The Role of Artificial Intelligence in Pharmacy Practice and Patient Care: Innovations and Implications

Abstract

Artificial Intelligence (AI) is reshaping pharmacy practice by enhancing decision-making, personalizing therapy, and improving medication safety. AI applications now span drug discovery, clinical decision support, and adherence monitoring. This narrative review explores key innovations, practical applications, and the implications of AI integration in pharmacy practice, with a focus on emerging tools, pharmacist roles, and ethical considerations. The review was conducted using literature from PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar. Thematic synthesis included AI-based drug interaction checkers, Clinical Decision Support Systems (CDSS), telepharmacy, pharmacogenomics, and predictive analytics. AI enhances clinical decision-making, reduces medication errors, and supports precision medicine. AI tools support pharmacists and healthcare professionals in optimizing care. However, data privacy, algorithmic bias, and workflow integration continue to pose challenges. AI holds transformative potential in pharmacy, though its integration requires overcoming ethical and workflow-related challenges. Ethical and regulatory vigilance, coupled with pharmacist training and interdisciplinary collaboration, is essential to realize the full potential of AI.

1. Introduction

Artificial intelligence (AI) has revolutionized various sectors of healthcare, including pharmacy practice and patient care. AI-driven tools and technologies enhance clinical decision-making, optimize medication management, and improve patient outcomes [1]. From drug interaction detection to personalized medicine and automation in dispensing, AI is reshaping traditional pharmacy roles [2]. This narrative review explores the impact of AI in pharmacy practice, highlighting innovations and their implications for healthcare professionals and patients.

Artificial intelligence is a branch of computer science which focuses on designing a system which shows intelligent behaviour. Its core capabilities are problem solving, learning, reasoning, and perception. These systems are enabled by algorithms and statistical models. They learn from data and make predictions [3].

Artificial Intelligence comprises several subfields that collectively contribute to its application in healthcare and pharmacy practice. Among these, Machine Learning (ML) supports pattern recognition and prediction in clinical data, while Deep Learning (DL) excels at processing complex datasets such as medical images and genomic sequences. Natural Language Processing (NLP) enables interpretation of unstructured text, aiding in drug information extraction and chatbot interactions. Other branches like Computer Vision and Reinforcement Learning (RL) facilitate automated visual analysis and feedback-driven optimization, respectively [4,5].

Table 1. Major subfields of AI and their roles in healthcare.

Subfield	Definition	Role in Healthcare
Machine Learning	Learns patterns from data	Predicting adverse drug reactions (ADRs), therapy optimization
Deep Learning	Neural networks for complex tasks	Imaging, genomic analysis
Natural Language Processing	Understands human language	Drug information extraction, chatbot queries
Computer Vision	Enables machines to interpret visual data	Pill identification, prescription label verification, automated inspection in compounding pharmacies
Reinforcement Learning	Learns via feedback	Dosing strategies, inventory control

summarizes these core AI domains and illustrates their roles in advancing pharmaceutical and patient-centered care.

Due to exponential growth in data, the healthcare sector is undergoing substantial transformation. With the aging population, there is increase in chronic diseases. The management of chronic diseases and comorbidities need complex medication regimens which necessitates the careful consideration of drug interactions [6]. Personalized medicine is capable of improving health outcomes. It creates a large amount of patient-specific data. This data includes genetic information and lifestyle details. In order to use this data well, advanced analytical tools are needed [7]. Together, an enormous amount of healthcare data is generated from electronic health records (EHRs), medical imaging, wearables, and clinical research. The challenge is to turn this data into clinically relevant and actionable evidence [8]. The rising healthcare costs put extra pressure on healthcare systems. This makes efficient use of resources and cost-effective treatments essential [9]. Patients today are more informed about their health and take an active role in their care. They expect care that is personalized, timely, and smooth [10]. Pharmacists play a key role in managing medicines, educating patients, and ensuring drug safety. Traditional pharmacy practices cannot keep up with the growing data and complexity. New solutions, like AI, are needed to improve efficiency, safety, and patient outcomes.

Artificial intelligence has progressed from early experimental programs—beginning with Christopher Strachey’s 1951 checker-playing system—to a defined research field following John McCarthy’s 1956 Dartmouth Conference, which coined the term “AI.” The 1960s–1970s were dominated by rule-based and expert systems, whose performance was constrained by limited computing power and data availability. In the 1980s–1990s, emphasis shifted to machine learning and neural networks, exemplified by IBM’s Deep Blue defeating Garry Kasparov in 1997, demonstrating the potential of data-driven approaches. The 2000s saw rapid advances in natural language processing and computer vision, enabling consumer-facing virtual assistants (e.g., Siri, Alexa) capable of understanding and responding to speech. Today, AI is reshaping healthcare sector; in biomedicine it accelerates analysis of large-scale datasets, catalyzing breakthroughs in genomics and drug discovery, and supports clinical practice through diagnostic tools and personalized treatment planning. As capabilities expand, responsible, equitable, and transparent development remains essential to maximize societal benefit and mitigate risks [11]. To contextualize the progression of AI and its growing influence on healthcare and pharmacy practice, a chronological overview of major milestones in AI development is presented Figure 1.

The integration of AI into drug discovery is transforming the pharmaceutical sector, propelled by substantial investments and a rapidly expanding market. In 2023, the global AI in drug discovery market was valued at approximately USD 1.5 billion, with forecasts projecting a compound annual growth rate (CAGR) of 29.7%, potentially reaching USD 11.8 billion by 2030. According to an estimate of Fortune Business Insights, in 2023, the value of AI in drug discovery was about USD 3.54 billion. By 2030, it is expected to grow to USD 7.94 billion, which means a growth rate of about 12.2% CAGR. These numbers show that AI in drug discovery has strong economic potential. It is becoming a major focus for innovation and investment in the pharmaceutical industry [2].

