INTRODUCTION:

Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently and analyze complex data. The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace. AI stands for Artificial Intelligence which is a set of technologies or machines to perform tasks that normally require human intelligence.

There are opportunities for AI to explore further in the field of pharmaceutical and healthcare research because of its ability to investigate enormous data from various modalities. Some of the current studies elaborate on the utilization of AI in healthcare and other sectors. AI focuses on producing intelligent modeling, which helps in imaging knowledge, cracking problems, and decision-making. Recently AI played an important role in various fields of pharmacy like drug discovery, drug delivery formulation, development, poly pharmacology, hospital pharmacy, etc.

The AI technologies in the healthcare industry include machine learning (ML), natural language processing (NLP), physical robots, robotic process automation, etc. In ML, neural network models and deep learning with various features are being applied to imaging data to identify clinically significant elements at the early stages, especially in cancer-related diagnoses¹. NLP uses computational techniques to comprehend human speech and derive its meaning. Lately, ML techniques are being widely incorporated in NLP for exploring unstructured data in the database and records in the form of doctor’s notes, lab reports, etc. by mapping the essential information from various imagery and textual data which helps in decision-making in diagnosis and treatment options.²

The ongoing disruptive innovation creates a pathway for the patients to receive a precise and rapid diagnosis and customized treatment interventions.⁴

AI-based solutions have been identified which include platforms that can make use of a variety of data types viz. symptoms reported by the patients, biometrics, imaging, biomarkers, etc. With the advancements in AI, the ability to detect potential illness well ahead is made possible, leading to a greater probability of prevention as an outcome of detection at a very early stage.

Physical robots are being used in various healthcare segments including nursing, telemedicine, cleaning, radiology, surgical, rehabilitation, etc. Robotic process automation uses technology, which is inexpensive, easy to program, and can perform structured digital tasks for administrative purposes and act like a semi-intelligent user of the systems. This can also be used in combination with image recognition. In the healthcare system, tasks such as preceding authorization, updating patient records, and billing, which are repetitive, can utilize this technology.

Artificial Intelligence has revolutionized the field of pharmacy by enhancing drug delivery, personalized medicine, and patient care. Its impact on the industry is profound, leading to more efficient processes and improved healthcare options.

AI use in pharmaceutical technology has increased over the years, and the use of technology can save time and money while providing a better understanding of the relationships between different formulations and process parameters. AI capabilities are rapidly evolving within healthcare, with both clinical and operational implications for pharmacy. Pharmacy uses AI to contribute to the overall healthcare industry, AI presents guidance on drug interactions, drug therapy monitoring, and drug formulary selections, there are many aspects of pharmacy that AI can have an impact on.

In Pharmacy, AI is called the pharmacy management system, housing patient utilization and drug data, it can identify drug-related problems through clinical decision support screening. AI can influence and shift our focus from dispensing medications toward providing a broader range of patient care services.³

AI is optimizing supply chains in pharmacies through various innovative applications that enhance efficiency, accuracy, and responsiveness.

AI algorithms analyze historical sales data, seasonal trends, and external factors (like health crises) to predict medication demand accurately. This capability allows pharmacies to adjust their inventory levels proactively, minimizing stockouts and overstock situations. For instance, AI-powered demand forecasting can improve accuracy by up to 40%, leading to significant reductions in inventory carrying costs and ensuring that essential medications are always available.⁵

AI automates inventory management by continuously monitoring stock levels and generating reorder alerts when supplies fall below predetermined thresholds. This real-time oversight helps pharmacies maintain optimal inventory levels, reducing the risk of expired medications and ensuring that high-demand items are consistently in stock.

AI tools can assess suppliers based on various criteria, including reliability, cost, and delivery speed. By analyzing historical performance data, AI can recommend the best suppliers for specific products, helping pharmacies maintain high-quality inventory while minimizing costs.

HISTORICAL BACKGROUND OF AI IN PHARMACY:

Evolution Of Pharmacy - The historical integration of AI in pharmacy dates to the early exploration of digital technologies in the healthcare sector, marking a significant shift in pharmaceutical practices.

Early Developments in AI for Pharmaceutical Research

· Early Research: AI has been used in pharmaceutical research for decades, initially to analyze large datasets and identify potential drug candidates.

Initial attempts to automate certain pharmaceutical tasks laid the foundation for the eventual incorporation of AI-driven systems into pharmacy operations.⁶

· Algorithm Evolution: Over time, AI algorithms have evolved, becoming more sophisticated and capable of handling complex tasks in drug discovery and development.

