Artificial intelligence in pharmacy

Artificial intelligence

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* Knowledge reasoning
* Planning
* Machine learning
* Natural language processing
* Computer vision
* Robotics
* Artificial general intelligence

Approaches

* Symbolic
* Deep learning
* Bayesian networks
* Evolutionary algorithms

Philosophy

* Ethics
* Existential risk
* Turing test
* Chinese room
* Control problem
* Friendly AI

History

* Timeline
* Progress
* AI winter

Technology

* Applications
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Glossary

* Glossary

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Artificial intelligence (AI) is playing a crucial role in driving the application and research in many fields. In pharmacy, AI helps discover, develop and deliver medications. It can enhance patient care through personalized treatment plans.[1][2] It can also assist with drug safety and dosage recommendations.[3]

## Contents

* 1 Applications
* 1.1 Drug discovery and development
* 1.2 Drug delivery systems
* 1.3 Drug safety
* 1.4 Clinical decision support and personalized medicine
* 1.5 Pharmacy operations and automation
* 1.6 Medication adherence
* 2 Adoption challenges and solutions
* 2.1 Barriers to AI adoption
* 2.2 Ethical and regulatory challenges
* 2.3 Solutions for AI adoption
* 2.4 Future directions
* 3 References

## Applications

### Drug discovery and development

The traditional methods for producing drugs are very complex. It costs around $2.6 billion for a pharmaceutical company to make a drug and it can take as long as 12-14 years.[4] AI algorithms analyze vast datasets with greater speed and accuracy than traditional methods.[5] This has enabled the identification of potential drug candidates, prediction of their interactions, and optimization of formulations.[6] AI-driven analysis and modeling assist researchers in understanding molecular interactions, thus expediting the drug development timeline.[7][8]

Artificial neural networks (ANNs) and generative adversarial networks (GANs) have been particularly useful for drug discovery. These models were used for tasks like virtual screening, structure-activity relationship (SAR) modeling, and de novo molecule generation.[9] For example, peptides designed using AI were far more effective against a large number of multidrug-resistant bacteria. Also, transcriptomic data from human cell lines was used to train deep learning models that were used to classify drugs based on therapeutic properties. These innovations help reduce the time, cost and effort involved in early-stage drug development using traditional methods.[4]

### Drug delivery systems

AI is revolutionizing drug delivery systems. AI techniques like neural networks, principal component analysis, and neuro-fuzzy logic are being used in identifying biological targets for pharmaceuticals, evaluating the pharmacological profiles of potential drugs, and analyzing genetic information.[citation needed] By leveraging AI’s capabilities in data analysis, pattern recognition, and optimization, pharmaceutical researchers and healthcare professionals can enhance drug efficacy, minimize side effects, and improve patient outcomes.[10] Intelligent systems can monitor patient response and adjust doses in real time based on individual physiology, with potential applications in the management of chronic diseases. In the future, this could lead to drugs personalized to an individual, targeted cancer treatments, and edible vaccines.[11][12]

### Drug safety

AI is helping in drug safety by predicting and detecting adverse drug reactions (ADRs). Different techniques like knowledge graphs, logistic regression classifier, and neural networks are used. In a 2023 study, a machine learning (ML) algorithm was developed using the knowledge graph to classify the known causes of adverse reactions.[13] Two studies showed that natural language processing and deep learning models like long short-term memory (LSTM) are better than the traditional methods for detecting opioid misuse and preventing overdoses. To accomplish this, the models analyze both structured data from electronic health records (EHRs) and unstructured sources such as clinical notes or social media.[14]

### Clinical decision support and personalized medicine

AI tools are increasingly used in clinical decision-making. Machine learning systems can be trained on patient datasets to predict individual risk profiles, including possible allergies and drug-drug interactions that can be harmful for the patient. This can save a significant amount of time for doctors, and reduce the probability of error.[2] It helps provide a personalized treatment plan for a person.[13]