Recent advancements in AI-driven image recognition and captioning models have significantly expanded the scope of pharmacy practice and patient care. In pharmacy settings, computer vision systems are increasingly applied for automated pill identification, detection of dispensing errors, and validation of prescriptions through image-based verification. Similarly, wearable devices integrated with AI-powered image analytics support medication adherence by recognizing patient dosing behaviors and identifying missed doses [5]. Emerging captioning models such as CLIP (Contrastive Language-Image Pretraining), BLIP (Bootstrapped Language-Image Pretraining), and Vision Transformers (ViT) demonstrate enhanced contextual understanding, enabling accurate interpretation of complex medication or adherence-related images. These innovations underscore the potential of visual AI systems to strengthen medication safety, improve adherence monitoring, and support data-driven patient care strategies in both community and clinical pharmacy settings [10].

Several earlier reviews have examined the use of artificial intelligence in healthcare and pharmaceutical sciences; however, most have focused on specific subdomains such as drug discovery, pharmacovigilance, or predictive analytics. For example, Kandhare et al. (2025) reviewed AI and ML tools in pharmaceutical sciences with emphasis on drug development pipelines, while Rahman et al. (2024) primarily discussed AI in smart healthcare systems, focusing on hospital informatics rather than pharmacy operations. Tantray et al. (2024) explored intelligent prescription systems but did not cover telepharmacy, patient counselling, or AI-driven clinical decision support in depth. Likewise, Meknassi Salime et al. (2025) conducted a systematic review on hospital pharmacy automation, focusing on robotics and dispensing technologies [4,5,12,13].

In contrast, the present review provides a comprehensive, pharmacy-centered synthesis of AI applications across the entire spectrum of pharmaceutical care—including clinical decision support, pharmacovigilance, personalized pharmacotherapy, telepharmacy, drug interaction checking, patient counselling, inventory optimization, and the emerging role of large language models (LLMs) and AI chatbots. Furthermore, this review integrates ethical, regulatory, workflow, and educational considerations, which are often overlooked in earlier reviews. By incorporating recently published studies from 2023 to 2025, our work offers an updated, multidimensional, and practice-oriented analysis that fills the gaps left by prior literature. The aim is to equip pharmacists, researchers, educators, and health policymakers with a clear understanding of how AI is transforming pharmacy practice today and the future directions likely to shape the profession.

The structure of this review is guided by a conceptual progression that mirrors the way AI is understood, adopted, and operationalized within pharmacy practice. It begins with foundational concepts and classifications of AI to establish a shared baseline with varied technical backgrounds. From this foundation, the review transitions into the core thematic domains of pharmacy where AI exerts the greatest impact, such as decision support, pharmacovigilance, dispensing automation, patient counselling, and personalized therapy. Subsequently, the review synthesizes cross-cutting issues such as ethical considerations, data governance, regulatory needs, and the evolving competencies required of pharmacists. These semantic groupings reflect the interconnected nature of AI implementation, where technological advancements must be contextualized within professional, ethical, and systemic frameworks. The concluding sections focus on future directions, integrating the thematic insights into forward-looking implications for practice, research, and policy.

AI adoption in pharmacy practice does not occur uniformly worldwide. Healthcare systems differ substantially in digital infrastructure, regulatory maturity, workforce capacity, economic resources, and public health priorities. These geographical dependencies shape not only the pace of AI implementation but also the types of pharmacy problems AI is expected to address in each region. High-income countries often deploy AI to optimize efficiency, clinical decision support, and data-intensive personalized medicine, whereas low- and middle-income countries (LMICs) prioritize AI for medication access, supply chain stability, telepharmacy, and public health surveillance. By integrating evidence from multiple regions, this review positions its findings as globally relevant yet contextually adaptable.

2. Materials and Methods

This review follows a structured literature search strategy designed to minimize duplication across meta-data indexing platforms. Four major databases were searched: PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar, from 2023 to 2025. These databases were selected because they index overlapping but complementary bodies of literature; therefore, a multistep de-duplication process was undertaken to ensure accuracy. Searches were performed using Boolean combinations of medical, technical, and pharmacy-specific keywords, including: “artificial intelligence”, “machine learning”, “deep learning”, “pharmacy practice”, “pharmacovigilance”, “telepharmacy”, “clinical decision support system”, “drug–drug interactions”, “automated dispensing”, and “AI in patient counselling.” The initial search retrieved 412 records (PubMed = 130; Scopus = 120; Web of Science = 90; Google Scholar = 72). All records were reviewed by two independent reviewers and 218 duplicate records were removed. After preprocessing, 194 unique articles remained. Then the reviewers screened titles and abstracts for relevance. Non-English papers, articles unrelated to healthcare or pharmacy, conference abstracts, incomplete manuscripts, and commentary pieces lacking methodological depth were excluded. This stage removed 42 records, leaving 152 full-text articles for evaluation. During full-text screening, articles were assessed based on relevance to AI applications in pharmacy practice, conceptual clarity, methodological rigor, and contribution to clinical, operational, ethical, or regulatory insights. After exclusions, 133 articles were included in the final synthesis. The process is summarized in Figure 2, following a PRISMA-style flow sequence adapted for the review.

3. AI in Pharmacy Practice: A Transformative Enabler

The integration of AI into pharmacy practice offers a transformative approach to tackling the increasing complexity of medication management, chronic disease burden, and data overload in healthcare [14]. Rather than replacing pharmacists, AI augments their capabilities by enabling faster, safer, and more personalized pharmaceutical care.

