COMPARATIVE EVALUATION OF ARTIFICIAL INTELLIGENCE AND DRUG INTERACTION TOOLS: A PERSPECTIVE WITH THE EXAMPLE OF CLOPIDOGREL

Zinnet Şevval Aksoyalp; Betül Rabia Erdoğan

doi:10.33483/jfpau.1460173

Research Article

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COMPARATIVE EVALUATION OF ARTIFICIAL INTELLIGENCE AND DRUG INTERACTION TOOLS: A PERSPECTIVE WITH THE EXAMPLE OF CLOPIDOGREL

Year 2024, Volume: 48 Issue: 3, 1011 - 1020, 10.09.2024

Zinnet Şevval Aksoyalp , Betül Rabia Erdoğan

https://doi.org/10.33483/jfpau.1460173

Abstract

Objective: The study aims to compare the ability of free artificial intelligence (AI) chatbots to detect drug interactions with freely available drug interaction tools, using clopidogrel as an example.
Material and Method: The Lexicomp database was used as a reference to determine drug interactions with clopidogrel. ChatGPT-3.5 AI and Bing AI were selected as the free AI chatbots. Medscape Drug Interaction Checker, DrugBank Drug Interaction Checker and Epocrates Interaction Check were selected as free drug interaction tools. Accuracy score and comprehensiveness score were calculated for each drug interaction tool and AI chatbots. The kappa coefficient was calculated to assess inter-source agreement for interaction severity.
Result and Discussion: The results most similar to those of Lexicomp were obtained from the DrugBank and the ChatGPT-3.5 AI chatbot. The ChatGPT-3.5 AI chatbot performed best, with 69 correct results and an accuracy score of 307. ChatGPT-3.5 AI has the highest overall score of 387 points for accuracy and comprehensiveness. In addition, the highest kappa coefficient with Lexicomp was found for ChatGPT-3.5 AI chatbot (0.201, fair agreement). However, some of the results obtained by ChatGPT-3.5 AI need to be improved as they are incorrect/inadequate. Therefore, information obtained using AI tools should not be used as a reference for clinical applications by healthcare professionals and patients should not change their treatment without consulting doctor.

Keywords

Artificial intelligence, clopidogrel, drug interactions, patient safety, pharmaceutical databases

