Artificial Intelligence in the Pharmacy Profession

Shivani Agarwal (1) , Rachit Kumar Gupta (2) , Shivang Kumar (3)
(1) Moradabad Educational Trust Faculty of Pharmacy, Moradabad, Uttar Pradesh-244001, India, India ,
(2) Moradabad Educational Trust Faculty of Pharmacy, Moradabad, Uttar Pradesh-244001, India, India ,
(3) Moradabad Educational Trust Faculty of Pharmacy, Moradabad, Uttar Pradesh-244001, India, India

Abstract

The scientist named ‘John McCarthy' used the term "Artificial Intelligence" for the very first time in 1956. Previously, it is only limited to engineering field, but in the recent years, it is briefly introduced into the other fields like pharma, healthcare, business, public sector etc. This review article presented to help as a short presentation of AI for the doctors and pharmacists. Here we describe the AI in various field of medical care, its advantage as well as disadvantages in pharmacy, and its tools. It is greatly advanced into the decision-making, problem solving and critical thinking and having applications in various fields like business, pharmacy, health care, and engineering as well. Now the robots are using in the various medical procedures as they are more trustworthy for doctors, as they are more advanced in their work, as they can do any task within the short time period and effectively than humans. This is concluded that AI is the new evolving field in every sector, even in pharmacy, and it need more development for updating the current scenario as well as for new researches.

Full text article

Generated from XML file

References

Aksu, B., Paradkar, A., Matas, M. D., Özer, Ö., Güneri, T., York, P. 2013. A quality by design approach using artificial intelligence techniques to control the critical quality attributes of ramipril tablets manufactured by wet granulation. Pharmaceutical Development and Technology, 18(1):236–245.

Atomwise 2015. Atomwise finds first evidence towards new Ebola treatments. Accessed on: 24 Mar 2021.

Bahl, M., Barzilay, R., Yedidia, A. B., Locascio, N. J., Yu, L., Lehman, C. D. 2018. High-Risk Breast Lesions: A Machine Learning Model to Predict Pathologic Upgrade and Reduce Unnecessary Surgical Excision. Radiology, 286(3):810–818.

Bejnordi, E., Veta, B., Diest, M. J. V., Ginneken, P. V., Karssemeijer, B., Litjens, N., Laak, G. V. D., Consortium, J. A. W. M., Hermsen, M., Manson, Q. F., Balkenhol, M., Geessink, O., Stathonikos, N., Dijk, M. C. V., Bult, P., Beca, F., Beck, A. H., Wang, D., Khosla, A., Venâncio, R. 2017. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA, 318(22):2199–2210.

Cattell, J., Chilukuri, S., Levy, M. 2013. How big data can revolutionize pharmaceutical R&D. McKinsey Center for Government, Pp: 9, Accessed on: 25 May 2021.

Chen, I. Y., Szolovits, P., Ghassemi, M. 2019. Can AI Help Reduce Disparities in General Medical and Mental Health Care? AMA Journal of Ethics, 21(2):167–179.

Chen, M., Decary, M. 2020. Artificial intelligence in healthcare: An essential guide for health leaders. Healthcare Management Forum, 33(1):10–18.

Chen, W., Desai, D., Good, D., Crison, J., Timmins, P., Paruchuri, S., Wang, J., Ha, K. 2016. Mathematical Model-Based Accelerated Development of Extended-release Metformin Hydrochloride Tablet Formulation. AAPS PharmSciTech, 17(4):1007–1013.

Choi, E., Schuetz, A., Stewart, W. F., Sun, J. 2017. Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association: JAMIA, 24(2):361–370.

Faggella, D. 2019a. 7 Applications of Machine Learning in Pharma and Medicine. Emerj (The AI Research and Advisory company), Accessed on: 25 May 2021.

Faggella, D. 2019b. What is artificial intelligence? An informed definition. Emerj (The AI Research and Advisory company), Accessed on: 25 May 2021.

Fogel, D. B. 2018. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review. Contemporary Clinical Trials Communications, 11:156–164.

Fu, J., Yan, H. 2012. Controlled drug release by a nanorobot. Nature Biotechnology, 30(5):407–408.