AI in healthcare is a technology that uses algorithms and software to approx. human condition in the analysis of complex medical data.

The primary aim of health-related AI applications is to analyze relationships between prevention or temperature techniques and patient outcomes. ⁷

· Pioneering Innovations: The emergence of AI in pharmacy was shaped by pioneering inventions and research, paving the way for its current applications and prospects.⁸

· Integration In Labs: Today, AI is integrated into pharmaceutical labs seamlessly, driving efficiency and accelerating the research process.⁹

Key Milestones in the Development of AI for Pharmacy:

· Early Experimentation: Initial trials and experiments to integrate AI into pharmaceutical processes, laying the groundwork for subsequent advancements.

· Breakthrough Innovations: Major milestones and breakthroughs that propelled AI technology into essential components of pharmacy practices and drug development.

· Industry adoption: The widespread adoption of AI-based solutions in pharmaceutical companies, making a significant shift in the industry’s approach to drug manufacturing and patient care.

AI In Disease Diagnosis:

Disease analysis becomes pivotal in designing a considerate treatment and safeguarding the wellness of patients. The inaccuracy generated by humans creates a hindrance to accurate diagnosis, as well as the misinterpretation of the generated information creating a dense and demanding task. AI can have varied applications by bringing about proper assurance of accuracy and efficiency. After a vivid literature survey, the applications of various technologies and methodologies for the purpose of disease diagnosis have been reported. With the evolution of the human population, there is always an ever-increasing demand for the healthcare system, according to varied environmental manifestations.

It is important to categorize the patients based on whether he/she is severely affected by the diseases, and AI can gain importance in diagnosis. Diagnosis refers to the state where, upon certain pre-existing problems, one’s condition is designated. It is always advised to maintain every patient’s health report form and to collect most reviews that are obtained via performing examinations and testing. Upon gathering information, the appropriate outcomes mainly concern the health care needs for a timely diagnosis. The analysis is the sole discretion of the state of the clinicians and may fluctuate.

There is an availability of multiple diagnostic strategies which leads to trust issues and thus, one needs to focus on AI for identification and determination of the early predictive stage of the disease more than the treatment or diagnostic phase. Such diagnosis can help to initiate early treatment, and initial treatment can bring noticeable changes in the patients as well as improved efficiency in AI modules.

Nowadays, identification, extraction, and catering to all the collated data would lead to ample technology usage based on deep learning, neural networking, and algorithms. Cancer and dementia are the two major diseases where AI has gained importance. Algorithms can never be biased if they are not self-generated or have never been associated with any existing data. For statistical supervision, a relevant and specific dataset is required. The acceptance lies not in the input from the user but in the salience of the identified clusters.

Hepatitis can be diagnosed through unsupervised learning. However, deep learning correlations can be obtained through various evolutionary changes and adjusting predictions. Usually, larger data sets and varied entries can serve the suitability of AI, but the outcome is incomprehensible. Among many examples of deep learning in diagnosis, one is the classification of dermatological diseases and atrial fibrillation detection. The usage of cross-validation can be used for random splitting into multiple sets for algorithm estimation. Accuracy, sensitivity, and specificity are three important aspects where the common measurements of AI focus. Many studies were performed for predictive modeling, which was noticeable for predicting early Parkinson’s disease.

The rib segmentation algorithm was developed using chest X-ray images for the diagnosis of lung diseases. Traditional methods are not useful in rib-wise segmentation of X-ray images due to various limitations. In this research, they have developed an algorithm via unpaired sample augmentation of chest X-ray images of pneumonia patients; later, a multi-scale network learns the features of images. The study reports that such an algorithm achieves good performance with better rib segmentation which could be useful in diagnosing lung cancers and other lung diseases.

Recently, algorithms and machine learning were used by the researchers in identification and classification of cardiac arrhythmia by processing the electrocardiogram signals. In another study, tuberculosis was classified and diagnosed by using the optimization genetic algorithm (GA) and support vector machine (SVM) classifier.¹⁰

AI In Drug Discovery and Development:

Numerous industries are striving to enhance their progress to meet the demands and expectations of their customers, utilizing various methodologies. The pharmaceutical industry is a critical field that plays a vital role in saving lives. It operates based on continuous innovation and the adoption of new technologies to address global healthcare challenges and respond to medical emergencies, such as the recent pandemic.

