Artificial Intelligence in Community Pharmacy: Bridging Human Touch with Digital Precision

Abstract

The rapidly expanding field of artificial intelligence, which has broad applications in the pharmaceutical industry, is a branch of computer science. The stimulation of human intelligence processes by technology, particularly computer systems, is known as artificial intelligence. The application of artificial intelligence has spread into almost every industry in recent years. Poly-pharmacology, hospital pharmacy, drug delivery formulation development, and drug discovery are just a few of the pharmacy domains where artificial intelligence is crucial. By facilitating quicker medication discovery, enhancing patient outcomes, cutting costs, and raising the accuracy and efficiency of various pharmacy procedures, the paper will make the case that artificial intelligence (AI) has the ability to completely transform the pharmacy sector. We'll also talk about the difficulties and restrictions posed by AI in the pharmacy sector, including issues with data protection, ethics, and regulations. The new AI pharmacy system automates routine procedures, offers individualized treatment plans, lowers costs, and improves patient outcomes while replacing the manual processes and human decision-making that were the foundation of the previous pharmacy system. Pharmacists can evaluate a vast amount of patient data, such as medication profiles, lab results, and medical records, by employing AI algorithms and machine learning. This helps them spot possible drug interactions, evaluate the safety and effectiveness of medications, and provide well-informed recommendations based on the needs of each patient. Different AI models have been created to help clinical decision support systems with medication-related decisions, anticipate and identify adverse drug occurrences, and automate community pharmacy dispensing procedures.

Keywords

Introduction

Dispensing Pharmacy: Automation of medication management procedures, including ordering, dispensing, delivering, and administering drugs, has become a key tactic to enhance the general performance and operations of retail, inpatient, and outpatient pharmacies. Pharmacists can concentrate more on value-added tasks, such as offering individualized counseling regarding successful medication management, discussing possible side effects, and providing extensive counseling on drug usage, thanks to this technique, which also improves operational elements. Additionally, by significantly cutting down on patient wait times, regular task automation allows pharmacies to serve more clients without sacrificing the quality of their services.Patients may be more satisfied when pharmacy workflows are optimized to increase efficiency since it allows customers to obtain timely service and individualized attention. This improvement in satisfaction is important because it leads to better medication adherence and increased patient loyalty. (Basile et al., 2024) The chemist can use AI to forecast the patient's medication purchases and make the best stock selections. Not all retail pharmacies, including Mckessons, Liberty, Win pharm, PrimeRx, and WinRx, use artificial intelligence,  there are now inventory management programs and technologies accessible or artificial intelligence.(Tarle et al., 2023) The use of dispensing robots lowers labor costs, increases productivity, decreases errors, and shields employees from dangerous medications. Because of variations in the packaging and dispensing of medications, these robots perform distinct tasks in different nations.

.AI In Prescription: One of the main problems that consumers or patients still deal with today is prescription errors. These errors may sometimes put patients in potentially fatal circumstances due to different types of drug-drug interactions or other interactions. To do this, it looks at the patients' previous medical records. AI can lessen the negative and hazardous effects of drug prescription errors as well as the prevalence of prescription errors. Knowledge robots using artificial intelligence (AI) can act as a personalized self-service advisor for patients or clients, providing the same kinds of guidance and assistance that the world's leading experts could. These AI bots will send patient information to the outside pharmacy as well as retrieve patient information. Additionally, after reviewing the patient's entire medical history, these AI bots will suggest the optimal medication for the individual, and then they will check and validate the medication that was provided .(Tarle et al., 2023)The process by which a doctor enters and transmits medication and treatment orders, as well as laboratory, admission, radiology, referral, and procedure orders, electronically through a computer application, as opposed to using more conventional methods like paper charts, verbal orders, telephone, and fax, is called Computerized Physician Order Entry (CPOE), also known as Computerized Provider Order Entry or Computerized Practitioner Order Entry. This technique lessens mistakes brought on by handwriting that is difficult to read or transcribing problems in prescription instructions.  These CPOE systems manage the electronic communication of pharmaceutical orders to pharmacies and dispensing pharmacists as well as the selection, presentation, and storage of medication histories. (Chalasani et al., 2023)

E-prescription refers to the process of an electronic device being used by the patient, physician, pharmacist, and health insurance company to submit and exchange prescription information. In most of the e-Prescription systems we examined, the patient's only engagement is to provide the prescriber and pharmacist permission to use the prescription. Using e-Prescription will enable the parties to deliver a safe, effective, and high-quality care service. Additionally, when a doctor and a pharmacist evaluate a prescription before dispensing, e-Prescription systems will serve as the communication channel. (Aldughayfiq & Sampalli, 2021)

Electronic health record : By using vast amounts of EHR data and artificial intelligence (AI) to identify patterns regarding appropriate medication use, a new predictive EHR algorithm can be implemented that can improve clinical decisions by detecting and alerting when a prescribed drug appears to deviate from its pattern of appropriate use. AI may also help with medicine selection by automatically classifying patients who are most likely to have negative side effects from a given medication. AI that has been integrated into EHR systems by the Patient Safety Learning Laboratory (PSLL) can recognize, evaluate, and reduce risks to patient safety.(Chalasani et al., 2023) on the progress of patient treatment and drug management. Given that prescription errors can result in adverse drug events and hospital re-admissions, this is essential to guaranteeing that patients receive safe and effective therapy. AI ?? a patient's prescription regimen. AI may be integrated with electronic health records to resolve any inconsistencies or missing data and give medical professionals real-time updates from their EHR histories. This can lower the possibility of prescription errors and save medical professionals time and effort when manually examining drug lists. (Khan et al., 2023)

