Artificial Intelligence Applications Used in Pharmacy and Pharmacy Related Fields

Yıl 2021, Cilt: 1 Sayı: 2, 34 - 42, 18.08.2021

Beşir Sefa Mumay , Ömrüm Ergüven

Öz

Changing occupational conditions with the development of technology required new definitions for each profession. Pharmacy is a field in which multidisciplinary studies that include many different fields are possible. The pharmacy profession, which was defined as a profession separate from medicine for the first time in 1960, has undergone changes required by the age in every period after 1960. Examples such as pharmacy laboratories where magistral medicines are prepared, the increase in the number of officinal drugs, the drug tracking system and the integration of the medulla system into pharmacies can be interpreted as a result of the pharmacy service's adaptation to the era. Nowadays, considering the necessity of change brought by technology, no pharmaceutical service can be considered independent of technology. Artificial intelligence algorithms can be used in every field of pharmacy. Artificial intelligence applications used in rational drug use, hospital pharmacy, community pharmacy, drug development and production. The aim of this study is; To evaluate the pharmacist's responsibilities and pharmacy practices in general in the light of current data and literature; The aim of this course is to explain artificial intelligence and the properties of artificial intelligence and present examples of artificial intelligence applications used in pharmacy.

Anahtar Kelimeler

Pharmaceutical Science, Rational drug use, Pharmaceutical production, Artificial intelligence, Pharmacist

Eczacılık ve Eczacılık ile İlgili Alanlarda Kullanılan Yapay Zekâ Uygulamaları

Öz

Teknolojinin gelişmesi ile birlikte değişen meslek şartları, her meslek için yeni tanımlamalar gerektirmiştir. Eczacılık, birçok farklı alanı barındıran multidisipliner çalışmaların mümkün olduğu bir alandır. İlk defa 1960'ta tıptan ayrı bir meslek olarak tanımlanan eczacılık mesleği, 1960'tan sonraki her dönemde, çağın gerektirdiği değişikliklere uğramıştır. Majistral ilaçların hazırlandığı eczane laboratuvarları, müstahzar ilaç sayısının artması, ilaç takip sistemi ve Medula sisteminin eczanelere entegre edilmesi gibi örnekler, eczacılık hizmetinin bulunduğu çağa uymasının bir sonucu olarak yorumlanabilir. Günümüzde, teknolojinin getirdiği değişim zorunluluğu da göz önüne alınınca, hiçbir eczacılık hizmeti teknolojiden bağımsız düşünülememektedir. Eczacılığın neredeyse her alanında yapay zekâ algoritmaları kullanılabilir. Akılcı ilaç kullanımında, hastane eczanesinde, serbest eczanelerde, ilaç geliştirilmesinde ve üretilmesinde kullanılan yapay zekâ uygulamaları bilinmektedir. Bu çalışmanın amacı; güncel veriler ve literatür ışığında, eczacının sorumluluklarını ve eczacılık uygulamalarını genel olarak değerlendirmek; yapay zekâyı ve yapay zekânın özelliklerini açıklayarak eczacılık alanında kullanılan yapay zekâ uygulamalarına örnekler sunmaktır.