In modern pharmacy settings, AI supports key functions such as identifying drug interactions, optimizing therapeutic regimens, and detecting prescribing errors. These technologies also enhance workflow efficiency through automation in dispensing and inventory control, while facilitating remote care via telepharmacy platforms and AI-enabled patient engagement tools. Furthermore, AI-driven pharmacogenomics and predictive analytics allow for a shift toward proactive, data-informed, and precision-based interventions [15,16].

Specific AI applications—clinical decision support, pharmacovigilance (PV), and patient education—collectively mark a paradigm shift, positioning pharmacists as data interpreters, therapy optimizers, and digital health collaborators in the evolving healthcare landscape. Figure 3 illustrates the principal domains in which AI is currently transforming pharmacy practice and patient care, highlighting both operational and clinical applications.

4. AI Tools Commonly Used in Pharmacy Practice and Patient Care

Artificial intelligence is modernizing pharmacy practice and patient care by introducing tools that enhance safety, efficiency, and personalization. These AI-powered solutions support pharmacists in analyzing complex drug interactions, identifying prescribing errors, optimizing therapy, and improving patient adherence [16,17]. Thus, AI augments their roles through data-driven support and automation.

Table 2. Applications and tools of AI in pharmacy practice and patient care.

AI Application	Purpose	Examples	References
AI-Powered Drug Interaction Checkers	Identify adverse effects from polypharmacy	DynaMedex (Micromedex with Watson), UpToDate® Enterprise EditionAI Chatbots (e.g., ChatGPT, Copilot, Gemini)	[18,19,20,21,22]
AI-Based Prescription Analysis & Medication Safety	Detect prescription errors, ensure dose accuracy, and guideline compliance	MedAware, DoseMeRx	[23,24]
AI Chatbots for Patient Counselling & Adherence	Improve patient understanding, adherence, and self-management	Pillo Health, Ada Health, MediBot	[25,26,27,28]
AI-Driven Drug Discovery & Pharmacovigilance	Accelerate drug development and detect adverse events post-marketing	IBM Watson for Drug Discovery, DeepMind Health, FDA Sentinel System	[29,30,31,32]
AI for Personalized Medicine & Pharmacogenomics	Tailor drug therapy using genetic and clinical data	PharmGKB, Tempus	[33,34]
AI in Automated Dispensing & Robotics	Enhance accuracy and efficiency in medication handling	BD Rowa™ Vmax, Omnicell, PillPack	[17,35,36]
AI in Clinical Decision Support Systems (CDSS)	Assist in evidence-based prescribing and drug safety	Epic Systems, IBM Watson Health, Cerner Millennium	[37,38,39]

offers a concise summary of these tools, categorized by function, with real-world examples illustrating their practical relevance across various domains—including clinical decision support, PV, personalized medicine, and drug development.

5. AI in Clinical Decision Support Systems

Artificial Intelligence has brought a paradigm shift in clinical decision-making, particularly through its integration into CDSS. These AI-driven platforms empower pharmacists by providing real-time, data-informed, and patient-specific recommendations that enhance medication safety, therapeutic efficacy, and workflow efficiency [40].

At the core of AI-enabled CDSS lies the ability to process vast volumes of clinical data—including EHRs, laboratory results, patient demographics, and medication histories—to support complex decisions at the point of care. These systems aid in optimizing drug selection and dosage, detecting potential drug–drug interactions (DDIs), and preventing adverse drug events (ADEs) [41]. Unlike traditional alert systems, AI-powered CDSS can contextualize alerts by incorporating patient-specific parameters such as age, renal function, and comorbidities, thus reducing alert fatigue and enhancing clinical relevance [42]. Building on these capabilities, real-world evaluations show measurable benefits at the bedside. Clinician feedback indicates that AI-enabled CDSS streamline decision-making, with an observed ~30% reduction in cognitive workload. Complementing these user-reported gains, clinical trials have demonstrated a 15% reduction in adverse drug reactions and a ~20% improvement in outcomes for chronic disease management [43].

AI-CDSS tools also serve as the backbone for several key pharmacy functions discussed throughout this review. For instance:

• Medication safety and prescription analysis, including error detection and dose individualization, are supported by CDSS platforms such as MedAware and DoseMeRx.
• Predictive analytics integrated within CDSS can anticipate high-risk scenarios—such as hospital readmissions or ADEs—by identifying subtle patterns in patient data.
• Medication Therapy Management is enhanced when CDSS tools integrate pharmacogenomic and behavioral data to recommend personalized regimens.
• Adherence monitoring and intervention can also be guided by CDSS through integration with wearable data and patient-reported outcomes, improving chronic disease management.

Furthermore, modern CDSS platforms are dynamic, continuously learning from real-world outcomes to refine their predictive models and clinical guidance [1,43]. For example, AI-enabled systems can automatically adjust dose recommendations based on evolving lab values or flag deviations from clinical guidelines in real time. A study conducted on Waldenström macroglobulinemia patient found that an AI-derived CDSS (CURATE.AI) could deliver longitudinal, biomarker-guided, patient-specific dose recommendations for ibrutinib over a two-year period—dynamically adjusting doses in step with changing laboratory parameters and operationalizing real-time individualized dosing under clinician oversight [44]. When integrated into EHRs, these tools support not only pharmacists but also the entire interdisciplinary care team, promoting coordinated, patient-centered care [41]. Thus, AI-powered CDSS functions as a unifying framework that supports various domains of pharmacy practice. Its adaptability and scalability position it as a cornerstone for safe, efficient, and evidence-based medication management in the era of digital health.

6. Automation and AI in Pharmacy Inventory and Supply Chain Management

Effective inventory and supply chain management is a cornerstone of modern healthcare, ensuring timely availability of medications while minimizing waste, reducing costs, and improving patient outcomes. Automation, enhanced by AI, has transformed these operations by increasing accuracy, efficiency, and safety across dispensing, inventory management, ordering, and distribution processes in pharmacy practice.