AI In Inventory Management: Inventory management software can now become intelligent processes thanks to advancements in artificial intelligence technologies, one of the improvements brought about by technical advancements and the advancements in computer technology. Methods from AI's machine learning (ML) and deep learning (DL) sub-fields are crucial in this regard. Numerous research carried out in the last few years indicate that interest in ML and DL approaches is steadily growing. These techniques increase demand forecasting accuracy while quickly analyzing vast and varied data sets. In addition, inventory management combined with AI approaches is a flexible and effective process that provides more contextual information, faster customer response times, and cheaper operating expenses. (Albayrak Ünal et al., 2023)Billions are spent on robotics for automation in the worldwide automobile sector. However, the cost-effectiveness of the inventory management techniques used today must be maximized. The industry requires assistance in determining the appropriate level of labor expertise for manual handling. Predictive analysis in inventory management using AI and ML is the suggested remedy. Productivity gains and cost reductions may result from this innovation. (Mandala, 2024)

Drug Drug Interaction:AI is able to detect possible drug interactions, including those between drugs and diseases, and notify patients and healthcare professionals of these interactions. The two drugs can be screened for possible drug interactions by AI. Artificial intelligence (AI) can notify the patient and healthcare provider of a possible interaction and offer suggestions for managing it, such changing dosages or recommending a different medicine. AI can also check for possible drug-disease combinations, such a prescription that could exacerbate an existing illness. AI has the ability to notify the doctor and recommend different drugs or dosages. In addition to lowering the chance of adverse drug events like drug toxicity or treatment failure, AI can help improve patient safety by screening for possible drug interactions. (Khan et al., 2023)

IMPORTANCE:

The potential of artificial intelligence (AI) in pharmacy must be understood since it has the ability to completely transform prescription administration, which would eventually improve patient outcomes and lower healthcare costs. In order to help patients better manage their drugs and lower the risk of medication errors, adverse drug reactions, and hospitalizations, AI can offer 24/7 support and individualized medication management. AI also has the ability to lessen the strain for medical professionals, freeing them up to concentrate on more difficult jobs and deliver higher-quality solutions. To ensure its safe and efficient deployment, it is crucial to comprehend the possible drawbacks and difficulties related to the application of AI in the pharmaceutical system. Thus, in order to create evidence-based solutions that optimize AI's advantages and reduce its risks, it is essential to comprehend the technology's potential in pharmacy.(Stasevych & Zvarych, 2023)

The research importance of AI in the pharmacy system may include.

RO-1: To evaluate the viability of integrating AI into the pharmacy system to provide patients with round-the-clock assistance and individualized medication management.

RO-2: To assess how AI may affect patient outcomes, including hospitalizations, adverse drug responses, and general health, as well as medication adherence.

RO-3: To determine any potential ethical ramifications of using AI in the pharmacy system, including data privacy and potential biases.

RO-4: To recognize technological obstacles and create ways to address them, such as data integration and system upkeep.

RO-5: To evaluate AI's usability for patients and pharmacists and pinpoint problems for

RO-6: To assess AI's cost-effectiveness in the pharmacy system and contrast it with more conventional methods of managing medications. (Khan et al., 2023)(Al Meslamani, 2023)

In E-Prescription:

To investigate how a dispensing robot and electronic prescribing are integrated.

To measure the potential savings from utilizing a combined electronic prescription and robot system.

To measure the potential efficiencies offered by an integrated electronic prescribing-robot system .(Beard & Smith, 2013)

In Inventory Management:

• Examine how the global automobile sector contributes to the supply chain.
• Assess trading relations between OEMs and suppliers,
• Investigate a real-world business situation in an outsourced supply chain network and develop an inventory management solution,
• Produce industry-specific guidelines for inventory management,
• Develop a mathematical model for evaluating inventory levels and
• Generate interest in inventory management from academics and automotive professionals. (Mandala, 2024)

There are a number of benefits to the suggested work on AI's application in the pharmacy system. By offering individualized treatment regimens and lowering the possibility of negative drug reactions, it can, first and foremost, enhance patient outcomes. Second, by automating repetitive processes, it can improve pharmaceutical system efficiency and lessen the burden on healthcare providers. In conclusion, it helps create new techniques and instruments for evaluating vast quantities of patient data, which may have wider consequences for the advancement of AI in healthcare.

AI can help pharmacists manage chronic illnesses like heart disease, diabetes, and high blood pressure. By monitoring patient data with AI-powered solutions, pharmacists can spot possible problems early and take prompt action. This can improve patient outcomes and lower healthcare costs by lowering the chance of hospital stays and ER visits.In general, the potential uses of AI in pharmacy can be very advantageous to aspiring pharmacists by increasing the precision and security of medicine delivery, speeding up the drug development process, and helping to treat long-term illnesses. As AI technology develops, we may anticipate even more cutting-edge uses in pharmacy, which will further revolutionize healthcare in the future.(Mandala, 2024)

