Authors: Milan Rydrych Authors‘ workplace: Mladí lékárníci, z. s., Praha Published in: Čes. slov. Farm., 2026; 75, 24-29 Category: Social Pharmacy doi: https://doi.org/10.36290/csf.2026.005
Introduction: Paper presents the principles of modern artificial intelligence (AI), its current capabilities, limitations, and practical applications in pharmacy. The text outlines the development of AI, explains key terms, and introduces the most important models and their capabilities.
Objective: The objective of the article is to show how AI can support pharmaceutical practice, where its limits lie, and what legislative and safety requirements must be respected when using it.
Results: Modern AI models enable text generation, data processing, image analysis, and decision support. In pharmacy, they can speed up data analysis (e.g., LEK-13 reports), facilitate information retrieval, and support the supervision of IPLP preparation. Examples confirm the high efficiency of the models (e.g., GPT-5 in CSV processing), but also reveal the risk of errors and hallucinations. Model performance varies—rankings (e.g., Chatbot Arena) show specific differences between GPT, Gemini, Claude, and open models. Training a computer vision model for capsule counting is feasible on a standard PC and is capable of very high accuracy.
Conclusion: AI is an important tool for pharmaceutical practice, but it does not replace the expertise of a pharmacist. It is necessary to understand its principles, limitations, ethical and legislative frameworks (AI Act, NIS2) and always check the outputs. When used correctly, it can significantly increase the efficiency, accuracy, and safety of pharmaceutical processes.
artificial intelligence – machine learning – Computer vision – Pharmaceutical Care
1. Tokenizer – OpenAI Platform. [cited 2025 Dec 13]. Available from: https://platform.openai.com/tokenizer.
2. Epstein R, et al. Parsing the Turing Test (Philosophical and Methodological Issues in the Quest for the Thinking Computer). Dordrecht: Springer Netherlands; 2008. https://doi.org/10.1007/978-1-4020-6710-5
3. Artificial Intelligence (AI) Coined at Dartmouth [Internet]. [cited 2025 Dec 13]. Available from: https://home.dartmouth.edu/about/artificial-intelligence-ai-coined-dartmouth
4. Russell S, Norvig P. Artificial Intelligence (A Modern Approach, Third edition, Global edition, with Ernest Davis a Douglas Edwards). Harlow: Pearson Education Limited; 2016.
5. Vaswani A, et al. Attention Is All You Need. Attention Is All You Need. 31st Conference on Neural Information Processing Systems; 2017
6. Walsh S, Timeline Of ChatGPT Updates & Key Events; 2025 [cited 2025 Dec 13]. Available from: https://www.searchenginejournal.com/history-of-chatgpt-timeline/488370/
7. Kucharavy A. Overview of Existing LLM Families (Threats, Exposure and Mitigation). Large Language Models in Cybersecurity. Springer Nature Switzerland; 2024.
8. NÚKIB vydal Varování před některými produkty společnosti DeepSeek. Národní úřad pro kybernetickou a informační bezpečnost; 2025 [cited 2025 Dec 13]. Available from: https://nukib.gov.cz/cs/infoservis/aktuality/2279-nukib-vydal-varovani-pred-nekterymi-produkty-spolecnosti-deepseek.
9. Liu Z, et al. am-ELO: A Stable Framework for Arena-based LLM Evaluation; 2025 [cited 2025 Dec 13]. Available from: https://proceedings.mlr.press/v267/liu25ak.html
10. Lianmin Z, et al. Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena; 2023 [cited 2025 Dec 13]. Available from: https://doi.org/10.48550/ARXIV.2306.05685
11. Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act) (Text with EEA relevance), No. 2024/1689; 2024 [cited 2025 Dec 13]. Available from: https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng
12. Shaping Europe’s digital future: AI Act; 2024 [cited 2025 Dec 13]. Available from: https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
13. Národní centrum elektronického zdravotnictví: Čato kladené otázky; 2025 [cited 2025 Dec 13]. Available from: https://ncez.mzcr.cz/cs/ai-inovativni-digitalni-technologie-ve-zdravotnictvi/casto-kladene-otazky-faq
14. Rydrych M. Doplňující materiály; 2025 [cited 2025 Dec 13]. Available from: https://github.com/Lyman911/AI
15. Blog – YOLOv8; 2024 [cited 2026 Feb 7]. Available from: https://yolov8.org/category/blog/
Don‘t have an account? Create new account
Enter the email address that you registered with. We will send you instructions on how to set a new password.