Robotic and AI-driven technologies are increasingly integrated into pharmacy workflows to automate key functions and reduce manual errors [17]. Automated Dispensing Systems (ADS) use robots and packaging machines to dispense medicines accurately. They help reduce mistakes made by humans. In a hospital in Brazil, robots were added to the central pharmacy. After this, prescription errors dropped from 26% in 2013 to 15% in 2017. Distribution errors also decreased, from 36% in 2013 to 33% in 2017 [12].

AI-driven inventory systems monitor prescription trends, predict demand, and manage stock levels effectively, helping prevent both shortages and excess stock. These systems also use data analytics to optimize medication replenishment and avoid medication expiry [2]. Additionally, prescription filling automation integrated with EHRs enables seamless cross-checking of prescriptions, detects potential drug interactions, and ensures regulatory compliance [13]. Barcode scanning and RFID (Radio-frequency identification) technologies further improve the accuracy of medication tracking and security throughout the supply chain [45].

AI helps not only in dispensing but also in ordering and distributing medicines. Automated systems predict demand and place purchase orders to keep the optimal stock levels [46]. Smart algorithms improve delivery by checking routes, supplier performance, and even weather conditions [47]. Predictive analytics evaluates prescription trends and epidemiological data to plan medicine needs in advance [48]. Blockchain is used to track medicines securely and prevent tampering to ensure authenticity and regulatory compliance [49]. In warehouses, robots and AI systems store and retrieve medicines quickly and accurately [50]. New delivery methods, like drones and self-driving vehicles, are being tested to send medicines to remote areas or during emergencies [51].

Integrating automation and AI across pharmacy supply chains improves operational efficiency while reinforcing medication safety, end-to-end traceability, and timely responsiveness to patient needs.

7. AI in Pharmacovigilance

Pharmacovigilance plays a critical role in ensuring medication safety by detecting, assessing, and preventing ADRs [52]. With the increasing complexity of drug regimens and the exponential growth of real-world data, traditional PV methods—such as spontaneous reporting systems and manual literature reviews—often fall short due to underreporting, delayed signal detection, and limited scalability. AI offers an advanced solution by transforming PV into a proactive, data-driven discipline [53].

AI technologies enable automated analysis of diverse data sources. These include EHRs, PV databases (e.g., FAERS, VigiBase), clinical trial repositories, social media platforms, and wearable health devices. By integrating and analyzing this data, AI systems can detect subtle patterns and associations that may indicate previously unrecognized ADRs—often in near real-time [54].

Predictive models powered by AI evaluate risk factors such as age, gender, genetic variants, comorbidities, and medication history to estimate a patient’s likelihood of experiencing an ADR. This supports targeted risk mitigation and complements AI applications in personalized medicine. In a cohort of hospitalized older adults, Hu et al. compared several ML models for ADR prediction, with the best model yielding 88.06% accuracy [53]. Additionally, pharmacogenomic platforms use AI to identify genetic markers linked to drug hypersensitivity or altered metabolism, enabling more personalized and safer prescribing [55].

AI tools also enhance the identification of complex DDIs, especially in polypharmacy cases. Using NLP to extract safety signals from literature and unstructured clinical notes, these systems can recommend alternative therapies or dose adjustments—often integrated within CDSS [53].

In signal detection, AI algorithms such as disproportionality analysis, Bayesian networks, and federated learning improve sensitivity and specificity. This is especially important for identifying rare or serious ADRs [53,54,55]. Moreover, real-time monitoring is enhanced through social media surveillance and wearable data analysis, expanding PV beyond formal healthcare settings [54,55].

Operationally, AI streamlines case processing by automating adverse event intake, mapping data to standard medical terminologies (e.g., MedDRA), and prioritizing high-risk reports using predictive scoring. These efficiencies reduce workload and improve response times for safety assessments [56].

8. AI in Patient Counselling and Education

Effective patient counselling and education are central to safe and successful therapy outcomes, especially in the management of chronic diseases. By ensuring that patients understand their medications, potential side effects, lifestyle modifications, and self-care strategies, healthcare professionals—particularly pharmacists—play a vital role in enhancing adherence and promoting health literacy [57].

The integration of AI into this domain has expanded the reach and scalability of counselling services. AI-powered chatbots and virtual assistants now deliver 24/7, personalized interactions that complement human-led consultations [58]. These tools use NLP to interpret patient queries, provide medication-related education, and offer behavior-change support tailored to individual needs [59].

AI platforms are very useful in educating patients with chronic diseases. They give condition-specific advice, such as lifestyle and diet tips for patients with diabetes [60]. AI also reduces extra administrative work. It can send reminders for appointments, medication refills, and answer common questions about drug use and side effects [58,61].

A notable advantage of AI in counselling is its integration with EHRs, which allows for real-time, context-specific interventions. For instance, AI assistants can deliver preoperative instructions, postoperative care reminders, or follow-up education based on the patient’s clinical status [62].

AI tools like smart pill bottles, ingestible sensors, and health apps support patient education. They track how patients take their medicines and give feedback. They also remind or guide patients to correct mistakes [63]. This makes counseling more interactive and based on real data.

The global burden of mental health conditions remains substantial, accounting for approximately 16% of the total disease burden. Depression and anxiety alone are estimated to cost the world economy roughly US$ 1 trillion annually in lost productivity. Persistent stigma and structural barriers further restrict access to timely, evidence-based care. Against this backdrop, the integration of AI into mental health services offers a pragmatic avenue to enhance screening, reduce treatment gaps, personalize interventions, and scale support—thereby helping to mitigate the burden and reshape the delivery of mental healthcare [64]. In this context, AI chatbots have also been employed to deliver basic psychological support, stress management techniques, and mindfulness coaching, especially in low-resource or remote settings. While not a substitute for professional therapy, such tools can enhance early support and triage [65,66].