A variety of techniques, including chat-bots, virtual assistants, robotics, data analytics software, electronic health records, machine learning algorithms, and natural language processing, can help integrate AI into the current pharmacy system. The pharmacy system's accuracy and efficiency can be increased, routine operations can be automated, vast volumes of patient data can be analyzed, and individualized treatment plans may be created with the help of these tools. Healthcare providers can lower expenses, enhance patient outcomes, and adhere to laws by implementing these tools. The application of these technologies also demonstrates how AI has the potential to revolutionize the healthcare sector by increasing its efficacy, accessibility, and efficiency. (Khan et al., 2023)

This review's main goals are to: assess AI's effectiveness in addressing long-standing bottlenecks like delayed adverse event detection and high attrition rates in drug discovery; critically evaluate emerging technologies, such as large language models (LLMs) like BioBERT for biomedical knowledge extraction and graph machine learning (ML) for Poly pharmacology prediction; and draw attention to unresolved ethical and regulatory issues, such as algorithmic transparency and data privacy, that impede the smooth transition of AI advancements into clinically useful solutions. The objective of this review is to present a fair assessment of AI's transformational potential and constraints in transforming pharmaceutical research and development by connecting technological developments with practical application. (Kandhare et al., 2025)(Mandala, 2024)

CONVENTIONAL METHOD:

1. Dispensing:

Dispensing is the act of preparing and delivering medication to a designated individual based on a valid prescription. Accurate preparation and proper interpretation of the prescriber's instructions are essential components of the sensible use of medications.(Beard & Smith, 2013)

A) Processing of Prescription:

(1) Screening: A prescription should be checked and verified upon receipt to make sure it is for the right patient and conforms with all applicable regulations. The prescription should be printed or written in a readable manner.

2. Understanding what the prescription order means: Drug interactions should be checked by the pharmacist who receives the prescription. duplicate medications, polypharmacy, improper medication use, allergies, and contraindications.

(3) Managing prescriptions that need clarification: Efforts should always be made to get in touch with the doctor if a prescription is received that is unclear or needs more explanation.

(B) Preparing the Medicines:

(1) Filling :Prevent medication errors while choosing which drug to dispense by putting in place a suitable mechanism to guarantee that the right medication is chosen, particularly if there are medications with similar names and packaging. Before choosing a medication, read the label at least twice and make sure the name and strength match the prescription.

(2) Labeling: All medications that are dispensed must be labeled in compliance with the legal requirements. It is recommended that labels be printed. If handwritten, it should include clear usage instructions and be tidy and readable. The following information should be included on the label: the name, address, and phone number of the hospital, clinic, or pharmacy; the patient's name; the names of the medications (trade or generic); and the dosage form along with the strength and quantity per unit dosage form, such as mg/ml for liquid preparations or mg/g for semi-solid preparations, among other things.

(3) Checking: Verify that the prescription and the filled medications match.

(C) Counter checking:

A second individual should perform the counter-checking instead of the employee who completed the filling and labeling activity earlier. Verify that every medication that is ready to be dispensed matches the prescription. The individual completing the counter-checking should sign the prescription after finishing it.

4. Recording:

A crucial component of dispensing is maintaining accurate records since they enable effective management and oversight of the services rendered. In the event that it becomes necessary to track down patients who were given a specific medication, these data will be necessary to confirm the stocks utilized in dispensing.

(E) Issuing Medicines to the Patient:

Only a pharmacist or a licensed medical professional should issue or supply medications. Make that the SRs-right patient, right medication, right dose, right route, and right time-are met when distributing the medications. Verify the patients identify by looking at their name and identification paperwork. A person under the age of eighteen should only receive medications for medical treatment. Inquire about known adverse drug reactions (ADRS) or allergies. Provide precise directions and appropriate guidance on how to take and use the prescribed medications.

Patient counseling:

Steps in effective patient counselling are:

(1) Establishing caring relationships.

(2) Assessing the patient's knowledge, attitude, and physical and mental capability.

(3) Providing visual aids in addition to oral information.

(4) Verifying patients understanding.

Effective patient counseling also requires building trust, communicating both verbally and non verbally, asking questions, actively listening, adhering to clinical objectives, demonstrating empathy and encouragement, providing privacy and confidentiality, customizing counseling to meet patient needs, and inspiring patients.( Dr. Ashok Hajare (n.d.)

Drug Distribution:

1.Centralized Unit Dose Dispensing: All medications are kept at the central pharmacy and given to patients when they need them. This approach can be used efficiently by sending copies of the doctor's original medication order to the pharmacy and delivering the unit doses directly to the patient using medicine carts and dumbwaiters.

2. Decentralized Unit Dose Dispensing: This system uses small satellite pharmacies on each floor of the hospital. The main pharmacy is made up of the manufacturing, storage, and packaging facilities that supply the satellite pharmacies. Drugs are delivered via medication carts. This kind of drug distribution method is used in hospitals with distinct buildings .Dr.Adhikarao.V.Yadav (2020)

APPLICATION:

Maintaining of medical records:

Maintaining patient medical records is a challenging undertaking. The AI system makes it simple to collect, store, normalize, and trace data. (Raza et al., 2022)

Treatment plan designing:

AI technology makes it possible to create treatment programs that are effective. An AI system is required to manage the scenario when a patient develops a severe condition and choosing an appropriate treatment strategy becomes challenging. The treatment plan that this technology suggests is designed taking into account all of the prior data and reports, clinical competence, etc. The software as a service, IBM Watson for Oncology, is a cognitive computing decision support system that helps oncology clinicians make well-informed decisions by comparing patient data to thousands of past cases and insights gained from working with physicians at Memorial Sloan Kettering Cancer Center for thousands of hours. It then presents treatment options. (Raza et al., 2022)