References

1. Bates, E.R., Lau, W.C., Angiolillo, D.J. (2011). Clopidogrel-drug interactions. Journal of the American College of Cardiology, 57(11), 1251-1263. [CrossRef]
2. Kazui, M., Nishiya, Y., Ishizuka, T., Hagihara, K., Farid, N.A., Okazaki, O., Ikeda, T., Kurihara, A. (2010). Identification of the human cytochrome P450 enzymes involved in the two oxidative steps in the bioactivation of clopidogrel to its pharmacologically active metabolite. Drug Metabolism & Disposition, 38(1), 92-99. [CrossRef]
3. Wang, Z.Y., Chen, M., Zhu, L.L., Yu, L.S., Zeng, S., Xiang, M.X., Zhou, Q. (2015). Pharmacokinetic drug interactions with clopidogrel: Updated review and risk management in combination therapy. Therapeutics and Clinical Risk Management, 11, 449-467. [CrossRef]
4. Lee, C.H., Franchi, F., Angiolillo, D.J. (2020). Clopidogrel drug interactions: A review of the evidence and clinical implications. Expert Opinion on Drug Metabolism & Toxicology, 16(11), 1079-1096. [CrossRef]
5. Agergaard, K., Mau-Sorensen, M., Stage, T.B., Jorgensen, T.L., Hassel, R.E., Steffensen, K.D., Pedersen, J.W., Milo, M., Poulsen, S.H., Pottegard, A., Hallas, J., Brosen, K., Bergmann, T.K. (2017). Clopidogrel-Paclitaxel drug-drug interaction: A pharmacoepidemiologic study. Clinical Pharmacology & Therapeutics, 102(3), 547-553. [CrossRef]
6. Bykov, K., Schneeweiss, S., Donneyong, M.M., Dong, Y.H., Choudhry, N.K., Gagne, J.J. (2017). Impact of an interaction between clopidogrel and selective serotonin reuptake inhibitors. American Journal of Cardiology, 119(4), 651-657. [CrossRef]
7. Cressman, A.M., Macdonald, E.M., Fernandes, K.A., Gomes, T., Paterson, J.M., Mamdani, M.M., Juurlink, D.N. (2015). A population-based study of the drug interaction between clopidogrel and angiotensin converting enzyme inhibitors. British Journal of Clinical Pharmacology, 80(4), 662-669. [CrossRef]
8. Leonard, C.E., Zhou, M., Brensinger, C.M., Bilker, W.B., Soprano, S.E., Pham Nguyen, T.P., Nam, Y.H., Cohen, J.B., Hennessy, S. (2019). Clopidogrel Drug interactions and serious bleeding: Generating real-world evidence via automated high-throughput pharmacoepidemiologic screening. Clinical Pharmacology & Therapeutics, 106(5), 1067-1075. [CrossRef]
9. Suzuki, Y., Suzuki, H., Umetsu, R., Uranishi, H., Abe, J., Nishibata, Y., Sekiya, Y., Miyamura, N., Hara, H., Tsuchiya, T., Kinosada, Y., Nakamura, M. (2015). Analysis of the Interaction between clopidogrel, aspirin, and proton pump inhibitors using the FDA adverse event reporting system database. Biological and Pharmaceutical Bulletin, 38(5), 680-686. [CrossRef]
10. Kheshti, R., Aalipour, M., Namazi, S. (2016). A comparison of five common drug-drug interaction software programs regarding accuracy and comprehensiveness. Journal of Research in Pharmacy Practice, 5(4), 257-263.
11. Marcath, L.A., Xi, J., Hoylman, E.K., Kidwell, K.M., Kraft, S.L., Hertz, D.L. (2018). Comparison of nine tools for screening drug-drug interactions of oral oncolytics. Journal of Oncology Practice, 14(6), e368-e374. [CrossRef]
12. Shariff, A., Belagodu Sridhar, S., Abdullah Basha, N.F., Bin Taleth Alshemeil, S.S.H., Ahmed Aljallaf Alzaabi, N.A. (2021). Assessing Consistency of drug-drug interaction-related information across various drug information resources. Cureus, 13(3), e13766. [CrossRef]
13. Al-Ashwal, F.Y., Zawiah, M., Gharaibeh, L., Abu-Farha, R., Bitar, A.N. (2023). Evaluating the sensitivity, specificity, and accuracy of ChatGPT-3.5, ChatGPT-4, Bing AI, and Bard Against conventional drug-drug interactions clinical tools. Drug, Healthcare and Patient Safety, 15, 137-147. [CrossRef]
14. Juhi, A., Pipil, N., Santra, S., Mondal, S., Behera, J.K., Mondal, H. (2023). The Capability of ChatGPT in predicting and explaining common drug-drug interactions. Cureus, 15(3), e36272. [CrossRef]
15. Akyon, S.H., Akyon, F.C., Yilmaz, T.E. (2023). Artificial intelligence-supported web application design and development for reducing polypharmacy side effects and supporting rational drug use in geriatric patients. Frontiers in Medicine, 10, 1029198. [CrossRef]
