Archive \ Volume.16 2025 Issue 4

What Work Has Artificial Intelligence Actually Taken on in Pharmacy? A Scoping Review of Dispensing, Clinical Review, and Medicines Information

, ,
  1. Department of Pharmacy AI Scoping and Review, Faculty of Pharmacy, Warsaw University of Life Sciences, Warsaw, Poland.
  2. Department of Clinical Pharmacy and AI Task Analysis, Faculty of Pharmacy, Jagiellonian University, Krakow, Poland.

Abstract

Claims about artificial intelligence in pharmacy frequently combine conceptual proposals, retrospective models, and operational systems, making it difficult to determine what work has actually been assigned to AI and where pharmacist judgment remains necessary. To map AI-supported tasks across dispensing, prescription and clinical review, medicines information, patient-facing services, and administrative pharmacy work while separating technical performance from workflow usefulness, safety, and autonomy. Peer-reviewed Q1 journal articles published from 2017 through 2025 were eligible when they evaluated an AI or hybrid AI component performing or supporting an identifiable pharmacy task and provided extractable evidence on setting, data, reference standard, human involvement, validation, comparator, or outcome. Searches covered MEDLINE/PubMed, Embase, Scopus, Web of Science, CINAHL, International Pharmaceutical Abstracts, IEEE Xplore, and supplementary citation and publisher searching. Evidence was charted by task, AI method, setting, data source, reference standard, pharmacist role, validation level, comparator, outcome, and autonomy, then synthesized using a review-derived task–maturity–oversight framework.

Searches identified 1,266 records. After removal of 472 duplicates, 794 records were screened; 153 reports were sought, 149 full texts were assessed, and 22 studies were included. Evidence concentrated on prescription prioritization, intervention prediction, anomaly detection, pill recognition, targeted patient services, and medicines-information answering. Pharmacists generally retained contextual review, authorization, communication, or final action. AI has primarily taken on bounded tasks that rank, flag, recognize, retrieve, or draft. The evidence does not establish autonomous completion of comprehensive pharmacy work or permit technical accuracy to be treated as proof of safety, benefit, or deployment readiness.


Downloads: 27
Views: 87

How to cite:
Vancouver
Wiśniewski K, Nowak M, Adamczyk T. What Work Has Artificial Intelligence Actually Taken on in Pharmacy? A Scoping Review of Dispensing, Clinical Review, and Medicines Information. Arch Pharm Pract. 2025;16(4):34-42. https://doi.org/10.51847/KoMXgftABY
APA
Wiśniewski, K., Nowak, M., & Adamczyk, T. (2025). What Work Has Artificial Intelligence Actually Taken on in Pharmacy? A Scoping Review of Dispensing, Clinical Review, and Medicines Information. Archives of Pharmacy Practice, 16(4), 34-42. https://doi.org/10.51847/KoMXgftABY