By integrating AI into patient counselling and education, healthcare systems can offer continuous, personalized, and accurate support that complements clinician-led interactions and enhances patient engagement and outcomes. Figure 4 illustrates the key features and benefits of AI-powered chatbots in facilitating patient counselling and education.

9. AI in Drug Discovery and Development

Drug discovery and development is a long, expensive, and complex process that typically spans over a decade and costs billions of dollars. It includes multiple phases such as target identification, compound screening, preclinical studies, clinical trials, and regulatory approval [67]. AI is advancing this landscape by drastically improving the efficiency, accuracy, and cost-effectiveness of each stage, thus accelerating the timeline from discovery to market [68,69].

At the initial stage of identifying biological targets (such as genes, proteins, or pathways), AI utilizes ML, DL, and NLP to process genomic, proteomic, and clinical datasets to pinpoint the most promising targets [68]. BenevolentAI have successfully applied AI tools to discover novel drug targets for complex diseases such as Parkinson’s disease and Amyotrophic lateral sclerosis [70].

Once targets are identified, AI expedites drug design and optimization. Rather than relying solely on labor-intensive high-throughput screening, AI can generate, simulate, and refine potential drug molecules using generative models and molecular docking simulations [71]. Platforms like Insilico Medicine have demonstrated that AI can reduce drug design timelines from several years to mere months [68]. Furthermore, AI helps predict drug-target interactions and potential side effects using DL and in silico pharmacology, enabling early safety profiling of drug candidates [72].

In clinical development, AI streamlines trials by improving patient recruitment, designing adaptive protocols, and creating synthetic control arms to minimize the need for traditional placebo groups [73,74,75]. Tools like Deep 6 AI rapidly identify suitable candidates from EHRs, reducing trial recruitment timelines significantly [76]. Regulatory bodies such as the Food and Drug Administration (FDA) and European Medicines Agency (EMA) are also adopting AI to automate documentation analysis, assess real-world evidence, and enhance post-marketing surveillance [77].

Table 3. Applications for AI in drug discovery and development.

Stage	AI Application	Techniques/Tools	Example	References
Target Identification	Pinpoint disease-associated genes, proteins, pathways	ML, DL, NLP, Multi-omics Integration	BenevolentAI	[78,79]
Drug Design & Optimization	Design drug molecules with optimal properties	Generative AI, Molecular Docking, Quantum-AI Modeling	Insilico Medicine (IPF candidate in 18 months)	[79,80]
Interaction & Toxicity Prediction	Predict pharmacokinetics and adverse effects	DL, In Silico Pharmacology, Toxicology AI Models	IBM Watson for Drug Discovery	[72,81]
Clinical Trial Optimization	Enhance recruitment, reduce costs, and improve trial design	EHR Mining, Synthetic Control Arms, Adaptive Trial Designs	Deep 6 AI	[76]
Regulatory & Post-Market Surveillance	Streamline submissions and monitor real-world safety	NLP for Documentation, RWE Analytics, Automated Compliance	Bayer’s Regulatory AI Platform	[82,83]

summarizes the key applications of AI across various stages of drug discovery and development, highlighting the techniques used and notable real-world examples.

10. AI in Personalized Medicine

Artificial Intelligence is modernizing healthcare by advancing personalized medicine—an approach that moves away from generic treatment protocols toward therapies tailored to the unique characteristics of each patient. This evolution allows for more accurate, effective, and safer healthcare interventions by factoring in individual differences in genetics, medical history, lifestyle, and real-time clinical data [11,84].

AI algorithms analyze complex, multidimensional datasets to generate customized treatment recommendations [85]. By examining a patient’s diagnostic reports, genetic profile, and prior therapeutic responses, AI can help clinicians identify the most appropriate medications, optimize dosages, and adjust treatment regimens dynamically based on evolving patient data. A study conducted by Satheeskumar to investigate the application of convolutional neural network (CNN)–based AI model to enhance diagnostic accuracy and support personalized treatment planning in oral oncology. Leveraging a comprehensive multimodal dataset that integrated imaging, clinical, and histopathological variables, the system achieved 87% accuracy in predicting optimal therapy based on individual patient characteristics. In comparative analyses, AI-guided recommendations were associated with a 20% improvement in overall survival and a 15% extension in progression-free survival, highlighting the potential of AI to augment clinical decision-making in oral oncology [86].

Genomic data analysis is a key part of personalized care. AI helps read and understand large amounts of genetic data. It can group patients by genetic differences and find those at risk of certain diseases. AI can identify harmful genetic changes, study how genes are expressed, and predict how genes affect drug response. These insights support finding new drug targets and creating precision treatments for diseases like cancer and cardiovascular disorders [87,88].

By integrating AI with genomics and real-world clinical data, personalized medicine empowers healthcare providers to make better-informed, patient-centered decisions. This enhances treatment outcomes and minimizes adverse effects [89]. As AI technologies continue to advance, they are poised to further expand the scope and impact of personalized medicine across all areas of healthcare.

11. AI and Wearable Technologies in Medication Adherence

Chronic diseases are long-standing conditions that typically persist for life and impose a substantial burden of morbidity and mortality. They are the leading causes of death globally, accounting for approximately 71% of all annual deaths. Their prevalence of chronic diseases such as hypertension and diabetes mellitus continues to rise worldwide. Effective management requires robust, well-regulated health systems capable of delivering high-quality, evidence-informed care. Patients living with chronic diseases generally need sustained pharmacotherapy and ongoing monitoring to maintain control and prevent complications [90]. Medication adherence is a critical determinant of therapeutic success. Non-adherence contributes to disease progression, increased hospitalizations, and rising healthcare costs [91]. A multicenter, cross-sectional, non-interventional study conducted in Greece reported low medication adherence rates of 38.8% in patients with diabetes, 61.3% in those with hypertension, and 66.7% in those with hyperlipidemia [92].