Health support and medication assistance:

AI technology has recently been acknowledged as effective in providing health support services and pharmaceutical assistance.) is greeted with a friendly face and a nice voice. During doctor's appointments, it aims to support patients with their chronic ailments and assist them in directing their own treatment. AI Cure is an app that runs on a smartphone's webcam and helps people manage their diseases. Both clinical trial participants and patients in dire medication problems can benefit from this app. (Raza et al., 2022)

Accuracy of medicine:

AI has a positive effect on genetic development and genomics. An AI system called Deep Genomics can be used to find mutations and connections to diseases by looking for patterns in genetic data and medical records. This approach provides physicians with information on what happens inside a cell when genetic variation .(Raza et al., 2022)

AI in drug safety, toxicology, and pharmacovigilance:

Artificial intelligence is being used more and more to improve toxicological prediction and drug safety surveillance, tackling the enormous volume and diversity of preclinical tests and post-marketing data. In order to discover adverse drug reactions (ADRs) early, artificial intelligence (AI) techniques allow the mining of a variety of data sources, including literature, wearable sensors, social media, and electronic health records. Imaging and laboratory data can also be integrated using multi-modal AI. For example, one study employed convolution neural networks on in-hospital monitoring to provide real-time predictions for surgical adverse outcomes. For example, models identified "patient-perceived" side effects that were overlooked in official reports, despite the fact that social media mining presents validation and noise issues. Aiming to prioritize possible safety signals in real time, AI-driven pharmacovigilance must overcome reporting biases and data lag.(Kandhare et al., 2025)

AI in clinical practice:

 AI is being used extensively in the healthcare industry for data collection, archiving, normalization, and tracking. The goal of deep genomics is to find patterns in massive genetic data sets and medical records in order to find mutations and connections to the disease developing a new generation of computational tools that give doctors insight into what will happen inside a cell when genetic variation, whether natural or medicinal, modifies DNA. (Tamanna Sharma et al., 2021)

Data analytics:

AI can examine patient data to find patterns and trends in adverse drug reactions, drug interactions, and medication adherence. In order to find patients who are having trouble following their prescription schedules, the AI system may gather information from patient surveys, electronic health records, and other sources. After that, it might employ machine learning algorithms to pinpoint elements like complicated dosage schedules or adverse effects that are linked to low adherence.(Khan et al., 2023)

Patient communication:

In addition to answering queries and resolving issues, AI can give patients individualized prescription recommendations and reminders. The patient could interact with AI via a messaging app rather than contacting their doctor or looking up information online. AI might offer tailored details on the drug, such as possible adverse effects, dosage instructions, and any risks or warnings. Better medication adherence and possibly better health outcomes would result from the patient feeling more knowledgeable and secure about taking their prescription. Additionally, AI's round-the-clock assistance would relieve the strain on medical professionals, freeing them up to concentrate on more complicated patient issues. (Khan et al., 2023)

Dose Recommendation :

A customized dosage recommendation system based on AI/ML can help patients by combining information from several sources, including electronic health records, safety and effectiveness metrics, illness specifics, treatment history, and patient feedback. These methods seek to reduce adverse effects while increasing treatment effectiveness. In order to forecast and modify dosages for precision-based cancer treatment, reinforcement learning algorithms have demonstrated promise. (Chalasani et al., 2023)

Drug Dispensing :

AI has the potential to enhance Automated Dispensing Systems (ADSs). AI can learn from past mistakes, use machine learning algorithms for ongoing system modification, and greatly improve dispensing accuracy and precision18. AI will increase operational efficiency and promote individualized patient care by quickly sorting and labeling pharmaceuticals, anticipating maintenance requirements, and customizing distribution based on patient preferences. Artificial intelligence (AI) can further expedite the medicine process from prescription generation to payment processing by integrating automated dispensing with other pharmacy management components, such as inventory control and electronic health information. AI significantly improves patient safety by quickly informing pharmacists of any drug interactions or patient allergies by cross-referencing dispensed medicine with patient medical records. (Al Meslamani, 2023)(Stasevych & Zvarych, 2023)

Telehealth:

The use of electronic communication to transfer medical information across locations in order to enhance health outcomes is referred to as telehealth or telemedicine. The module will help with FDA reporting and automatically transcribe and export the data to the pharmaceutical company after the adverse event has been recognized. Artificial Intelligence has the potential to enhance pharmacovigilance in telehealth situations. In one study, automated phone calls to patients beginning new drugs were found to be useful in detecting adverse drug events. For additional evaluation, patients whose answers suggested the presence of ADEs were referred to a pharmacist. Artificial intelligence could be used to forecast which patients should be screened and when to get in touch with them. When combined with other technologies like SMS and patient portals, this could increase the efficacy and efficiency of pharmacovigilance initiatives. Health information technology including wireless monitoring devices, mobile health apps, and telemonitoring are beneficial to patients. These include, but are not limited to, disease information, symptom diaries, medication logs, reminders, nutrition diaries, and communication tools. The ability of wearable technology and mobile health applications to track physiological metrics, physical status, and personal analytics can assist in medication scheduling. Patients make use of a variety of networked medical equipment, including portable insulin pumps, wearable external devices, pacemakers, and consumer goods like Fitbits and Apple Watch. (Chalasani et al., 2023)

ADVANTAGES:(Khan et al., 2023)(Albayrak Ünal et al., 2023)(Basile et al., 2024)

1. Medication reminders: AI can give patients individualized medication reminders that include the dosage and when to take their medications. decreased chance of missing doses and increased drug adherence.

