages AI in healthcare: [28-32]

  • Artificial Intelligence in Radiology
  • Artificial Intelligence in Ophthalmology
  • Artificial Intelligence in Cardiology
  • Gastroenterology with AI
  • Artificial Intelligence in Oncology
  • AI for targeted genetic therapy and diagnosis
  • Regarding medical accuracy
  • preserving medical documentation
  • The precision of medicine
  • Analysis of the healthcare system
  • Drug creation
  • Medication modeling Dosage planning
  • Protein structure
  • Pharmacokinetic and pharmacodynamic modeling
  • In-vitro and in-vivo correlation

Disadvantages of AI :

  • Legal Concerns with Trust and Responsibility
  • Security and privacy of data
  • patient security
  • Moral consideration
  • AI mostly lacks human interaction since it is incapable of thinking; instead, it can only act in accordance with instructions.
  • Robots with AI have the ability to surpass humans and enslave humanity.
  • If machines are placed in the wrong hands, they can quickly cause havoc. That is, for many people, at least, a fear.

CONCLUSION:

The review highlights how AI can expedite the entire drug development process, from target identification to medication. Given the cutting-edge problems of today and the direction of the future, artificial intelligence (AI) has enormous potential to improve healthcare and medication discovery worldwide.  Deep learning and natural language processing are two AI techniques that are revolutionizing drug development by comprehending and analyzing enormous amounts of bioscience data.  AI integration is portrayed as a game-changing force in labor efficiency, clinical trials, and supply chain management.  Health care providers can improve their decision-making and offer patients individualized care by implementing AI into clinical practice.

REFERENCES

  1. Dasta, J. F. (1992). Application of artificial intelligence to pharmacy and medicine. Hospital pharmacy, 27(4), 312-5.
  2. Mak KK, Pichika MR. Artificial intelligence in drug development: Present status and future prospects. Drug Discov Today. 2019;24(3):773-80.
  3. Hassanzadeh P, Atyabi F, Dinarvand R. The significance of artificial intelligence in drug delivery system design. Adv Drug Deliv Rev. 2019;151: 169-90.
  4. Allen Flynn. Using artificial intelligence in health-system pharmacy practice: finding new patterns that matter. Pharm.D., Ph.D Am J Health Syst Pharm. 2019;76(9): 622–627. https://doi.org/10.1093/ajhp/zxz018, 1 May.
  5. Vaishya R, Javaid M, Khan IH, Haleem A. Artificial intelligence (AI) applications for COVID-19 pandemic, diabetes & metabolic syndrome. Clin Res Rev. 2020;14(4): 337–339. https://doi.org/10.1016/j.dsx.2020.04.012, 2020.
  6. Khan O, Khan MZ, Alam MT, et al. Comparative study of soft computing and metaheuristic models in developing reduced exhaust emission characteristics for diesel engine fueled with various blends of biodiesel and metallic nanoadditive mixtures: an ANFIS–GA–HSA approach. ACS Omega. 2023;8:7344–7367. https:// doi.org/10.1021/acsomega.2c05246, 2023.
  7. Ahmad S, Parvez M, Khan TA, Siddiqui SA, Khan O. Performance comparison of solar powered cogeneration and trigeneration systems via energy and exergy analyses. Int J Exergy. 2022;39(4):395–409.
  8. Islam SMU, Khan S, Ahmad H, Rahman MAU, Tomar S, Khan MZ. Assessment of challenges and problems in supply chain among retailers during COVID-19 epidemic through AHP-TOPSIS hybrid MCDM technique. Internet of Things and Cyber-Physical Systems. 2022. https://doi.org/10.1016/j.iotcps.2022.10.001.