### Pharmacy operations and automation

Automating pharmacy operations using AI can improve speed, accuracy, and safety. The adoption of robotic technology at the University of San Francisco (UCSF) Medical Center allowed them to make 350,000 medication doses with 100% accuracy.[4] Robots like TUG help in preparing and transporting the medications and lab samples. AI is also used in inventory management, it can predict the demand for a particular medicine based on certain circumstances, and make sure there is no shortage.[15][14]

### Medication adherence

Monitoring that the correct medication is taken by the patient is a big problem in healthcare. AI can check this with smart pillboxes, RFID tags, ingestible sensors, and video check-ins. Smart pillboxes use sensors to record when they are opened. These tools can be used to get real-time data on the patient's health.[13]

## Adoption challenges and solutions

### Barriers to AI adoption

Despite AI being a potential problem solver in the field of pharmacy, there are barriers to overcome before it goes fully mainstream. More research is needed in different pharmaceutical practices to ensure that they are beneficial to patients. There is a lack of training and knowledge among pharmacists. The research facilities do not have a proper AI infrastructure to support innovation and to build the right facilities for AI adoption, which needs a lot of financial investment.[16] If an AI model is trained on a biased dataset, it can give misleading results which could harm patients.[13]

### Ethical and regulatory challenges

AI adoption also raises a lot of ethical and privacy questions, like security, potential bias, and data privacy.[2] Data breaches could expose sensitive information, and a model trained on a biased dataset could suggest unsuitable (and potentially fatal) treatment plans.[13]

### Solutions for AI adoption

AI-based education and training programs could be started to tackle the problem of lack of training and knowledge of AI. The government could assign more funds to healthcare to encourage more research in the field. Patient data could be encrypted and protected safely, with accountability. To prevent the use of biased datasets, regulatory guidelines or policies could be established.[16] Transparency could be improved so that people using the models know the population on which the data was based and how it was trained.[14]

### Future directions

Experts say that for the future of AI in pharmacy, it should focus on better combination with electronic health records and other technologies to reduce the healthcare costs.[2] There could be a common AI framework to encourage international collaboration to speed up the research and contributions of everyone in the field.[14]