2. Drug interactions: AI can check for possible drug interactions and notify patients and healthcare professionals. Better patient safety and a lower chance of negative medication interactions.

3. Patient communication: AI can provide patients with individualized support by responding to their queries and concerns. enhanced patient involvement and better patient satisfaction.

4. Adverse drug reaction monitoring: AI can keep an eye out for negative drug reactions in patients and notify both patients and medical professionals if any signs are found. Better patient safety and lower medical expenses.

5. Personalized medication management: AI can manage patient medication regimens, including dosage, frequency, and timing of administration. Improved medication adherence, reduced risk of missed doses.

6. Electronic health record (EHR) integration: AI can integrate with EHRs to provide healthcare providers with real-time updates on patient medication management and treatment progress. Improved coordination of care, reduced risk of medication errors.

7. Chronic disease management :AI can provide personalized support and medication management for patients with chronic conditions, such as diabetes or hypertension .Improved disease management, reduced healthcare costs .

8. Patient education: AI can provide patients with educational resources on medication management, disease management, and other health-related topics. Improved patient knowledge, increased patient engagement.

9. Data analytics: AI can analyze patient data to identify trends and patterns in medication adherence, drug interactions, and adverse drug reactions. Improved patient outcomes, reduced healthcare costs.

10. Prescription refill management: AI can assist patients with prescription refills, including reminders and online ordering. Improved medication adherence, increased patient convenience.

11. Prescription transfer: AI can assist patients with transferring prescriptions between pharmacies. Increased patient convenience, improved medication adherence.

12.Medication dosage adjustments: AI can assist healthcare providers with adjusting medication dosages based on patient data. Improved patient outcomes, reduced risk of medication errors.

13. Drug formulary management: AI can assist healthcare providers with selecting medications based on patient data and formulary requirements. Reduced healthcare costs, improved patient outcomes.

14. Medication therapy management: AI can provide medication therapy management services, including comprehensive medication reviews and medication counseling. Improved medication adherence, reduced risk of medication errors.

15. Patient screening: AI can screen patients for potential medication-related issues, such as nonadherence or drug interactions. Improved patient outcomes, reduced healthcare costs.

16 Patient triage: AI can assist healthcare providers with training patients based on their medication-related needs. Improved patient outcomes, reduced healthcare costs.

17. Medication reconciliation: AI can assist healthcare providers with reconciling medication lists between different care settings. Improved coordination of care, reduced risk of medication errors.

18. Patient-centered care: AI can provide personalized medication management and support, tailored to each patient's unique needs. Improved patient satisfaction, increased patient engagement.

19. Medication adherence tracking: AI can track patient medication adherence and provide alerts to healthcare providers and patients if nonadherence is detected. Improved medication adherence, reduced healthcare costs.

20. Telemedicine support: AI can provide telemedicine support, including virtual medication reviews and consultations. Increased patient convenience, improved access to care.

DISADVANTAGES:(khan et al., 2023)

In Hospital Pharmacy:

1. Irregular and demanding schedules, often including nights, weekends, and holidays.
2. High stress levels due to complex patient cases and critical care responsibilities.
3. Longer educational pathway, often including a residency.
4. Less direct interaction with patients compared to retail roles.

In Retail Pharmacy:

8683511. I. High workload with pressure to meet corporate metrics like prescription volume and vaccination goals.
8683511. II. Stress of customer service challenges, insurance issues, and high prescription loads.
8683511. III. Less clinical involvement in patient treatment decisions compared to hospital pharmacists.
8683511. IV. Fewer specialization opportunities without additional certifications or advanced roles.

Data Privacy and Security:

 Sensitive health data is frequently the subject of data breaches. Protecting patient data is therefore essential. Lawsuits have been brought against big health systems and AI firms for exchanging data, and some patients may worry that their privacy will be abused. When it comes to data privacy, patient consent is crucial since healthcare organizations may permit the extensive use of patient data for AI training without getting enough individual patient approval. (Chalasani et al., 2023)

Data Integration:

When a system learns irrelevant correlations between patient characteristics and outcomes, over fitting may happen. It results from an excessive number of variable parameters compared to outcomes, which causes the algorithm to make predictions based on unsuitable attributes. Using a minimal amount of data can yield very good accuracy from some classification and clustering techniques, however this may not be appropriate or feasible.(Chalasani et al., 2023)

FUTURE SCOPE: (Khan et al., 2023)

Research and development: In order to grow portfolios, pharmaceutical businesses must reduce risk by allocating R&D funding appropriately to support decision-making.

1. By raising the possibility that new drugs will be discovered.
2. Create a strong increase in revenue.
3. To get advantages from an integrated R&D environment, such as a production, sales, and marketing setting.