  9. Javaid M, Haleem A, Singh RP, Suman R. Sustaining the healthcare systems through the conceptual of biomedical engineering: a study with recent and future potentials. Biomedical Technology. 2023;1:39–47. https://doi.org/10.1016/j.bmt.2022.11.004.
  10. Ahmad S, Alam MT, Bilal M, Khan O, Khan MZ. Analytical modelling of HVAC-IoT systems with the aid of UVGI and solar energy harvesting. Energy Harvesting. 2022: 65–80.
  11. Russell S, Dewey D, Tegmark M. Research priorities for robust and beneficial artificial intelligence. Ai Magazine. 2015 Dec 31;36(4):105-14.
  12. Lakshmi Teja T, Keerthi P, Debarshi Datta NB. Recent trends in the usage of robotics in pharmacy.
  13. Yussupova N, Kovács G, Boyko M, Bogdanova D. Models and methods for quality management based on artificial intelligence applications. Acta Polytechnica Hungarica. 2016 Mar;13(3):45-60
  14. Zhang Y, Balochian S, Agarwal P, Bhatnagar V, Housheya OJ. Artificial intelligence and its applications 2014.
  15. Khan O, Yadav AK, Khan ME, Parvez M. Characterization of bioethanol obtained from Eichhornia Crassipes plant; its emission and performance analysis on CI engine. Energy Sources, Part A Recovery, Util Environ Eff. 2019;43:1–11.
  16. Khan O, Khan MZ, Ahmad N, Qamer A, Alam MT, Siddiqui AH. Performance and emission analysis on palm oil derived biodiesel coupled with Aluminium oxide nanoparticles. Mater Today Proc. 2019;46.
  17. Zhang Y, Balochian S, Agarwal P, Bhatnagar V, Housheya OJ. Artificial intelligence and its applications 2014.
  18. 20  Yussupova N, Kovács G, Boyko M, Bogdanova D. Models and methods for quality management based on artificial intelligence applications. Acta Polytechnica Hungarica. 2016 Mar;13(3):45-60
  19. Roff HM. Advancing human security through artificial intelligence. Chatham House; 2017 May.
  20. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y. Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology. 2017 Dec 1;2(4).
  21. Han K, Cao P, Wang Y, et al. A review of approaches for predicting drug–drug interactions based on machine learning. Front Pharmacol. 2022 Jan 28;12:3966.
  22. Mei S, Zhang K. A machine learning framework for predicting drug–drug interactions. Sci Rep. 2021 Sep 2;11(1):17619
  23. Sun TQ, Medaglia R. Mapping the challenges of artificial intelligence in the public sector: evidence from public healthcare. Gov Inf Q. 2019 Apr 1;36(2):368–383.
  24. Zauderer, M. G., Gucalp, A., Epstein, A. S., Seidman, A. D., Caroline, A., Granovsky, S., Fu, J., Keesing, J., Lewis, S., Co, H., Petri, J., Megerian, M., Eggebraaten, T., Bach, P., Kris, M. G. 2014. Piloting IBM Watson Oncology within Memorial Sloan Kettering’s regional network. Journal of Clinical Oncology, 32(15_suppl):e17653.
  25. Staines, R. 2020. FDA approves Caption Health’s AIdriven cardiac ultrasound software. Pharmaphorum, Accessed On: 10 Feb 2021
  26.  Agarwal S, Gupta RK, Kumar S. Artificial Intelligence in the Pharmacy Profession. Int. J. Res. Pharm. Sci. 2021;12:2269-79.
  27. Shukla MK, Srivastava H, Gupta N, Yadav R. A Detailed Review On Artificial Intelligence In Pharmacy.
  28. Sharma T, Mankoo A, Sood V. Artificial intelligence in advanced pharmacy. International Journal of Science and Research Archive. 2021;2(1):047-54.
  29. Khan O, Parvez M, Kumari P, Parvez S, Ahmad S. The future of pharmacy: how AI is revolutionizing the industry. Intelligent Pharmacy. 2023 Jun 1;1(1):32-40.
  30. Chalasani SH, Syed J, Ramesh M, Patil V, Kumar TP. Artificial intelligence in the field of pharmacy practice: A literature review. Exploratory Research in Clinical and Social Pharmacy. 2023 Dec 1;12:100346.