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., Kim, R., Raman, R., Nelson, P. C., Mega, J. L., Webster, D. R. 2016. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 316(22):2402–2410.

IBM 2021. Supporting Cancer Research and Treatment, Cancer Research at IBM. IBM Watson Health, Accessed on: 25 May 2021.

Kalafatis, S. P. 2000. Positioning strategies in business markets. Journal Business Industrial Marketing, 15(6):416–437.

McKinsey and Company 2018. AI adoption advances, but foundational barriers remain. Survey Report, Pp: 11, Accessed on: 25 May 2021.

Meskó, B., Görög, M. 2020. A short guide for medical professionals in the era of artificial intelligence. Npj Digital Medicine, 3(1):126(1)–126(8).

Milgrom, P. R., Tadelis, S. 2018. How Artificial Intelligence and Machine Learning Can Impact Market Design. National Bureau of Economic Research, page 26.

Mintz, Y., Brodie, R. 2019. Introduction to artificial intelligence in medicine. Minimally Invasive Therapy and Allied Technologies: MITAT: Official Journal of the Society for Minimally Invasive Therapy, 28:73–81.

NHS 2016. Moorfields announces research partnership. Moorfields Eye Hospital NHS Foundation Trust, Accessed on: 03 May 2021.

Patient Care 2011. New UCSF Robotic Pharmacy Aims to Improve Patient Safety. University of California San Fransisco, Accessed on: 07 Mar 2021.

Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., Tekade, R. K. 2021. Artificial intelligence in drug discovery and development. Drug Discovery Today, 26(1):80–93.

Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., Mcconnell, M. V., Corrado, G. S., Peng, L., Webster, D. R. 2018. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering, 2(3):158–164.

Sacha, G. M., Varona, P. 2013. Artificial intelligence in nanotechnology. Nanotechnology, 24(45):452002.

Sellwood, M. A. 2018. Artificial intelligence in drug discovery. Future Science, 10(17):2025–2028.

Slomka, P. J., Dey, D., Sitek, A. 2017. Cardiac imaging: working towards fully-automated machine analysis and interpretation. Expert Rev Med Devices, 14(3):197–212.

Somashekhar, S. P., Sepúlveda, M. J., Puglielli, S., Norden, A. D., Shortliffe, E. H., Kumar, C., Rauthan, A., Kumar, N., Patil, P., Rhee, K., Ramya, Y. 2018. Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Annals of Oncology, 29(2):418–423.

Staines, R. 2020. FDA approves Caption Health’s AI-driven cardiac ultrasound software. Pharmaphorum, Accessed On: 10 Feb 2021.

TUG 2018. Smart Autonomous Mobile Robot, TUG robots. Aethon [Automating Intralogistics], Accessed on: 25 May 2021.

Vyas, M., Thakur, S., Riyaz, B., Bansal, K. K., Tomar, B., Mishra, V. 2018. Artificial intelligence: The beginning of a new era in pharmacy profession. Asian Journal of Pharmaceutics, 12:72–76.

WHO 2016. Global Report on Diabetes. Technical document, World Health Organization, Accessed on: 21 Apr 2021.

Yu, K. H., Zhang, C., Berry, G. J., Altman, R. B., Ré, C., Rubin, D. L., Snyder, M. 2016. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nature Communications, 7:12474.

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 Oncol ogy, 32(15_suppl):e17653.

Zhao, C., Jain, A., Hailemariam, L., Suresh, P., Akkisetty, P., Joglekar, G., Venkatasubramanian, V., Reklaitis, G. V., Morris, K., Basu, P. 2006. Toward intelligent decision support for pharmaceutical product development. Journal of Pharmaceutical Innovation, 1(1):23–35.

Authors

Shivani Agarwal
shivaniagarwalmbd2013@gmail.com (Primary Contact)
Rachit Kumar Gupta
Shivang Kumar
Shivani Agarwal, Rachit Kumar Gupta, & Shivang Kumar. (2021). Artificial Intelligence in the Pharmacy Profession. International Journal of Research in Pharmaceutical Sciences, 12(3), 2269–2279. Retrieved from https://ijrps.com/home/article/view/321

Article Details

No Related Submission Found