Clinical Trial Research: Artificial Intelligence (AI) has the potential to revolutionize clinical trials and enhance the safety and quality of life-improving medications. It is far more accurate than other methods for predicting drug activity. Clinical studies take too long and are very costly; it takes around 15 years to have a product that is ready for the market. It's not good; the present drug discovery approach is no longer as viable. Artificial intelligence-powered bots: In the future, pharmaceutical companies will be able to develop bots for doctors in a similar manner to how they utilize apps. (Tamanna Sharma et al., 2021)

CONCLUSION:

Artificial Intelligence (AI) is reshaping the pharmacy sector by moving it away from traditional, manual processes toward a system that is more efficient, accurate, and patient-centered. From dispensing and prescription management to inventory control and drug interaction checks, AI provides tools that reduce errors, save time, and improve safety. Its integration with electronic health records and predictive algorithms ensures better monitoring, personalized care, and enhanced decision-making for both patients and healthcare professionals. While the advantages are clear improved patient outcomes, reduced costs, and optimized workflows—there are also important challenges. Issues of data privacy, ethical use, and system integration remain obstacles that must be addressed before AI can reach its full potential. Additionally, both hospital and retail pharmacies face stress and workload concern that AI may help alleviate, but human oversight and professional judgment will always be essential. As technology advances, pharmacists will increasingly act as healthcare advisors supported by AI-driven insights rather than being limited to routine tasks. In short, AI is not here to replace pharmacists but to empower them—helping deliver safer, smarter, and more personalized care. If its risks are managed responsibly, AI holds the promise of transforming pharmacy into a more patient-focused, accessible, and innovative field for the future.