In the pharmaceutical industry, innovation is typically predicated on extensive research and development across various domains, including but not limited to manufacturing technology, packaging considerations, and customer-oriented marketing strategies.

Novel pharmaceutical innovations range from small drug molecules to biologics, with a preference for better stability with high potency to fulfill unmet needs to treat diseases. The assessment of the significant levels of toxicity associated with new drugs is an area of considerable concern, necessitating extensive research and exploration in the foreseeable future. One of the primary aims is to provide drug molecules that offer optimal benefits and suitability for utilization in the healthcare industry. Despite this, the pharmacy industry faces numerous obstacles that necessitate further advancement using technology-driven methods to address worldwide medical and healthcare demands. ¹¹

The need for a proficient workforce in the healthcare industry is persistent, necessitating the continuous provision of training to healthcare personnel to augment their involvement in routine duties. Identifying skill gaps in the workplace is a crucial undertaking within the pharmaceutical industry. It is imperative to effectively address the identified gaps through appropriate remedial measures while acknowledging that providing adequate training can also pose a significant challenge. As per a report presented by certain authorities, it has been observed that approximately 41% of supply chain disruptions occurred in June 2022.¹²

The report further highlights that supply chain disruption has emerged as the second most formidable challenge to overcome. Several pharmaceutical industries are anticipating further advancements in their supply chain, as well as innovative models to address these challenges, with the potential to enhance business resilience. The global outbreak of corona virus disease 2019 (COVID-19) has caused significant disruptions to various operations worldwide, including ongoing clinical trials.¹³

Pandemics, natural catastrophes, pricing changes, cyber attacks, logistical delays, and product issues increase supply chain disruptions. Transportation challenges caused by the epidemic have devastated the supply chain network and global industries.¹⁴

Decision-induced delays for price updates from suppliers owing to misunderstanding over whether to utilize the new price or the existing price for commodities or materials create price fluctuation delays. New obstacles arise from countries’ cross-border trade cooperation strategies, increasing criminal activity and instability in the availability of crucial resources for operation and production. The manufacturing of footprint modifications is needed to suit patient needs and compliance.

Within the pharmaceutical industry, a significant quantity of COVID-19 vaccines ended up being unusable during the pandemic because of complications related to the maintenance of the cold chain. The primary cause of supply chain disruption resulting from the delayed response can be attributed to insufficient innovation and imprecise forecasting in industrial and commercial operations. Supply chain disruptions within the pharmaceutical industry have significant ramifications on customer satisfaction, corporate reputation, and potential profits.

The implementation of AI is poised to bring about a significant transformation in the way the pharmaceutical industry handles supply chain operations (Figure 1).

Figure 1. AI addresses pharmaceutical challenges by optimizing workforce management, enhancing supply chain and clinical trial efficiency, and bolstering cyber security.

It also consolidates numerous AI research endeavors from recent decades to create effective solutions for diverse supply chain issues. Additionally, the study suggests potential research areas that could enhance decision-making tools for supply chain management in the future.

Drug discovery and development of new drugs is quite an expensive and competitive process for every pharmacy company. It is highly wired on data science and massive scientific and research data sets. Machine Learning in pharmacy rapidly increases the discovery of new molecules.¹⁵

Figure- 2 AI enhances drug development by optimizing nano system design, improving testing models, refining parameter selection, and studying drug interactions and permeation.

AI In Clinical Trials:

In drug discovery, clinical trials are the longest and require a huge amount of investment. Despite the time and capital invested in clinical trials, the success rate is only marginal for those that obtain approval from the Food Drug Administration (FDA). There are several bottlenecks in clinical trials, and those can lead to failure of the trial. Those bottlenecks include the insufficient number of participants, drop-outs during the trial, side effects of the test drug, or inconsistent data. If such failure occurs in late phases of clinical trials, such as in phase-III and phase-IV, the sponsor has to absorb an extremely high economic burden.¹⁶

The clinical trials which are associated with high costs also have subsequent effects on therapeutic costs for patients. Due to this reason, biopharma companies tie R&D costs of failed trials into the pricing of approved drugs to hold out the profit. The process of execution and conducting of clinical trials includes clinical trial design, patient recruitment/selection, site selection, monitoring, data collection and analysis. Out of these processes, patient recruitment and selection are the cumbersome process where 80% of the trials overshoot the enrolment timeline, and 30% of phase-III trials are prematurely terminated due to patient enrolment challenges. Trial monitoring in a multi-centered global trial is a very expensive and time-consuming process. Other challenges in clinical trials are the duration from the “last subject last visit” to data submission to regulatory agencies, which are huge data collection and analysis procedures. With the help of AI and digitization, these challenges in the clinical trial have been transforming.¹⁷

AI can research and cross-reference published scientific materials with alternative resources, including clinical trials results to develop drugs and discover new effective treatment methods for rare diseases.