## References

1. ↑ Vora, Lalitkumar K.; Gholap, Amol D.; Jetha, Keshava; Thakur, Raghu Raj Singh; Solanki, Hetvi K.; Chavda, Vivek P. (2023-07-10). "Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design" (in en). Pharmaceutics 15 (7): 1916. doi:10.3390/pharmaceutics15071916. ISSN 1999-4923. PMID 37514102.
2. ↑ 2.0 2.1 2.2 2.3 Khan, Osama; Parvez, Mohd; Kumari, Pratibha; Parvez, Samia; Ahmad, Shadab (2023). "The future of pharmacy: How AI is revolutionizing the industry" (in en). Intelligent Pharmacy 1 (1): 32–40. doi:10.1016/j.ipha.2023.04.008.
3. ↑ Chaudhary, Shivang; Muthudoss, Prakash; Madheswaran, Thiagarajan; Paudel, Amrit; Gaikwad, Vinod (2023), "Artificial intelligence (AI) in drug product designing, development, and manufacturing" (in en), A Handbook of Artificial Intelligence in Drug Delivery (Elsevier): pp. 395–442, doi:10.1016/B978-0-323-89925-3.00015-0, ISBN 978-0-323-89925-3, https://linkinghub.elsevier.com/retrieve/pii/B9780323899253000150, retrieved 2025-04-26
4. ↑ 4.0 4.1 4.2 "View of Artificial Intelligence: The Beginning of a New Era in Pharmacy Profession" (in en-US). Asian Journal of Pharmaceutics 12 (2). 2018. doi:10.22377/ajp.v12i02.2317. https://asiapharmaceutics.info/index.php/ajp/article/view/2317/864. Retrieved 2025-05-03.
5. ↑ "AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems". SN Computer Science 3 (2). 2022-02-10. doi:10.1007/s42979-022-01043-x. PMID 35194580.
6. ↑ "Target Identification and Validation in Drug Development" (in en). 4 November 2022. http://www.technologynetworks.com/drug-discovery/articles/target-identification-and-validation-in-drug-development-366706.
7. ↑ "Computational approaches streamlining drug discovery". Nature 616 (7958): 673–685. April 2023. doi:10.1038/s41586-023-05905-z. PMID 37100941. Bibcode: 2023Natur.616..673S.
8. ↑ "Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery". Chemical Reviews 119 (18): 10520–10594. September 2019. doi:10.1021/acs.chemrev.8b00728. PMID 31294972.
9. ↑ Raza, Muhammad Ahmer; Aziz, Shireen; Noreen, Misbah; Saeed, Amna; Anjum, Irfan; Ahmed, Mudassar; Raza, Shahid Masood (2022). "Artificial Intelligence (AI) in Pharmacy: An Overview of Innovations". Innovations in Pharmacy 13 (2): 13. doi:10.24926/iip.v13i2.4839. ISSN 2155-0417. PMID 36654703.
10. ↑ Vora, Lalitkumar K.; Gholap, Amol D.; Jetha, Keshava; Thakur, Raghu Raj Singh; Solanki, Hetvi K.; Chavda, Vivek P. (2023-07-10). "Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design" (in en). Pharmaceutics 15 (7): 1916. doi:10.3390/pharmaceutics15071916. ISSN 1999-4923. PMID 37514102.
11. ↑ A Handbook of Artificial Intelligence in Drug Delivery. Elsevier. 2023. doi:10.1016/c2020-0-03058-6. ISBN 978-0-323-89925-3.
12. ↑ Hassanzadeh, Parichehr; Atyabi, Fatemeh; Dinarvand, Rassoul (2019-11-01). "The significance of artificial intelligence in drug delivery system design". Advanced Drug Delivery Reviews. Editor's Collection 2019 151-152: 169–190. doi:10.1016/j.addr.2019.05.001. ISSN 0169-409X. PMID 31071378. https://www.sciencedirect.com/science/article/pii/S0169409X19300511.
13. ↑ 13.0 13.1 13.2 13.3 13.4 Chalasani, Sri Harsha; Syed, Jehath; Ramesh, Madhan; Patil, Vikram; Pramod Kumar, T. M. (2023). "Artificial intelligence in the field of pharmacy practice: A literature review". Exploratory Research in Clinical and Social Pharmacy 12. doi:10.1016/j.rcsop.2023.100346. ISSN 2667-2766. PMID 37885437.
14. ↑ 14.0 14.1 14.2 14.3 Wong, Adrian; Wentz, Erin; Palisano, Nicholas; Dirani, Manar; Elsamadisi, Pansy; Qashou, Farah; Celi, Leo; Badawi, Omar et al. (2023). "Role of artificial intelligence in pharmacy practice: A narrative review" (in en). Journal of the American College of Clinical Pharmacy 6 (11): 1237–1250. doi:10.1002/jac5.1856. ISSN 2574-9870. https://accpjournals.onlinelibrary.wiley.com/doi/abs/10.1002/jac5.1856.
15. ↑ Al Meslamani, Ahmad Z. (2023-12-31). "Applications of AI in pharmacy practice: a look at hospital and community settings". Journal of Medical Economics 26 (1): 1081–1084. doi:10.1080/13696998.2023.2249758. ISSN 1369-6998. PMID 37594444.
16. ↑ 16.0 16.1 Jarab, Anan S.; Abu Heshmeh, Shrouq R.; Al Meslamani, Ahmad Z. (2023). "Artificial intelligence (AI) in pharmacy: an overview of innovations". Journal of Medical Economics 26 (1): 1261–1265. doi:10.1080/13696998.2023.2265245. ISSN 1941-837X. PMID 37772743.

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