REFERENCES

1. Rosenthal, M.M.; Breault, R.R.; Austin, Z.; Tsuyuki, R.T. Pharmacists’ self-perception of their professional role: Insights into community pharmacy culture. J. Am. Pharm. Assoc. JAPhA 2011, 51, 363–367. [CrossRef] [PubMed]
2. Aldhwaihi, K.; Schifano, F.; Pezzolesi, C.; Umaru, N. A systematic review of the nature of dispensing errors in hospital pharmacies.Integr. Pharm. Res. Pract. 2016, 5, 1–10. [CrossRef] [PubMed]
3. Batson, S.; Herranz, A.; Rohrbach, N.; Canobbio, M.; Mitchell, S.A.; Bonnabry, P. Automation of in-hospital pharmacy dispensing:A systematic review. Eur. J. Hosp. Pharm. 2021, 28, 58–64. [CrossRef] [PubMed]
4. Al Nemari, M.; Waterson, J. The Introduction of Robotics to an Outpatient Dispensing and Medication Management Process in Saudi Arabia: Retrospective Review of a Pharmacy-led Multidisciplinary Six Sigma Performance Improvement Project. JMIR Hum. Factors 2022, 9, e37905. [CrossRef] [PubMed]
5. Alahmari, A.R.; Alrabghi, K.K.; Dighriri, I.M. An Overview of the Current State and Perspectives of Pharmacy Robot and Medication Dispensing Technology. Cureus 2022, 14, e28642. [CrossRef] [PubMed]
6. Al-Abri, R.; Al-Balushi, A. Patient Satisfaction Survey as a Tool Towards Quality Improvement. Oman Med. J. 2014, 29, 3–7.[CrossRef] [PubMed]
7. AlOmari, F.; Hamid, A.B.A. Strategies to improve patient loyalty and medication adherence in Syrian healthcare setting: The mediating role of patient satisfaction. PLoS ONE 2022, 17, e0272057. [CrossRef] [PubMed]
8. Raza M A, Aziz S, Noreen M, Saeed A, Anjum I,AhmedM, & Raza S M. (2022). Artificial Intelligence (AI) in Pharmacy: An Overview of Innovations. Innovations in pharmacy, 13(2), 13-13.
9. Roullet B and Droulers O. Pharmaceutical packaging color and drug expectancy. Adv Consum Res. 2005; 32: 164-171. Ruhle F, Braun R and Ostermann H. Impact of robotic dispensing machines in German pharmacies on business performance indicators. Libyan J Med. 2009; 4: 146-151.
10. Kalyane, D., Sanap, G., Paul, D., Shenoy, S., Anup, N., Polaka, S., & Tekade, R. K. (2020). Artificial intelligence in the pharmaceutical sector: current scene and future prospect. In The Future of Pharmaceutical Product Development and Research (pp. 73-107). Academic Press.
11. Jungreithmayr V, Meid AD, Haefeli WE, Seidling HM. The impact of a computerized physician order entry system implementation on 20 different criteria of medication documentation—a before-and-after study. BMC Med Inform Decis Mak. 2021 Dec;21 (1):1–2.
12. Schiff G. Brigham and Women’s Hospital, Harvard Medical School, Partners Health-care. Available from https://www.fda.gov/files/drugs/published/ Computerized-Prescriber-Order-Entry-Medication-Safety.pdf; 2023.
13. E-Prescription is defined as using an electronic device to submit and exchange the prescription information among the involved parties, namely, the patient, prescriber, pharmacy and health insurance company. It is worth mentioning that the patient involvement in the majority of the e-Prescription systems we reviewed is only to consent to use an e-Prescription by the prescriber and the pharmacy. The use of e-Prescription will allow the involved parties to provide a safe, quality, and efficient care service. Moreover,e-Prescription systems will provide the communication me-dium between a prescriber and a pharmacist upon reviewing a prescription before dispensing (AMA et al., 2011; Bell et al.,2004; Mon, 2009; Odukoya and Chui, 2013; Samadbeik et al., 2017; Van Dijk et al., 2011).
14. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019 Dec;17:1–9.
15. Segura JMG. Artificial intelligence in pharmacy practice: information technology [Internet]. Pharma Focus Asia; 2023. Available from https://www.pharmafocusasia. com/information-technology/artificial-intelligence-pharmacy-practice [Accessed on 20th June 2023].
16. Dalal AK, Fuller T, Garabedian P, et al. Systems engineering and human factors support of a system of novel I-integrated tools to prevent harm in the hospital. J Am Med Inform Assoc. 2019;26(6):553–560. https://doi.org/10.1093/jamia/ocz002.
17. Seraj M,M, Khan O, Khan MZ, et al. Analytical research of artificial intelligent models for machining industry under varying environmental strategies: an industry 4.0approach. Sustainable Operations and Computers. 2022;3:176–187.
18. Parvez M, Khan O. Parametric simulation of biomass integrated gasification combined cycle (BIGCC) power plant using three different biomass materials. Biomass Conversion and Refinery. 2020;10(4):803–812.
19. Perez HD et al (2021) Algorithmic approaches to inventory management optimization. Processes 9(1):102
20. Singh D, Verma A (2018) Inventory management in supply chain. Mater Today 5(2):3867–3872.
21. Luo, H., Guo, S., Li, Y., & Liu, Z. (2019). A hierarchical deep learning-based framework for inventory prediction in a multi-echelon supply chain. IEEE Transactions on Industrial Informatics, 15(10), 5458-5468. DOI: 10.1109/TII.2018.2877262.
22. Kassem LM, Alhabib B, Alzunaydi K, Farooqui M. Understanding patient needs regarding adverse drug reaction reporting smartphone applications: a qualitative insight from Saudi arabia. Int J Environment Res Pu-bl Health. 2021;18(8):3862. https://doi.org/10.3390/ijerph18083862.
23. Smith JM (2005) Building a safer NHS for patients, Improving medication safety.www.doh.gov.uk
24. Popova M, Isayev O, Tropsha A. Deep reinforcement learning for de novo drug design. Sci Adv 2018;4(7):eaap7885. https://doi.org/10.1126/sciadv.aap7885.
25. Dr. Ashok Hajare ,' A textbook of Community Pharmacy, Nirali Prakashan.
26. Dr. Adhikarao.V.Yadav, A textbook of Hospital and Clinical Pharmacy, Nirali Prakashan.
27. Deep Mind’s health team. [cited 2022 13 June]; Available from https://www.deepmind.com/blog/deepminds-health- team-joins-google-health.
28. IBM Watson for Oncology.; Available from: https://www.ibm.com/common/ssi/cgi-bin/ssialias?appname=skmwww&htmlfid=897%2FENUS5725W51&infotype=DD&subtype=SM&mhsrc=ibmsearch_a&mhq=IBM%20WATSON%20ONcology#:~:text=IBM%20Watson%20for%20Oncology%2C%20software,Center%20physicians%20and%20other%20analysts.
29. MOLLY, THE VIRTUAL NURSE. [cited 2022 13 June]; Available from: http://adigaskell.org/2015/03/20/meet-molly-the-virtual-nurse/.
30. Shampo, M.A. and R.A. Kyle, J. Craig Venter--The Human Genome Project. Mayo Clinic proceedings, 2011. 86(4): p. e26-e27.
31. Singh Neetu, Varshney Upkar. IT-based reminders for medication adherence:systematic review, taxonomy, framework and research directions. Eur J Inf Syst.2020;29(1):84–108. https://doi.org/10.1080/0960085X.2019.1701956.