AI-powered wearable technologies are emerging as valuable tools to improve adherence by enabling real-time monitoring, personalized reminders, and data-driven interventions [93,94]. Wearable devices—including smart pill bottles, fitness bands, smart patches, and ingestible sensors—support adherence by tracking medication intake patterns, sending timely alerts, and providing biometric feedback. These technologies, when integrated with AI algorithms, analyze adherence behavior and physiological data to detect non-compliance, predict risks, and guide personalized interventions [94,95,96,97,98].

AI virtual assistants and communication platforms further enhance adherence through customized scheduling, educational prompts, and two-way interaction between patients and providers [99]. These applications also contribute to patient engagement and are closely linked with remote patient monitoring (RPM) capabilities. In randomized controlled trials, AI-based interventions improved medication adherence by 6.7% to 32.7% compared to any intervention controls and current practices, respectively [100].

Table 4. AI and wearable technology applications in medication adherence.

Technology/Tool	Functionality	Examples/Features	References
Smart Pill Bottles/Dispensers	Track medication access and send reminders	Alarms, real-time alerts, EHR synchronization, automated dispensing	[95]
Smartwatches/Fitness Bands	Monitor adherence and physiological responses	Vibration alerts, biomarker tracking (e.g., BP, glucose), motion detection of intake	[94,96,97]
AI-Enabled Smart Patches	Ensure drug release and monitor compliance	Bluetooth data transmission, adherence logging, alternative to oral/injectable meds	[94,97]
Ingestible Sensors (Smart Pills)	Confirm ingestion and track medication absorption	Ingestion data transmission to app/wearables, used in psychiatry and cardiology	[101]
AI Virtual Assistants/Chatbots	Provide reminders, education, and real-time support	Voice/text alerts, Alexa/Google integration, habit-based scheduling	[99]
AI Communication Tools	Engage patients and provide follow-ups	Two-way SMS, app alerts, telehealth prompts, missed dose follow-ups	[64]
AI Monitoring Systems	Analyze behavior and confirm medication intake	Facial recognition, behavioral cues, provider alerts for non-compliance	[94]

summarizes the core technologies and their functions in supporting medication adherence across various care settings. Together, these innovations enable a proactive and patient-centered approach to chronic disease management.

12. AI in Remote Patient Monitoring and Telepharmacy

Remote Patient Monitoring and telepharmacy are rapidly advancing areas in healthcare, driven by the integration of AI and digital technologies. These innovations enable healthcare providers to deliver timely, continuous, and personalized care—particularly to patients with chronic diseases or those in underserved areas [15,16]. In the United States, patient adoption of telehealth services increased from 11% in 2019 to 46%, alongside a marked increase in provider-delivered telehealth visits. These trends underscore the health sector’s adaptability to digital care models and the growing acceptance of remote services among patients [102].

AI-enabled mental health technologies increasingly integrate natural language processing, speech analytics, and computer-vision–based affect recognition. For example, the conversational agent Woebot applies sentiment analysis to users’ text inputs to detect persistent expressions of sadness, hopelessness, or despair and then provides tailored guidance or recommends referral to professional care. Likewise, the telehealth platform Cogito analyzes prosodic features of patient speech (e.g., pitch, tone, and rhythm) during sessions and alerts clinicians to patterns suggestive of anxiety or depression. Complementing these modalities, Affectiva’s emotion-recognition tools quantify facial expressions and have been employed in mental health research to support early detection and ongoing monitoring [64].

12.1. RPM Applications Enhanced by AI

AI enables real-time analysis of health data collected from wearable devices such as smartwatches, ECG patches, and continuous glucose monitors. Machine learning algorithms detect anomalies—like irregular heart rhythms or sudden blood pressure changes—and trigger alerts for early intervention [103].

Predictive analytics, a core AI function, anticipates disease exacerbations (e.g., COPD flare-ups, glycemic instability) and supports early therapeutic modifications. These systems help clinicians move from reactive to proactive care by identifying at-risk patients before complications occur [104].

AI also personalizes treatment by integrating real-time data into dosing algorithms—for example, dynamically adjusting insulin levels based on glucose readings. This improves safety, reduces emergency visits, and enhances long-term disease control [105].

12.2. Telepharmacy Benefits Powered by AI

Telepharmacy platforms leverage AI to extend pharmacy services remotely. These include:

• Prescription Verification: AI tools such as NLP analyze digital prescriptions for errors, incorrect dosages, and potential drug interactions [106].
• Medication Adherence Monitoring: Smart devices and chatbots track medication intake and issue personalized reminders [100].
• Patient Counselling: AI-powered assistants provide 24/7 counselling support and automate frequently asked medication queries, complementing the pharmacist’s role [106].

In addition, AI enhances PV in telepharmacy settings. By analyzing EHRs and patient-reported data in real time, AI systems can detect emerging ADR patterns, supporting timely clinical decisions and intervention strategies [54].

12.3. Intelligent Dose Adjustment and Risk Mitigation

Another key contribution of AI is personalized dose calculation. Algorithms consider factors such as age, weight, renal function, and comorbidities to suggest optimal dosing regimens—especially for high-risk medications. This minimizes dosing errors and enhances safety [44]. Moreover, AI-driven systems can stratify patients by risk and prioritize pharmacist interventions where they are most needed, improving resource allocation and clinical efficiency [15].

The key AI-driven functions across RPM and telepharmacy are systematically summarized in

Table 5. Applications of AI in remote patient monitoring and telepharmacy.