16. UpToDate. Clopidogrel drug information 2023 [cited 2023]. Available from: https://www.uptodate.com/contents/clopidogrel-drug information?search=clopidogrel&source=panel _search_result&selectedTitle=1~148&usage_type=panel&kp_tab=drug_general&display_rank=1#F153600. Acess date:14.04.2023.
17. Landis, J.R., Koch, G.G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159-174. [CrossRef]
18. Nakagita, K., Wada, K., Terada, Y., Matsuda, S., Terakawa, N., Oita, A., Takada, M. (2018). Effect of fluconazole on the pharmacokinetics of everolimus and tacrolimus in a heart transplant recipient: Case report. International Journal of Clinical Pharmacology Research, 56(6), 270-276. [CrossRef]
19. Zeldin, R.K., Petruschke, R.A. (2004). Pharmacological and therapeutic properties of ritonavir-boosted protease inhibitor therapy in HIV-infected patients. The Journal of Antimicrobial Chemotherapy, 53(1), 4-9. [CrossRef]
20. Niemi, M., Backman, J.T., Fromm, M.F., Neuvonen, P.J., Kivisto, K.T. (2003). Pharmacokinetic interactions with rifampicin : Clinical relevance. Clinical Pharmacokinetics, 42(9), 819-850. [CrossRef]
21. McQuade, B.M., Campbell, A. (2021). Drug prescribing: Drug-drug interactions. FP Essentials, 508, 25-32.
22. Drwiega, E.N., Badowski, M.E., Michienzi, S. (2022). Antiretroviral drug-drug interactions: A comparison of online drug interaction databases. Journal of Clinical Pharmacy and Therapeutics., 47(10), 1720-1724. [CrossRef]
23. Alkhalid, Z.N., Birand, N. (2022). Determination and comparison of potential drug-drug interactions using three different databases in northern cyprus community pharmacies. Nigerian Journal of Clinical Practice, 25(12), 2005-2009. [CrossRef]
24. Hecker, M., Frahm, N., Bachmann, P., Debus, J.L., Haker, M.C., Mashhadiakbar, P., Langhorst, S.E., Baldt, J., Streckenbach, B., Heidler, F., Zettl, U.K. (2022). Screening for severe drug-drug interactions in patients with multiple sclerosis: A comparison of three drug interaction databases. Frontiers in Pharmacology, 13, 946351. [CrossRef]
25. Suriyapakorn, B., Chairat, P., Boonyoprakarn, S., Rojanarattanangkul, P., Pisetcheep, W., Hunsakunachai, N., Vivithanaporn, P., Wongwiwatthananukit, S., Khemawoot, P. (2019). Comparison of potential drug-drug interactions with metabolic syndrome medications detected by two databases. PLoS One, 14(11), e0225239. [CrossRef]
26. Patel, R.I., Beckett, R.D. (2016). Evaluation of resources for analyzing drug interactions. Journal of the Medical Library Association, 104(4), 290-295. [CrossRef]
27. Pehlivanli, A., Eren-Sadioglu, R., Aktar, M., Eyupoglu, S., Sengul, S., Keven, K., Erturk, S., Basgut, B., Ozcelikay, A.T. (2022). Potential drug-drug interactions of immunosuppressants in kidney transplant recipients: Comparison of drug interaction resources. International Journal of Clinical Pharmacy, 44(3), 651-662. [CrossRef]
28. Martins, M.A., Carlos, P.P., Ribeiro, D.D., Nobre, V.A., Cesar, C.C., Rocha, M.O., Ribeiro, A.L. (2011). Warfarin drug interactions: A comparative evaluation of the lists provided by five information sources. European Journal of Clinical Pharmacology, 67(12), 1301-1308. [CrossRef]
29. Monteith, S., Glenn, T. (2019). A comparison of potential psychiatric drug interactions from six drug interaction database programs. Psychiatry Research, 275, 366-372. [CrossRef]
30. Alowais, S.A., Alghamdi, S.S., Alsuhebany, N., Alqahtani, T., Alshaya, A.I., Almohareb, S.N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H.A., Al Yami, M.S., Al Harbi, S., Albekairy, A.M. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Medical Education, 23(1), 689. [CrossRef]
31. Younis, H.A., Eisa, T.A.E., Nasser, M., Sahib, T.M., Noor, A.A., Alyasiri, O.M., Salisu, S., Hayder, I.M., Younis, H.A. (2024). A systematic review and meta-analysis of artificial intelligence tools in medicine and healthcare: applications, considerations, limitations, motivation and challenges. Diagnostics (Basel), 14(1), 109. [CrossRef]
32. Davenport, T., Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98. [CrossRef]