· Virtual Screening- AI algorithms are used to rapidly screen and identify potential drug candidates, expediting the discovery process.

· Target Identification- Using Artificial Intelligence AI algorithms to analyze biological data and identify potential drug targets.

Artificial Intelligence AI aids in identifying potential drug targets within biological systems, streamlining the drug development process.

· Molecular Design- Advanced Artificial Intelligence AI algorithms are utilized to design new drug molecules with specific properties and therapeutic effects.

· Lead Generation- Employing AI to develop new drug candidates more efficiently.¹⁸

CURRENT PHARMACEUTICAL CHALLENGES AND THE ROLE OF AI

In the pharmaceutical industry, research on small molecules for better products and customer satisfaction is ongoing due to their multiple advantages. The chemical synthesis process is simple, while the synthetic derivative preparation is economical. Thus, many stable and potent small-molecule-loaded formulations are present in the pharmacy sector. Except for the treatment of rare diseases, many innovative small molecules face competition from generic molecules, and complex data are required for them to be launched, along with clinical trials.

These processes increase the economic pressure on companies to engage in more innovation. However, the biomolecular drug industry is still growing at a rapid pace to compensate for the crisis induced by the small molecular size and poor dissemination of research and innovations. Small-molecule actions are based on their conformation and reactivity.

Biomolecules, which are large units, mostly contain amino acids from the protein source along with nucleotides or ribonucleotides for the nucleic acid. Their stability and function are also influenced by the supramolecular sequence and the spatial conformation. Some biomolecules are very successful products, such as insulin and adalimumab. The pharmacokinetic aspects of these molecules are complex, as infusion is the preferred and most usable route of administration for these biomolecules. Pharmacokinetic modulation and molecular stabilization are important aspects of nucleic acid-based research. The pharmacokinetic exposure and enhancement of these molecular forms are crucial goals. ¹⁹

New technological advancement may be helpful to address these challenges and solve related issues. Although there is huge scope for AI in drug delivery innovation and drug discovery, it still presents some major limitations that ultimately require human interference or intellectuals to interpret the complex results. The major contributions of AI predictions are based on the datasets, but the interpretation of the results, owing to the gray zone, requires human interference to reach the appropriate conclusion. AI can experience issues with algorithm bias regarding the processing of information for predictions and the assessment of hypotheses. Moreover, it is not uncommon for docking simulations to result in the discovery of inactive molecules.

Therefore, a critical analysis of these parameters still requires human involvement for effective decision-making and cross-verifications, to rule out system bias issues. Nevertheless, there is huge potential in AI for possible application, and thus, extensive work may be able to reduce the limitations associated with AI and make it effective and reliable.^{20, 21}

In pharmaceutical product development, various AI models have been explored to enhance different aspects of the process. A list of commonly explored AI models in this domain is described in Figure 3.

AI In Drug Repurposing

AI enables the identification of existing drugs that can be repurposed for new medical indications, saving time and resources.

· Identification of New Indication: AI assists in finding new uses for existing drugs, expediting treatment discovery.

· Cost And Time Savings: Repurposing reduces the time and cost to bring a drug to market compared to traditional development.

· Enhanced Drug Safety: Artificial Intelligence AI helps in predicting potential adverse events and drug interactions.²²

Figure 3. Different supervised and unsupervised AI learning models/tools for pharmaceutical applications.

AI In Personalized Medicine and Precision Dosing

· Customized Treatments: Artificial Intelligence AI plays a crucial role in tailoring medical treatments to individual genetic profiles and patient characteristics.

· Dosing Optimization: By analyzing patient data, AI helps in determining the most precise and effective drug dosages for personalized care. AI algorithms determine optimal medication dosages based on individual patient characteristics, maximizing efficacy and safety.

· Genomic Analysis: AI facilitates in-depth genomic analysis to customize treatments based on an individual genetic makeup.²³

AI In Medication Management and Adherence:

· Predictive Adherence Models: AI-powered models predict patient adherence patterns, allowing for targeted interventions to improve medication compliance.