Reference

  1. Dasta, J. F. (1992). Application of artificial intelligence to pharmacy and medicine. Hospital pharmacy, 27(4), 312-5.
  2. Mak KK, Pichika MR. Artificial intelligence in drug development: Present status and future prospects. Drug Discov Today. 2019;24(3):773-80.
  3. Hassanzadeh P, Atyabi F, Dinarvand R. The significance of artificial intelligence in drug delivery system design. Adv Drug Deliv Rev. 2019;151: 169-90.
  4. Allen Flynn. Using artificial intelligence in health-system pharmacy practice: finding new patterns that matter. Pharm.D., Ph.D Am J Health Syst Pharm. 2019;76(9): 622–627. https://doi.org/10.1093/ajhp/zxz018, 1 May.
  5. Vaishya R, Javaid M, Khan IH, Haleem A. Artificial intelligence (AI) applications for COVID-19 pandemic, diabetes & metabolic syndrome. Clin Res Rev. 2020;14(4): 337–339. https://doi.org/10.1016/j.dsx.2020.04.012, 2020.
  6. Khan O, Khan MZ, Alam MT, et al. Comparative study of soft computing and metaheuristic models in developing reduced exhaust emission characteristics for diesel engine fueled with various blends of biodiesel and metallic nanoadditive mixtures: an ANFIS–GA–HSA approach. ACS Omega. 2023;8:7344–7367. https:// doi.org/10.1021/acsomega.2c05246, 2023.
  7. Ahmad S, Parvez M, Khan TA, Siddiqui SA, Khan O. Performance comparison of solar powered cogeneration and trigeneration systems via energy and exergy analyses. Int J Exergy. 2022;39(4):395–409.
  8. Islam SMU, Khan S, Ahmad H, Rahman MAU, Tomar S, Khan MZ. Assessment of challenges and problems in supply chain among retailers during COVID-19 epidemic through AHP-TOPSIS hybrid MCDM technique. Internet of Things and Cyber-Physical Systems. 2022. https://doi.org/10.1016/j.iotcps.2022.10.001.
  9. Javaid M, Haleem A, Singh RP, Suman R. Sustaining the healthcare systems through the conceptual of biomedical engineering: a study with recent and future potentials. Biomedical Technology. 2023;1:39–47. https://doi.org/10.1016/j.bmt.2022.11.004.
  10. Ahmad S, Alam MT, Bilal M, Khan O, Khan MZ. Analytical modelling of HVAC-IoT systems with the aid of UVGI and solar energy harvesting. Energy Harvesting. 2022: 65–80.
  11. Russell S, Dewey D, Tegmark M. Research priorities for robust and beneficial artificial intelligence. Ai Magazine. 2015 Dec 31;36(4):105-14.
  12. Lakshmi Teja T, Keerthi P, Debarshi Datta NB. Recent trends in the usage of robotics in pharmacy.
  13. Yussupova N, Kovács G, Boyko M, Bogdanova D. Models and methods for quality management based on artificial intelligence applications. Acta Polytechnica Hungarica. 2016 Mar;13(3):45-60
  14. Zhang Y, Balochian S, Agarwal P, Bhatnagar V, Housheya OJ. Artificial intelligence and its applications 2014.
  15. Khan O, Yadav AK, Khan ME, Parvez M. Characterization of bioethanol obtained from Eichhornia Crassipes plant; its emission and performance analysis on CI engine. Energy Sources, Part A Recovery, Util Environ Eff. 2019;43:1–11.
  16. Khan O, Khan MZ, Ahmad N, Qamer A, Alam MT, Siddiqui AH. Performance and emission analysis on palm oil derived biodiesel coupled with Aluminium oxide nanoparticles. Mater Today Proc. 2019;46.
  17. Zhang Y, Balochian S, Agarwal P, Bhatnagar V, Housheya OJ. Artificial intelligence and its applications 2014.
  18. 20  Yussupova N, Kovács G, Boyko M, Bogdanova D. Models and methods for quality management based on artificial intelligence applications. Acta Polytechnica Hungarica. 2016 Mar;13(3):45-60
  19. Roff HM. Advancing human security through artificial intelligence. Chatham House; 2017 May.
  20. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y. Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology. 2017 Dec 1;2(4).
  21. Han K, Cao P, Wang Y, et al. A review of approaches for predicting drug–drug interactions based on machine learning. Front Pharmacol. 2022 Jan 28;12:3966.
  22. Mei S, Zhang K. A machine learning framework for predicting drug–drug interactions. Sci Rep. 2021 Sep 2;11(1):17619
  23. Sun TQ, Medaglia R. Mapping the challenges of artificial intelligence in the public sector: evidence from public healthcare. Gov Inf Q. 2019 Apr 1;36(2):368–383.
  24. Zauderer, M. G., Gucalp, A., Epstein, A. S., Seidman, A. D., Caroline, A., Granovsky, S., Fu, J., Keesing, J., Lewis, S., Co, H., Petri, J., Megerian, M., Eggebraaten, T., Bach, P., Kris, M. G. 2014. Piloting IBM Watson Oncology within Memorial Sloan Kettering’s regional network. Journal of Clinical Oncology, 32(15_suppl):e17653.
  25. Staines, R. 2020. FDA approves Caption Health’s AIdriven cardiac ultrasound software. Pharmaphorum, Accessed On: 10 Feb 2021
  26.  Agarwal S, Gupta RK, Kumar S. Artificial Intelligence in the Pharmacy Profession. Int. J. Res. Pharm. Sci. 2021;12:2269-79.
  27. Shukla MK, Srivastava H, Gupta N, Yadav R. A Detailed Review On Artificial Intelligence In Pharmacy.
  28. Sharma T, Mankoo A, Sood V. Artificial intelligence in advanced pharmacy. International Journal of Science and Research Archive. 2021;2(1):047-54.
  29. Khan O, Parvez M, Kumari P, Parvez S, Ahmad S. The future of pharmacy: how AI is revolutionizing the industry. Intelligent Pharmacy. 2023 Jun 1;1(1):32-40.
  30. Chalasani SH, Syed J, Ramesh M, Patil V, Kumar TP. Artificial intelligence in the field of pharmacy practice: A literature review. Exploratory Research in Clinical and Social Pharmacy. 2023 Dec 1;12:100346.

Photo
Nutan Yashwant Date
Corresponding author

Department of pharmaceutical quality assurance technique, JSPM College of Pharmacy, Wagholi Pune India 412207

Photo
Shrawani Eknath Rakshe
Co-author

Department of pharmaceutical quality assurance technique, Sinhgad College of Pharmacy Vadgaon, Pune India 412105

Photo
Akshada Sunil Dhamdhere
Co-author

Department of pharmaceutical quality assurance technique, JSPM College of Pharmacy, Wagholi Pune India 412207

Nutan Date , Shrawani Rakshe , Akshada Dhamdhere , A Overview On Role Of Artificial Intelligence In Future Of Pharmacy, Int. J. of Pharm. Sci., 2024, Vol 2, Issue 8, 3895-3899. https://doi.org/10.5281/zenodo.13376732

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