YAPAY ZEKA VE İLAÇ ETKİLEŞİM ARAÇLARININ KARŞILAŞTIRMALI DEĞERLENDİRİLMESİ: KLOPİDOGREL ÖRNEĞİ İLE BİR BAKIŞ AÇISI

Year 2024, Volume: 48 Issue: 3, 1011 - 1020, 10.09.2024

Zinnet Şevval Aksoyalp , Betül Rabia Erdoğan

https://doi.org/10.33483/jfpau.1460173

Abstract

Amaç: Çalışmanın amacı, klopidogrel örneğini kullanarak ücretsiz yapay zekâ (AI) sohbet robotlarının ilaç etkileşimlerini saptama yeteneklerini ücretsiz olarak erişilebilen ilaç etkileşim araçları ile karşılaştırmaktır.
Gereç ve Yöntem: Klopidogrel ile ilaç etkileşimlerini belirlemek için referans veri tabanı olarak Lexicomp kullanılmıştır. Ücretsiz yapay zekâ sohbet robotları olarak ChatGPT-3.5 AI ve Bing AI, ücretsiz ilaç etkileşim araçları olarak ise Medscape Drug Interaction Checker, DrugBank Drug Interaction Checker ve Epocrates Interaction Check seçilmiştir. Her bir ilaç etkileşim aracı ve yapay zekâ sohbet robotu için doğruluk puanı ve kapsamlılık puanı hesaplanmıştır. Etkileşim şiddeti açısından kaynaklar arası uyumu değerlendirmek için kappa katsayısı hesaplanmıştır.
Sonuç ve Tartışma: Lexicomp veri tabanına en benzer sonuçlar Drugbank ve ChatGPT-3.5 AI sohbet robotundan elde edilmiştir. ChatGPT-3.5 AI sohbet robotunun 69 doğru sonuç ve 307 doğruluk puanı ile en yüksek sonuçlara sahip olduğu bulunmuştur. Doğruluk ve kapsamlılık açısından 387 puanla en yüksek toplam puan ChatGPT-3.5 AI sohbet robotu sonuçları ile elde edilmiştir. Ayrıca Lexicomp ile en yüksek kappa skoru (0.201, orta düzey uyum) ChatGPT-3.5 AI chatbot için bulunmuştur. Ancak ChatGPT-3.5 AI ile elde edilen sonuçlardan bazılarının yanlış/yetersiz bulunması nedeniyle iyileştirilmesine ihtiyaç vardır. Sonuç olarak yapay zekâ araçlarından yararlanılarak elde edilen bilgiler sağlık profesyonelleri tarafından klinik uygulamalar için referans olarak kullanılmamalı ve hastalar doktora danışmadan tedavilerini değiştirmemelidir.

Keywords

Hasta güvenliği, ilaç etkileşimleri, ilaç veri tabanları, klopidogrel, yapay zekâ