Area	AI Application	Function/Example	References
Remote Patient Monitoring	Real-Time Health Data Analysis	Detects anomalies from wearables (e.g., arrhythmias, BP changes)	[107]
	Predictive Analytics	Forecasts disease exacerbations (e.g., COPD, glucose spikes)	[108]
	Automated Alerts & Interventions	Notifies providers/patients; suggests lifestyle changes or treatment adjustments	[109]
	Personalized Treatment Plans	AI-adjusted insulin/medication dosing based on real-time data	[110]
Telepharmacy	Drug Interaction Detection	Evaluates DDIs via AI tools (e.g., UpToDate®, Chatbots)	[20,21]
	Medication Adherence Monitoring	Tracks intake via smart devices	[111]
	Prescription Verification	Identifies errors using OCR/NLP in digital prescriptions	[112,113]
	Pharmacovigilance & ADR Monitoring	Analyzes EHRs and patient data for real-time ADR detection	[15,114]
	Personalized Dose Adjustment	Calculates dosages based on patient-specific factors	[115,116]

, showcasing how these technologies enhance clinical decision-making, medication safety, and personalized care delivery.

AI-enabled RPM and telepharmacy represent a major advancement in proactive, decentralized healthcare. These technologies support early intervention, reduce hospitalizations, improve medication safety, and extend high-quality care to remote or resource-limited settings. Their integration into pharmacy practice not only empowers patients but also enables pharmacists to deliver more precise and efficient care.

In addition to specific applications, Figure 5 highlights the broader patient-centered benefits of AI integration in remote healthcare delivery, including improved outcomes, enhanced accessibility, and reduced healthcare costs.

13. AI in Forecasting Disease Progression and Patient Outcomes

Artificial Intelligence is playing an increasingly pivotal role in forecasting disease trajectories and patient outcomes by leveraging large-scale clinical, genetic, behavioral, and real-time health data. Unlike traditional approaches, which rely heavily on retrospective analysis, AI enables proactive care planning by identifying at-risk individuals and anticipating health deterioration before symptoms escalate [61,116].

13.1. Predictive Models for Chronic Disease Management

AI-driven predictive models have demonstrated value in chronic conditions such as heart failure, COPD, and diabetes. These models can detect subtle trends in EHRs, wearable data, and lab results to forecast disease exacerbations and guide early interventions [117,118]. While similar capabilities are embedded within CDSS, forecasting tools operate on broader, population-level datasets to inform care prioritization and resource allocation [119]. For example, predictive algorithms can identify patients likely to experience hospitalization due to poor disease control and alert healthcare providers to intervene with preventive measures such as treatment optimization or behavioral support. These tools are especially useful for risk stratification in value-based care models.

13.2. Role in Personalized and Population Health

In the context of personalized medicine, AI forecasting complements individualized care by identifying response patterns to therapy, anticipating adverse effects, and enabling dynamic treatment adjustments [120]. At the population level, forecasting tools support public health decision-making by predicting disease trends, informing vaccination strategies, and allocating healthcare resources efficiently [121]. AI also contributes to early detection efforts. For instance, forecasting models can identify individuals at high risk of developing type 2 diabetes or cardiovascular disease years before clinical diagnosis, enabling lifestyle or pharmacological interventions during the preclinical stage [122].

13.3. Operational and Economic Implications

Beyond clinical benefits, AI forecasting has significant operational and economic advantages. By reducing emergency visits, preventing complications, and minimizing readmissions, these tools contribute to cost savings and improved system efficiency [123]. Reports estimate that AI-driven forecasting could save billions annually in avoidable healthcare expenditures, particularly in high-burden chronic conditions. A study found that following arthroplasty, multilingual chatbots reduced readmissions from 8.3% to 0% and emergency department visits from 8% to 0.9%. In lower-complexity settings, chatbot-mediated follow-up achieved efficacy comparable to manual workflows while reducing provider time by more than 90% [124]. In addition, AI enhances safety by supporting diagnostic accuracy and reducing treatment variability. However, the responsible deployment of these models requires ongoing validation, transparent algorithms, and ethical oversight.

14. Challenges and Ethical Considerations in the Integration of AI in Healthcare and Pharmacy

The integration of AI into healthcare and pharmacy practice presents transformative opportunities. However, it also introduces a complex set of challenges that must be critically addressed to ensure safe, equitable, and ethical implementation.

14.1. Data Privacy and Security

AI systems rely heavily on access to large datasets like EHRs, prescriptions, genetic profiles, and real-time monitoring. Using so much data creates concerns about privacy and security. Risks include data breaches, weak consent processes, and not following laws like Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR) [61,125]. A cross-sectional survey of pharmacy professionals across the Middle East and North Africa (MENA) region found that pharmacists worry about privacy (58.9%), cybersecurity (58.9%), job loss (62.9%), and lack of proper legal rules (67.0%) [126]. To solve these issues, healthcare organizations should use strong data protection methods. These include encryption, controlled access, data anonymization, and audit trails. Such steps ensure safe and ethical use of patient information in AI-based healthcare systems [127].

14.2. Integration into Clinical Workflows

Many healthcare and pharmacy systems continue to operate on legacy platforms that lack interoperability with modern AI tools. This results in several challenges, including technological incompatibility, disruption of existing clinical workflows, and resistance from healthcare professionals—often due to limited AI literacy [126]. Addressing these issues requires strategic investments in digital infrastructure, the adoption of standardized data exchange protocols, and ongoing professional training programs to ensure seamless, efficient, and sustainable integration of AI into routine pharmacy practice [128,129].