Anahtar Kelimeler

Eczacılık, Akılcı ilaç kullanımı, İlaç üretimi, Yapay zekâ, Eczacı

Kaynakça

• 1-Donepudi PK. AI and Machine Learning in Retail Pharmacy: Systematic Review of Related Literature. ABC Journal of Advanced Research. 2018 Nov;7(2):109-112. doi: 10.18034/abcjar.v7i2.514
• 2-Rio-Bermudez CD, Medrano IH, Yebes L, Poveda JL. Towards A Symbiotic Relationship Between Big Data, Artificial Intelligence, and Hospital Pharmacy. J of Pharm Policy and Pract. 2020 Nov; 13:75. doi: 10.1186/s40545-020-00276-6
• 3-Nelson SD, Walsh CG, Olsen CA, McLaughlin AJ, LeGrand JR, Schutz N, Lasko TA. Demystifying artificial intelligence in pharmacy. American Journal of Health-System Pharmacy. 2020 July;77(19):1556-1570. doi: 10.1093/ajhp/ zxaa218
• 4-Türkiye İlaç ve Tıbbi Cihaz Kurumu. Eczacılar ve Eczaneler Hakkında Yönetmelik [intenet]. Turkey, 28970 Sayılı T.C. Resmi Gazetesi; 2014 Apr [cited 2020 Dec 12]. Available from: https://www.resmigazete.gov.tr/eskiler/2014/04/ 20140412-14.htm
• 5-Toklu HZ, Akıcı A, Keyer Uysal M, Dülger G. Akılcı ilaç kullanımı sürecinde hasta uyuncuna hekim ve eczacının katkısı. Türkiye Aile Hekimliği Dergisi. 2010 June;14(3):139-145. doi: 10.2399/tahd.10.139
• 6-World Health Organization (WHO). New tool to enhance role of pharmacists in health care [internet]. Switzerland, WHO; 2006 Nov [cited 2020 Dec 12]. Available from:
• https://www.who.int/mediacentre/news/new/2006/nw05/en/
• #:~:text=The%20role%20of%20the%20pharmacist,of%20rese archer%20has%20been%20added.
• 7-Oxford Insights, Government Artificial Intelli-gence Readiness Index [internet], England, Oxford Insights and the International Development Research Centre; 2019 Nov [cited 2021 Jan 11]. Available from: https://www. oxfordinsights.com/ ai-readiness2019
• 8-Turing AM, 2009. Computing Machinery and Intelligence, In: Parsing the Turing Test, Ed; Epstein R, Roberts G, Beber G, First edition, Springer, Dordrecht, Netherlands, pp; 23-65. doi: 10.1007/978-1-4020-6710-5 3
• 9- Atav A. İlaçların diğer ilaçlar ile etkileşimlerinin uzman sistem ile belirlenmesi [master thesis]. [İstanbul (Turkey)]: Maltepe University, 2020.
• 10- Kliegr T, Bahnik S, Fürnkranz J. A review of possible effects of cognitive biases on interpretation of rulebased machine learning models. Artificial Intelligence. 2021 Jan;295:103458. doi: 10.1016/j.artint.2021.103458
• 11- Shrestha YR, Ben-Menahem SM, von Krogh G. Organizational Decision-Making Structures in the Age of Artificial Intelligence. California Management Review. 2019 July;61(4):66-83. doi: 10.1177/0008125619862257
• 12- Dimiduk DM, Holm EA, Niezgoda SR. Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering. Integr Mater Manuf Innov. 2018 Aug;7:157-172. doi: 10.1007/s40192-018-0117-8
• 13- Turban E, Aronson JE, Liang TP. Decision Support System And Intelligent System, 7th ed., Prentice Hall Inc, New
• Jersey, 2005. p.300-357.
• 14- Turban E. Decision Support and Expert Systems Management Support Systems, 4th ed., Prentice Hall Inc, New Jersey, 1995, p435-675.
• 15- Liao SH. Expert system methodologies and applications a decade review from 1995 to 2004. Expert Systems with Applications. 2005 Jan;28(1):93-103. doi: 10.1016/j.eswa.2004.08.003
• 16- Kobayashi VB, Mol ST, Berkers HA, Kismihók G, Den Hartog DN. Text Mining in Organizational Research. Organizational Research Methods. 2018 Aug;21(3):733-765. doi: 10.1177/1094428117722619
• 17- Sevli O, Başer VG. Covid-19 Salgınına Yönelik Zaman Serisi Verileri ile Prophet Model Kullanarak Makine Öğrenmesi Temelli Vaka Tahminlemesi. Avrupa Bilim ve Teknoloji Dergisi. 2020 Aug; 19:827-835. doi: 10.31590/ejosat.766623
• 18- Pesapane F, Volonté C, Codari M, Sardanelli F. Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States. Insights into imaging. 2018 June;9(5):745-753. doi: 10.1007/s13244- 018-0645-y
• 19- Lipmann RP. An Introduction to Computing with Neural Nets. IEEE ASSP Magazine. 1987 Apr;4(2):4-22. doi: 10.1109/MASSP.1987.1165576.
• 20- Bre F, Gimenez JM, Fachinotti VD. Prediction of wind pressure coefficients on building surfaces using artificial neural networks. Energy and Buildings. 2018 Jan; 158:1429-1441. doi:
• 10.1016/j.enbuild.2017.11.045 21- Hardalaç F, Kutbay U. İlaç İlaç Etkileşimlerinin Jordan Elman Ağları Kullanılarak Sınıflandırılması. Journal of the
• Faculty of Engineering and Architecture of Gazi University, 2014 Mar;29(1):149-154. doi: 10.17341/gummfd.87747