References

1. Bates, E.R., Lau, W.C., Angiolillo, D.J. (2011). Clopidogrel-drug interactions. Journal of the American College of Cardiology, 57(11), 1251-1263. [CrossRef]
2. Kazui, M., Nishiya, Y., Ishizuka, T., Hagihara, K., Farid, N.A., Okazaki, O., Ikeda, T., Kurihara, A. (2010). Identification of the human cytochrome P450 enzymes involved in the two oxidative steps in the bioactivation of clopidogrel to its pharmacologically active metabolite. Drug Metabolism & Disposition, 38(1), 92-99. [CrossRef]
3. Wang, Z.Y., Chen, M., Zhu, L.L., Yu, L.S., Zeng, S., Xiang, M.X., Zhou, Q. (2015). Pharmacokinetic drug interactions with clopidogrel: Updated review and risk management in combination therapy. Therapeutics and Clinical Risk Management, 11, 449-467. [CrossRef]
4. Lee, C.H., Franchi, F., Angiolillo, D.J. (2020). Clopidogrel drug interactions: A review of the evidence and clinical implications. Expert Opinion on Drug Metabolism & Toxicology, 16(11), 1079-1096. [CrossRef]
5. Agergaard, K., Mau-Sorensen, M., Stage, T.B., Jorgensen, T.L., Hassel, R.E., Steffensen, K.D., Pedersen, J.W., Milo, M., Poulsen, S.H., Pottegard, A., Hallas, J., Brosen, K., Bergmann, T.K. (2017). Clopidogrel-Paclitaxel drug-drug interaction: A pharmacoepidemiologic study. Clinical Pharmacology & Therapeutics, 102(3), 547-553. [CrossRef]
6. Bykov, K., Schneeweiss, S., Donneyong, M.M., Dong, Y.H., Choudhry, N.K., Gagne, J.J. (2017). Impact of an interaction between clopidogrel and selective serotonin reuptake inhibitors. American Journal of Cardiology, 119(4), 651-657. [CrossRef]
7. Cressman, A.M., Macdonald, E.M., Fernandes, K.A., Gomes, T., Paterson, J.M., Mamdani, M.M., Juurlink, D.N. (2015). A population-based study of the drug interaction between clopidogrel and angiotensin converting enzyme inhibitors. British Journal of Clinical Pharmacology, 80(4), 662-669. [CrossRef]
8. Leonard, C.E., Zhou, M., Brensinger, C.M., Bilker, W.B., Soprano, S.E., Pham Nguyen, T.P., Nam, Y.H., Cohen, J.B., Hennessy, S. (2019). Clopidogrel Drug interactions and serious bleeding: Generating real-world evidence via automated high-throughput pharmacoepidemiologic screening. Clinical Pharmacology & Therapeutics, 106(5), 1067-1075. [CrossRef]
9. Suzuki, Y., Suzuki, H., Umetsu, R., Uranishi, H., Abe, J., Nishibata, Y., Sekiya, Y., Miyamura, N., Hara, H., Tsuchiya, T., Kinosada, Y., Nakamura, M. (2015). Analysis of the Interaction between clopidogrel, aspirin, and proton pump inhibitors using the FDA adverse event reporting system database. Biological and Pharmaceutical Bulletin, 38(5), 680-686. [CrossRef]
10. Kheshti, R., Aalipour, M., Namazi, S. (2016). A comparison of five common drug-drug interaction software programs regarding accuracy and comprehensiveness. Journal of Research in Pharmacy Practice, 5(4), 257-263.
11. Marcath, L.A., Xi, J., Hoylman, E.K., Kidwell, K.M., Kraft, S.L., Hertz, D.L. (2018). Comparison of nine tools for screening drug-drug interactions of oral oncolytics. Journal of Oncology Practice, 14(6), e368-e374. [CrossRef]
12. Shariff, A., Belagodu Sridhar, S., Abdullah Basha, N.F., Bin Taleth Alshemeil, S.S.H., Ahmed Aljallaf Alzaabi, N.A. (2021). Assessing Consistency of drug-drug interaction-related information across various drug information resources. Cureus, 13(3), e13766. [CrossRef]
13. Al-Ashwal, F.Y., Zawiah, M., Gharaibeh, L., Abu-Farha, R., Bitar, A.N. (2023). Evaluating the sensitivity, specificity, and accuracy of ChatGPT-3.5, ChatGPT-4, Bing AI, and Bard Against conventional drug-drug interactions clinical tools. Drug, Healthcare and Patient Safety, 15, 137-147. [CrossRef]
14. Juhi, A., Pipil, N., Santra, S., Mondal, S., Behera, J.K., Mondal, H. (2023). The Capability of ChatGPT in predicting and explaining common drug-drug interactions. Cureus, 15(3), e36272. [CrossRef]
15. Akyon, S.H., Akyon, F.C., Yilmaz, T.E. (2023). Artificial intelligence-supported web application design and development for reducing polypharmacy side effects and supporting rational drug use in geriatric patients. Frontiers in Medicine, 10, 1029198. [CrossRef]