Download Citation
References
  1. Nelson SD, Walsh CG, Olsen CA, McLaughlin AJ, LeGrand JR, Schutz N, et al. Demystifying artificial intelligence in pharmacy. Am J Health Syst Pharm. 2020;77(19):1556-70. doi:10.1093/ajhp/zxaa218
  2. Ranchon F, Chanoine S, Lambert-Lacroix S, Bosson JL, Moreau-Gaudry A, Bedouch P. Development of artificial intelligence powered apps and tools for clinical pharmacy services: A systematic review. Int J Med Inform. 2023;172:104983. doi:10.1016/j.ijmedinf.2022.104983
  3. Hatzimanolis J, Riley B, El-Den S, Aslani P, Zhou J, Chaar BB. Applications of artificial intelligence in current pharmacy practice: A scoping review. Res Social Adm Pharm. 2025;21(3):134-41. doi:10.1016/j.sapharm.2024.12.007
  4. Damiani G, Altamura G, Zedda M, Nurchis MC, Aulino G, Heidar Alizadeh A, et al. Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: A systematic review. BMJ Open. 2023;13(3). doi:10.1136/bmjopen-2022-065301
  5. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Ann Intern Med. 2018;169(7):467-73. doi:10.7326/M18-0850
  6. Peters MDJ, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L, et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evid Synth. 2020;18(10):2119-26. doi:10.11124/JBIES-20-00167
  7. Munn Z, Peters MDJ, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018;18(1):143. doi:10.1186/s12874-018-0611-x
  8. Pollock D, Peters MDJ, Khalil H, McInerney P, Alexander L, Tricco AC, et al. Recommendations for the extraction, analysis, and presentation of results in scoping reviews. JBI Evid Synth. 2023;21(3):520-32. doi:10.11124/JBIES-22-00123
  9. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ. 2021;372. doi:10.1136/bmj.n71
  10. Rethlefsen ML, Kirtley S, Waffenschmidt S, Ayala AP, Moher D, Page MJ, et al. PRISMA-S: An extension to the PRISMA statement for reporting literature searches in systematic reviews. Syst Rev. 2021;10(1):39. doi:10.1186/s13643-020-01542-z
  11. Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension. Lancet Digit Health. 2020;2(10). doi:10.1016/S2589-7500(20)30218-1
  12. Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRIT-AI and CONSORT-AI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI extension. Lancet Digit Health. 2020;2(10). doi:10.1016/S2589-7500(20)30219-3
  13. Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med. 2019;170(1):51-8. doi:10.7326/M18-1376
  14. Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, et al. TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385. doi:10.1136/bmj-2023-078378
  15. Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, et al. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med. 2022;28(5):924-33. doi:10.1038/s41591-022-01772-9
  16. Norgeot B, Quer G, Beaulieu-Jones BK, Torkamani A, Dias R, Gianfrancesco M, et al. Minimum information about clinical artificial intelligence modeling: The MI-CLAIM checklist. Nat Med. 2020;26(9):1320-4. doi:10.1038/s41591-020-1041-y
  17. Corny J, Rajkumar A, Martin O, Dode X, Lajonchère JP, Billuart O, et al. A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error. J Am Med Inform Assoc. 2020;27(11):1688-94. doi:10.1093/jamia/ocaa154
  18. Rozenblum R, Rodriguez-Monguio R, Volk LA, Forsythe KJ, Myers S, McGurrin M, et al. Using a machine learning system to identify and prevent medication prescribing errors: A clinical and cost analysis evaluation. Jt Comm J Qual Patient Saf. 2020;46(1):3-10. doi:10.1016/j.jcjq.2019.09.008
  19. Wong YF, Ng HT, Leung KY, Chan KY, Chan SY, Loy CC. Development of fine-grained pill identification algorithm using deep convolutional network. J Biomed Inform. 2017;74:130-6. doi:10.1016/j.jbi.2017.09.005
  20. Worrall C, Shirley D, Bullard J, Dao A, Morrisette T. Impact of a clinical pharmacist-led, artificial intelligence-supported medication adherence program on medication adherence performance, chronic disease control measures, and cost savings. J Am Pharm Assoc (2003). 2025;65(1):102271. doi:10.1016/j.japh.2024.102271
  21. Balestra M, Chen J, Iturrate E, Aphinyanaphongs Y, Nov O. Predicting inpatient pharmacy order interventions using provider action data. JAMIA Open. 2021;4(3). doi:10.1093/jamiaopen/ooab083