14.3. Regulatory and Legal Ambiguities

The regulatory landscape for AI in healthcare remains in a state of evolution, presenting several unresolved challenges. Key concerns include the absence of clear approval pathways for AI-based tools, ambiguity regarding the classification of AI as a medical device, and legal uncertainty in assigning accountability for clinical errors resulting from AI-driven decisions. In response to these issues, regulatory agencies such as the U.S. FDA and the EMA are working to establish adaptive and flexible frameworks suited to the dynamic nature of AI technologies [126,130]. Furthermore, continuous post-market surveillance and rigorous model auditing are critical, particularly for AI systems that evolve over time through ML processes [131].

14.4. Algorithmic Bias and Health Equity

AI systems trained on biased or non-representative datasets have the potential to exacerbate existing health disparities. This may lead to consequences such as misdiagnosis or misinterpretation of clinical data in underrepresented populations, as well as inaccurate dosing or inappropriate treatment recommendations for minority groups [132]. Healthcare professionals play a crucial role in mitigating such biases by critically evaluating AI-generated outputs, advocating for inclusive and diverse data collection during model development, and promoting fairness, transparency, and accountability in the deployment of AI technologies across healthcare settings [133].

14.5. Ethical Use and Professional Oversight

Ethical concerns in AI integration stem from the risk of over-reliance on automated systems at the expense of clinical judgment and patient autonomy. To uphold trust and ethical integrity in healthcare, it is essential that AI serves to augment—rather than replace—professional decision-making. Patients should be transparently informed when AI tools are involved in their care decisions, ensuring informed consent and shared decision-making [130]. Pharmacists, in particular, must take on the role of educators and stewards of responsible AI use, guiding both patients and interdisciplinary teams in the ethical application of these technologies [126].

Table 6. Key challenges and recommended strategies.

Challenge Area	Key Issues	Suggested Mitigations
Data Privacy & Security	Breaches, consent, regulatory compliance	Encryption, access control, data governance frameworks
Workflow Integration	Compatibility, resistance, training gaps	Infrastructure upgrades, AI literacy programs
Regulatory & Legal Gaps	Approval ambiguity, liability questions	Adaptive frameworks, post-market model surveillance
Algorithmic Bias	Unequal outcomes, underrepresentation	Inclusive datasets, pharmacist oversight, transparency
Ethical Oversight	Over-reliance, patient autonomy concerns	Clinical validation, disclosure, human-in-the-loop design

synthesizes the major challenges associated with AI integration in healthcare and pharmacy practice, along with recommended strategies to address each concern through regulatory, technological, and professional approaches.

15. Discussion

The integration of AI into pharmacy practice represents a transformative shift in healthcare delivery, enhancing pharmacovigilance, predictive analytics, personalized therapy, telepharmacy, and patient monitoring. Collectively, these systems enable data-driven decisions that improve medication safety, optimize therapeutic outcomes, and expand access to care. However, the effectiveness of each AI component depends on contextual and technical factors. Predictive analytics significantly aids ADR detection and disease progression forecasting, yet its reliability is closely tied to the diversity and representativeness of datasets [46]. Wearable technologies and smart devices improve medication adherence and generate valuable real-time feedback, though their impact is limited when patient engagement or data accuracy declines. Telepharmacy systems extend pharmaceutical care to underserved regions but can create workflow challenges or safety risks if clinicians over-rely on automated outputs without adequate oversight. From a computational perspective, machine learning models such as logistic regression and random forests offer efficiency and feasibility for real-time pharmacovigilance applications, whereas deep learning frameworks—particularly convolutional and recurrent neural networks—achieve superior performance in image-based medication identification and adherence tracking but demand higher processing capacity and larger training datasets [48]. Natural language processing models provide a practical balance between computational cost and interpretive accuracy in prescription verification and patient counseling. Consequently, the selection of AI models in pharmacy practice must balance predictive performance, computational complexity, and available digital infrastructure. Furthermore, real-world implementation challenges—including poor system interoperability, data quality issues, limited digital literacy among healthcare professionals, and high operational costs—often constrain large-scale deployment [81]. Addressing these limitations requires inclusive data collection, model validation, and sustained pharmacist oversight to ensure that AI tools complement rather than replace clinical judgment. By recognizing both the strengths and shortcomings of AI systems, pharmacy practice can advance toward safer, more efficient, and equitable patient-centered care [104].

16. Conclusions

The integration of AI into pharmacy practice represents a transformative advancement in modern healthcare. Across diverse domains—including drug interaction screening, clinical decision support, PV, personalized medicine, and automated dispensing—AI technologies are enhancing the quality, safety, and efficiency of pharmaceutical care. These tools enable more informed decision-making, support real-time interventions, and facilitate the shift from reactive to proactive healthcare delivery.

AI empowers pharmacists to deliver individualized therapies, monitor adherence, and optimize medication use by leveraging large-scale datasets and predictive models. It also strengthens remote healthcare services, such as telepharmacy and RPM, by extending the reach of pharmaceutical expertise beyond traditional settings. As pharmacy practice becomes increasingly data-driven, AI allows pharmacists to transition from operational roles to clinical collaborators, educators, and patient advocates.

AI has great potential in pharmacy, but there are challenges. Key concerns include data privacy, bias in algorithms, poor system compatibility, and unclear regulations. These problems can be managed with strong rules, ethical checks, and comprehensive validation frameworks. To use AI effectively, investment in digital systems is needed. Pharmacy education must be updated, and pharmacists need ongoing training. This will equip pharmacists with the skills needed to navigate AI-enabled care.

Looking ahead, the future of AI in pharmacy will depend on collaborative efforts among healthcare professionals, technology developers, educators, and policymakers. These partnerships must ensure that AI tools are not only innovative but also safe, equitable, and ethically aligned with the principles of patient-centered care. As stewards of medication safety and therapeutic optimization, pharmacists are uniquely positioned to lead the responsible adoption of AI and to shape its role in the evolving healthcare ecosystem.