• 22- Pharmaino. About Pharmaino [internet]. Turkey, Pharmaino Science; 2020 Nov [cited 2021 Jan 07]. Available from: www.pharmaino.com
• 23- AiCure Company. About AiCure [internet]. USA, AiCure LLC; 2019 Oct [cited 2021 Jan 03]. Available from: www.aicure.com/company
• 24- Labovitz DL, Shafner L, Reyes Gil M, Virmani D, Hanina A. Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy. Stroke. 2017 Apr;48(5):1416-1419. doi: 10.1161/STROKEAHА. 116.016281
• 25- Özgüven Öztornacı B, Başbakkal ZD. İlaç hatalarının
• önlenmesinde yeni dizayn edilmiş karar destek sistemi örneği:
• web tabanlı ilaç uygulama ve doz hesaplama programı [internet].
• Turkey, Uluslararası Sağlıkta Yapay Zekâ Kongresi Bildiri Kitabı; 2020 Jan [cited 2021 Jan 11]. Available from: sagliktayapayzeka2020.org
• 26- Aydın M, Koyuncuoğlu CZ, Kılboz MM, Akıcı A. Diş Hekimliğinde Akılcı Antibiyotik Kullanımı. Turkiye Klinikleri J Dental Sci. 2017 Aug;23(1):33-47. doi: 10.5336/dentalsci.2015- 47189
• 27- Yesil Science, About Yesil Science [internet]. Turkey, Yesil Science A.Ş.; 2020 Feb [cited 2021 Jan 07]. Available from: https://www.yesilscience.com
• 28- Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, MacNair CR, French S, Carfrae LA, Bloom- Ackermann Z, Tran VM, Chiappino-Pepe A, Badran AH, Andrews LW, Chory EJ, Church GM, Brown ED, Jaakkola TS Barzilay R, Collins JJ. A Deep Learning Approach to Antibiotic Discovery. Cell. 2020 Feb;180(4):688-702. doi: 10.1016/j.cell. 2020.01.021
• 29- Abdulla A, Wang B, Qian F, Kee T, Blasiak A, Ong YH, Hooi L, Parekh F, Soriano R, Olinger GG, Keppo J, Hardesty CL, Chow EK, Ho D, Ding X. Project IDentif.AI: Harnessing Artificial Intelligence to Rapidly Optimize Combination Therapy Development for Infectious Disease Intervention. Advenced Therapeutics. 2020 Apr;3:2000034. doi: 10.1002/adtp.202000034
• 30- Vyas M, Thakur S, Riyaz B, Bansal KK, Tomar B, Mishra V. Artificial Intelligence: The Beginning of a New Era in Pharmacy Profession. Asian Journal of Pharmaceutics. 2018 June; 12(2):72-76. doi: 10.22377/AJP.V12102.2317
• 31- Fleming N. How artificial intelligence is changing drug discovery. Nature. 2018 May;557(7707):55-57. doi: 10.1038/d41586-018-05267-x.
• 32- Kannan S, Subbara K, Ali S, Kannan H. The Role of Artificial Intelligence and Machine Learning Techniques: Race for COVID-19 Vaccine. Arch Clin Infect Dis. 2020 April; 15(2):103232. doi: 10.5812/archcid.103232.
• 33- IBM. Artificial intelligence in medicine [internet]. USA, IBM Watson Health; 2020 Oct [cited 2021 Jan 28].
• Available from: https://www.ibm.com/watson- health/learn/artificial-intelligence-medicine
• 34- Khatib MME, Ahmed G. Robotic pharmacies potential and limitations of artificial intelligence: a case study. International Journal of Business Innovation and Research. 2020 Oct;23(3):298-312. doi: 10.1504/IJBIR.2020.110972
• 35- Farrier CE, Pearson JD, Beran TN. Children's fear and pain during medical procedures: A quality improvement study with a humanoid robot. Canadian Journal of Nursing Research. 2019 July; 1-7. doi: 10.1177/0844562119862742
• 36- Shekhar SS. Artificial Intelligence in Automation. International Journal of Multidisciplinary. 2019 June;4(6):14- 17. doi: 10.5281/zenodo.3247197 37- Stafford RQ, MacDonald BA, Jayawardena C, Wegner DM, Broadbent E. Does the robot have a mind? Mind perception
• and attitudes towards robots predict use of an eldercare robot. International journal of social robotics. 2014 Jan;6(1):17-32. doi: 10.1007/s12369-013-0186-y
• 38- Zhou F, Wang X, Goh M. Fuzzy extended VIKOR- based mobile robot selection model for hospital pharmacy. International Journal of Advanced Robotic Systems. 2018 Aug;15(4):1729881418787315. doi: 10.1177/172988141 8787315
• 39- Summerfield MR, Seagull FJ, Vaidya N, Xiao Y. Use of pharmacy delivery robots in intensive care units. American Journal of Health-System Pharmacy. 2011 Jan;68(1):77-83. doi: 10.2146/ajhp100012
• 40- Hayran O. Yeni Tıp Teknolojilerinin Kullanımı ve Etik Sorunlar. Journal of Biotechnology and Strategic Health Research. 2019 Aug;3(2):54-60. doi: 10.34084/bshr.539032
• 41- Wirtz BW, Weyerer JC, Geyer C. Artificial Intelligence and the Public Sector Applications and Challenges. International Journal of Public Administration. 2018 July;42(7):596-615. doi: 10.1080/01900692.2018.1498103