  22. Hogue SC, Chen F, Brassard G, Lebel D, Bussières JF, Durand A, et al. Pharmacists’ perceptions of a machine learning model for the identification of atypical medication orders. J Am Med Inform Assoc. 2021;28(8):1712-8. doi:10.1093/jamia/ocab071
  23. Ashraf AR, Somogyi-Végh A, Merczel S, Gyimesi N, Fittler A. Leveraging code-free deep learning for pill recognition in clinical settings: A multicenter, real-world study of performance across multiple platforms. Artif Intell Med. 2024;150:102844. doi:10.1016/j.artmed.2024.102844
  24. Levivien C, Cavagna P, Grah A, Buronfosse A, Courseau R, Bézie Y, et al. Assessment of a hybrid decision support system using machine learning with artificial intelligence to safely rule out prescriptions from medication review in daily practice. Int J Clin Pharm. 2022;44(2):459-65. doi:10.1007/s11096-021-01366-4
  25. King CR, Abraham J, Fritz BA, Cui Z, Galanter W, Chen Y, et al. Predicting self-intercepted medication ordering errors using machine learning. PLoS One. 2021;16(7). doi:10.1371/journal.pone.0254358
  26. Johns E, Guendouz A, Dal Mas L, Beck M, Alkanj A, Gourieux B, et al. Using machine learning to predict pharmaceutical interventions during medication prescription review in a hospital setting. Am J Health Syst Pharm. 2025;82(22):1238-48. doi:10.1093/ajhp/zxaf089
  27. Wang SV, Rogers JR, Jin Y, DeiCicchi D, Dejene S, Connors JM, et al. Stepped-wedge randomised trial to evaluate population health intervention designed to increase appropriate anticoagulation in patients with atrial fibrillation. BMJ Qual Saf. 2019;28(10):835-42. doi:10.1136/bmjqs-2019-009367
  28. Heo J, Kang Y, Lee S, Jeong DH, Kim KM. An accurate deep learning-based system for automatic pill identification: Model development and validation. J Med Internet Res. 2023;25. doi:10.2196/41043
  29. Huang X, Estau D, Liu X, Yu Y, Qin J, Li Z. Evaluating the performance of ChatGPT in clinical pharmacy: A comparative study of ChatGPT and clinical pharmacists. Br J Clin Pharmacol. 2024;90(1):232-8. doi:10.1111/bcp.15896
  30. Triplett S, Ness-Engle GL, Behnen EM. A comparison of drug information question responses by a drug information center and by ChatGPT. Am J Health Syst Pharm. 2025;82(8):448-60. doi:10.1093/ajhp/zxae316
  31. Andrikyan W, Sametinger SM, Kosfeld F, Jung-Poppe L, Fromm MF, Maas R, et al. Artificial intelligence-powered chatbots in search engines: A cross-sectional study on the quality and risks of drug information for patients. BMJ Qual Saf. 2025;34(2):100-9. doi:10.1136/bmjqs-2024-017476
  32. Morath B, Chiriac U, Jaszkowski E, Deiß C, Nürnberg H, Hörth K, et al. Performance and risks of ChatGPT used in drug information: An exploratory real-world analysis. Eur J Hosp Pharm. 2024;31(6):491-7. doi:10.1136/ejhpharm-2023-003750
  33. Kaas-Hansen BS, Leal Rodríguez C, Placido D, Thorsen-Meyer HC, Nielsen AP, Dérian N, et al. Using machine learning to identify patients at high risk of inappropriate drug dosing in periods with renal dysfunction. Clin Epidemiol. 2022;14:213-23. doi:10.2147/CLEP.S344435
  34. Cho J, Lee AR, Koo D, Kim K, Jeong YM, Lee HY, et al. Development of machine-learning models using pharmacy inquiry database for predicting dose-related inquiries in a tertiary teaching hospital. Int J Med Inform. 2024;185:105398. doi:10.1016/j.ijmedinf.2024.105398
  35. Nagata K, Tsuji T, Suetsugu K, Muraoka K, Watanabe H, Kanaya A, et al. Detection of overdose and underdose prescriptions—An unsupervised machine learning approach. PLoS One. 2021;16(11). doi:10.1371/journal.pone.0260315
  36. Sato H, Kimura Y, Ohba M, Ara Y, Wakabayashi S, Watanabe H. Prediction of prednisolone dose correction using machine learning. J Healthc Inform Res. 2023;7(1):84-103. doi:10.1007/s41666-023-00128-3
  37. Fournier A, Fallet C, Sadeghipour F, Perrottet N. Assessing the applicability and appropriateness of ChatGPT in answering clinical pharmacy questions. Ann Pharm Fr. 2024;82(3):507-13. doi:10.1016/j.pharma.2023.11.001
  38. He N, Yan Y, Wu Z, Cheng Y, Liu F, Li X, et al. Chat GPT-4 significantly surpasses GPT-3.5 in drug information queries. J Telemed Telecare. 2025;31(2):306-8. doi:10.1177/1357633X231181922

 

 


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.