16. UpToDate. Clopidogrel drug information 2023 [cited 2023]. Available from: https://www.uptodate.com/contents/clopidogrel-drug information?search=clopidogrel&source=panel _search_result&selectedTitle=1~148&usage_type=panel&kp_tab=drug_general&display_rank=1#F153600. Acess date:14.04.2023.
17. Landis, J.R., Koch, G.G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159-174. [CrossRef]
18. Nakagita, K., Wada, K., Terada, Y., Matsuda, S., Terakawa, N., Oita, A., Takada, M. (2018). Effect of fluconazole on the pharmacokinetics of everolimus and tacrolimus in a heart transplant recipient: Case report. International Journal of Clinical Pharmacology Research, 56(6), 270-276. [CrossRef]
19. Zeldin, R.K., Petruschke, R.A. (2004). Pharmacological and therapeutic properties of ritonavir-boosted protease inhibitor therapy in HIV-infected patients. The Journal of Antimicrobial Chemotherapy, 53(1), 4-9. [CrossRef]
20. Niemi, M., Backman, J.T., Fromm, M.F., Neuvonen, P.J., Kivisto, K.T. (2003). Pharmacokinetic interactions with rifampicin : Clinical relevance. Clinical Pharmacokinetics, 42(9), 819-850. [CrossRef]
21. McQuade, B.M., Campbell, A. (2021). Drug prescribing: Drug-drug interactions. FP Essentials, 508, 25-32.
22. Drwiega, E.N., Badowski, M.E., Michienzi, S. (2022). Antiretroviral drug-drug interactions: A comparison of online drug interaction databases. Journal of Clinical Pharmacy and Therapeutics., 47(10), 1720-1724. [CrossRef]
23. Alkhalid, Z.N., Birand, N. (2022). Determination and comparison of potential drug-drug interactions using three different databases in northern cyprus community pharmacies. Nigerian Journal of Clinical Practice, 25(12), 2005-2009. [CrossRef]
24. Hecker, M., Frahm, N., Bachmann, P., Debus, J.L., Haker, M.C., Mashhadiakbar, P., Langhorst, S.E., Baldt, J., Streckenbach, B., Heidler, F., Zettl, U.K. (2022). Screening for severe drug-drug interactions in patients with multiple sclerosis: A comparison of three drug interaction databases. Frontiers in Pharmacology, 13, 946351. [CrossRef]
25. Suriyapakorn, B., Chairat, P., Boonyoprakarn, S., Rojanarattanangkul, P., Pisetcheep, W., Hunsakunachai, N., Vivithanaporn, P., Wongwiwatthananukit, S., Khemawoot, P. (2019). Comparison of potential drug-drug interactions with metabolic syndrome medications detected by two databases. PLoS One, 14(11), e0225239. [CrossRef]
26. Patel, R.I., Beckett, R.D. (2016). Evaluation of resources for analyzing drug interactions. Journal of the Medical Library Association, 104(4), 290-295. [CrossRef]
27. Pehlivanli, A., Eren-Sadioglu, R., Aktar, M., Eyupoglu, S., Sengul, S., Keven, K., Erturk, S., Basgut, B., Ozcelikay, A.T. (2022). Potential drug-drug interactions of immunosuppressants in kidney transplant recipients: Comparison of drug interaction resources. International Journal of Clinical Pharmacy, 44(3), 651-662. [CrossRef]
28. Martins, M.A., Carlos, P.P., Ribeiro, D.D., Nobre, V.A., Cesar, C.C., Rocha, M.O., Ribeiro, A.L. (2011). Warfarin drug interactions: A comparative evaluation of the lists provided by five information sources. European Journal of Clinical Pharmacology, 67(12), 1301-1308. [CrossRef]
29. Monteith, S., Glenn, T. (2019). A comparison of potential psychiatric drug interactions from six drug interaction database programs. Psychiatry Research, 275, 366-372. [CrossRef]
30. Alowais, S.A., Alghamdi, S.S., Alsuhebany, N., Alqahtani, T., Alshaya, A.I., Almohareb, S.N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H.A., Al Yami, M.S., Al Harbi, S., Albekairy, A.M. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Medical Education, 23(1), 689. [CrossRef]
31. Younis, H.A., Eisa, T.A.E., Nasser, M., Sahib, T.M., Noor, A.A., Alyasiri, O.M., Salisu, S., Hayder, I.M., Younis, H.A. (2024). A systematic review and meta-analysis of artificial intelligence tools in medicine and healthcare: applications, considerations, limitations, motivation and challenges. Diagnostics (Basel), 14(1), 109. [CrossRef]
32. Davenport, T., Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98. [CrossRef]