32. Butow P, aHoque E. Using artificial intelligence to analyse and teach communication in healthcare. Breast. 2020;50:49–55.
33. Johnson M, Patel M, Phipps A, Van der Schaar M, Boulton D, Gibbs M. The potential and pitfalls of artificial intelligence in clinical pharmacology. CPT Pharmacometrics System Pharmacol. 2023 Mar;12(3):279–284.
34. NEJM Catalyst. What is telehealth?. Available from: https://catalyst.nejm.org/what-is-telehealth/; February 1, 2018. Accessed 28 June 2023.
35. Edrees H, Song W, Syrowatka A, Simona A, Amato MG, Bates DW. Intelligent telehealth in pharmacovigilance: a future perspective. Drug Saf. 2022;45(5): 449–458. https://doi.org/10.1007/s40264-022-01172-5.
36. Bohlmann A, Mostafa J. Kumar M machine learning and medication adherence.Scoping Review JMIRx Med. 2021;2(4), e26993. https://doi.org/10.2196/26993.
37. Singh Neetu, Varshney Upkar. IT-based reminders for medication adherence:systematic review, taxonomy, framework and research directions. Eur J Inf Syst.2020;29(1):84–108. https://doi.org/10.1080/0960085X.2019.1701956.
38. Zhang Q, Li S, Chen F, Zeng R, Tong R. Targeted delivery strategy: a beneficial partner for emerging senotherapy. Biomed Pharmacother. 2022;155, 113737. https://doi.org/10.1016/j.biopha.2022.113737.
39. Li Raymond, Curtis Kate, Zaidi Syed Tabish, Connie Van, Castelino Ronald. A new paradigm in adverse drug reaction reporting: consolidating the evidence for an intervention to improve reporting. Expet Opin Drug Saf. 2022;21(9):1193–1204.
40. Kassem LM, Alhabib B, Alzunaydi K, Farooqui M. Understanding patient needs regarding adverse drug reaction reporting smartphone applications: a qualitative insight from Saudi arabia. Int J Environ Res Publ Health. 2021;18(8):3862. https://doi.org/10.3390/ijerph18083862.
41. Motulsky A, Nikiema JN, Bosson-Rieutort D. Artificial intelligence and medication management. In: Househ M, Borycki E, Kushniruk A, eds. Multiple Perspectives on Artificial Intelligence in Healthcare. Lecture Notes in Bioengineering. Cham: Springer;2021. https://doi.org/10.1007/978-3-030-67303-1_8.
42. Haque Ahshanul, Chowdhury Md Naseef-Ur-Rahman. Transforming chronic disease management with chatbots: key use cases for personalized and cost-effective care.TechRxiv. Preprint. 2023 https://doi.org/10.36227/techrxiv.22579735.v1.
43. Morin O, Vallieres M, Braunstein S, et al. An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication. Nat Can (Ott). 2021;2:709–722. https://doi.org/10.1038/s43018-021-00236-2.
44. Cris Martin P, Jacoba Leo Anthony Celi, Silva Paolo S. Biomarkers for progression in diabetic retinopathy: expanding personalized medicine through integration of AI with electronic health records. Semin Ophthalmol. 2021;36(4):250–257. https://doi.org/10.1080/08820538.2021.1893351.
45. Subramanian M, Wojtusciszyn A, Favre L, et al. Precision medicine in the era of artificial intelligence: implications in chronic disease management. J Transl Med.2020;18:472. https://doi.org/10.1186/s12967-020-02658-5.
46. Xie Y, Lu L, Gao F, et al. Integration of artificial intelligence, blockchain, and wearable technology for chronic disease management: a new paradigm in smart healthcare. CURR MED SCI. 2021;41:1123–1133. https://doi.org/10.1007/s11596-021-2485-0.
47. Lewis Deborah, EdD RN, CDE. Computer-based approaches to patient education: a review of the literature. J Am Med Inf Assoc. July 1999;6(4):272–282. https://doi.org/10.1136/jamia.1999.0060272.
48. Reddy Sandeep, Allan Sonia, Coghlan Simon, Cooper Paul. A governance model for the application of AI in health care. J Am Med Inf Assoc. March 2020;27(Issue 3): 491–497. https://doi.org/10.1093/jamia/ocz192.
49. Fu R, Xu H, Lai Y, et al. A VOSviewer-based bibliometric analysis of prescription refills. Front Med. 2022 Jun 21;9, 856420. https://doi.org/10.3389/fmed.2022.856420. PMID: 35801215; PMCID: PMC9254907.
50. McInnes D, Shimada S, Midboe A, et al. Patient use of electronic prescription refill and secure messaging and its association with undetectable HIV viral load: a retrospective cohort study. J Med Internet Res. 2017;19(2):e34.
51. Anita M Preininger, Brett South, Heiland Jeff, et al. Artificial intelligence-based conversational agent to support medication prescribing. JAMIA Open. July 2020;3(Issue 2):225–232. https://doi.org/10.1093/jamiaopen/ooaa009.
52. Adam Wright, McEvoy Dustin S, Aaron Skye, et al. Structured override reasons for drug-drug interaction alerts in electronic health records. J Am Med Inf Assoc. October 2019;26(Issue 10):934–942. https://doi.org/10.1093/jamia/ocz033.
53. Laura C, et al. Identifying Drugs with Disease-Modifying Potential in Parkinson's Disease Using Artificial Intelligence and Pharmacoepidemiology. Issue8: Volume29; 2020: 864–872.
54. Goedken Amber M, Butler Christine M, McDonough Randal P, Deninger Michael J,Doucette William R. Continuous Medication Monitoring (CoMM): a foundational model to support the clinical work of community pharmacists. Res Soc Adm Pharm.2018;14(1):106–111.
55. Weisberg EM, Chu LC, Fishman EK. The first use of artificial intelligence (AI) in the ER: triage not diagnosis. Emerg Radiol. 2020;27:361–366. https://doi.org/10.1007/s10140-020-01773-6.
56. Wang M, Xia C, Huang L, et al. Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation. Lancet Digital Health. 2020;2:e506–e515.
57. Pieczynski J, Kuklo P, Grzybowski A. The role of telemedicine, in-home testing and artificial intelligence to alleviate an increasingly burdened healthcare system: diabetic retinopathy. Ophthalmol Ther. 2021;10:445–464. https://doi.org/10.1007/s40123-021-00353-2.
58. Grundy BL, Crawford P, Jones PK, et al. Telemedicine in critical care: an experiment in health care delivery. JACEP. 1977;6:439–444.
59. Howari H, Parvez M, Khan O, Alhodaib A, Mallah A, Yahya Z. Multi-Objective optimization for ranking waste biomass materials based on performance and emission parameters in a pyrolysis process—an AHP–TOPSIS approach.Sustainability. 2023;15(4):3690. https://doi.org/10.3390/su15043690.
60. S. Ji, Q. Gu, H. Weng, Q. Liu, P. Zhou, Q. He, R. Beyah, T. Wang, De-health: all your online health information are belong to us. arXiv preprint. (2019).
61. B. Lubarsky, Re-identifcation of “anonymized data.” UCLA L. Rev. 1701, 1754 (2010)
62. Patil DP. Emotion in artificial intelligence and its life research to facing troubles. International Journal of Research in Computer Applications and Robotics. 2016 Apr.
63. Roff HM. Advancing human security through artificial intelligence. Chatham House; 2017 May.