There are 32 citations in total.

Details

Primary Language	English
Subjects	Clinical Pharmacology and Therapeutics
Journal Section	Research Article
Authors	Zinnet Şevval Aksoyalp 0000-0002-7822-3154 Betül Rabia Erdoğan
Early Pub Date	August 5, 2024
Publication Date	September 10, 2024
Submission Date	March 28, 2024
Acceptance Date	July 17, 2024
Published in Issue	Year 2024 Volume: 48 Issue: 3

Cite

APA	Aksoyalp, Z. Ş., & Erdoğan, B. R. (2024). COMPARATIVE EVALUATION OF ARTIFICIAL INTELLIGENCE AND DRUG INTERACTION TOOLS: A PERSPECTIVE WITH THE EXAMPLE OF CLOPIDOGREL. Journal of Faculty of Pharmacy of Ankara University, 48(3), 1011-1020. https://doi.org/10.33483/jfpau.1460173
AMA	Aksoyalp ZŞ, Erdoğan BR. COMPARATIVE EVALUATION OF ARTIFICIAL INTELLIGENCE AND DRUG INTERACTION TOOLS: A PERSPECTIVE WITH THE EXAMPLE OF CLOPIDOGREL. J. Fac. Pharm. Ankara. September 2024;48(3):1011-1020. doi:10.33483/jfpau.1460173
Chicago	Aksoyalp, Zinnet Şevval, and Betül Rabia Erdoğan. “COMPARATIVE EVALUATION OF ARTIFICIAL INTELLIGENCE AND DRUG INTERACTION TOOLS: A PERSPECTIVE WITH THE EXAMPLE OF CLOPIDOGREL”. Journal of Faculty of Pharmacy of Ankara University 48, no. 3 (September 2024): 1011-20. https://doi.org/10.33483/jfpau.1460173.
EndNote	Aksoyalp ZŞ, Erdoğan BR (September 1, 2024) COMPARATIVE EVALUATION OF ARTIFICIAL INTELLIGENCE AND DRUG INTERACTION TOOLS: A PERSPECTIVE WITH THE EXAMPLE OF CLOPIDOGREL. Journal of Faculty of Pharmacy of Ankara University 48 3 1011–1020.
IEEE	Z. Ş. Aksoyalp and B. R. Erdoğan, “COMPARATIVE EVALUATION OF ARTIFICIAL INTELLIGENCE AND DRUG INTERACTION TOOLS: A PERSPECTIVE WITH THE EXAMPLE OF CLOPIDOGREL”, J. Fac. Pharm. Ankara, vol. 48, no. 3, pp. 1011–1020, 2024, doi: 10.33483/jfpau.1460173.
ISNAD	Aksoyalp, Zinnet Şevval - Erdoğan, Betül Rabia. “COMPARATIVE EVALUATION OF ARTIFICIAL INTELLIGENCE AND DRUG INTERACTION TOOLS: A PERSPECTIVE WITH THE EXAMPLE OF CLOPIDOGREL”. Journal of Faculty of Pharmacy of Ankara University 48/3 (September 2024), 1011-1020. https://doi.org/10.33483/jfpau.1460173.
JAMA	Aksoyalp ZŞ, Erdoğan BR. COMPARATIVE EVALUATION OF ARTIFICIAL INTELLIGENCE AND DRUG INTERACTION TOOLS: A PERSPECTIVE WITH THE EXAMPLE OF CLOPIDOGREL. J. Fac. Pharm. Ankara. 2024;48:1011–1020.
MLA	Aksoyalp, Zinnet Şevval and Betül Rabia Erdoğan. “COMPARATIVE EVALUATION OF ARTIFICIAL INTELLIGENCE AND DRUG INTERACTION TOOLS: A PERSPECTIVE WITH THE EXAMPLE OF CLOPIDOGREL”. Journal of Faculty of Pharmacy of Ankara University, vol. 48, no. 3, 2024, pp. 1011-20, doi:10.33483/jfpau.1460173.
Vancouver	Aksoyalp ZŞ, Erdoğan BR. COMPARATIVE EVALUATION OF ARTIFICIAL INTELLIGENCE AND DRUG INTERACTION TOOLS: A PERSPECTIVE WITH THE EXAMPLE OF CLOPIDOGREL. J. Fac. Pharm. Ankara. 2024;48(3):1011-20.

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The Journal of Faculty of Pharmacy of Ankara University is an international medium, an open access, peer-reviewed journal for the publication of original research reports, reviews and short communications in English or Turkish on relevant developments in pharmaceutical sciences., Proceeding of scientific meetings may be published as special issues of supplements to the journal. Additionally, it contains previous and forthcoming national and international scientific meetings with social activities in pharmaceutical areas.