· Medication Reminders: AI-driven reminders and alerts aid patients in adhering to complex medication regimens, enhancing patient outcomes.²⁴

AI In Pharmacy Operations and Supply Chain Management:

· Inventory Management: AI systems optimize inventory levels, reducing wastage and ensuring consistent availability of pharmaceutical products.

· Supply Chain Efficiency: AI enhances logistical operations, improving the speed and accuracy of pharmaceutical supply chain management.

· Predictive Analytics: Advanced analytics powered by AI assist in forecasting demand and optimizing distribution networks.

AI In Pharmacy Automation and Robotics:

AI is revolutionizing pharmacy automation and robotics, paving the way for increased efficiency, accuracy and patient safety. From drug discovery to medication dispensing, AI is reshaping the pharmaceutical industry.

Benefits of AI in Pharmacy Automation

· Increased Efficiency: AI streamlines processes, reducing operational costs and optimizing workflow, leading to a more efficient pharmacy environment.

· Patient Safety: AI-driven systems minimize errors, ensuring accurate medication dispensing and enhancing patient safety.

· Drug Discovery: AI expedites the drug development process, accelerating the identification and development of new medications.

· Robotic Prescription Fulfillment: Robotic systems use AI to fill and label prescriptions, increasing efficiency and reducing manual labor.

· Inventory Management: AI-powered systems optimize medication inventory, ensuring timely restocking and reducing wastage.²⁵

Enhancing Patient Safety with AI:

· Medication Verification: AI systems verify prescriptions against patient records, ensuring correct medication, dosage, and patient information.

· Error Prevention: AI-enabled error detection reduces the risk of medication errors, enhancing patient safety and reducing adverse drug events.

· Real-Time Monitoring: AI monitors patient responses to medications, providing real-time data for healthcare professionals to optimize treatment. ²⁶

AI-Driven Robotic Systems for Compounding:

· Automated Compounding: Robotic systems ensure precise mixing and compounding of medications, improving dosage accuracy and quality control.²⁷

· Packaging Efficiency: AI-driven robotics automate packaging processes, enhancing speed and efficiency while maintaining medication integrity.²⁸

· Workflow Optimization: AI enhances compounding workflow, minimizing manual labor and reducing the risk of mix-ups or contamination.²⁹

AI Applications in Medication Dispensing

· Automated Dispensing: AI-powered systems accurately dispense medications, reducing human errors and ensuring precise dosing for patients.

· Inventory Management: AI optimizes inventory levels, minimizing stockouts and reducing wastage through predictive analytics and demand forecasting.³⁰

The Future of AI Pharmacy Automation:

· Personalized Medicine: AI will enable tailored treatments based on genetics, individual responses and specific health conditions, ushering in a new era of personalized medicine.²⁶

· Robotic Consultations: AI-Driven robotic systems will provide medication counseling, patient education and medication therapy management, improving healthcare accessibility.

· AI Ethical Considerations: The ethical implications of AI, including data privacy, algorithm bias and patient consent, will shape the future integration of AI in pharmacy practice.

Challenges in Implementing AI in Pharmacies:

· Integration Complexity: Seamlessly integrating AI systems with existing pharmacy workflows and software poses technical challenges and requires dedicated expertise.

· Regulatory Compliance: Pharmacy AI solutions must adhere to stringent regulatory requirements, ensuring patient privacy, security and compliance with industry standards.³¹

Case Studies of AI Integration:

· Automated Prescription Verification: Analyze cases where AI-powered systems successfully verify prescriptions, leading to reduced errors and improved patient care.

· AI-Based Inventory Optimization: Explore a case study highlighting AI’s impact on inventory management, resulting in minimized wastage and cost savings.³²

Current State of AI in Pharmacy:

· Advanced Technologies: State-of-the-art AI platforms and machine learning algorithms are being deployed to optimize various pharmacy functions and processes.

· Industry Adoption: Increasing acceptance and implementation of AI solutions across pharmaceutical companies and healthcare institutions worldwide.

· Regulatory Framework: Ongoing development of regulatory frameworks to govern the ethical and safe use of AI technology in pharmacy and healthcare.

CONCLUSION:

· AI integration is reshaping traditional pharmacy operations, optimizing efficiency and improving patient outcomes.

· Embracing AI fosters a culture of innovation, driving the development of advanced pharmaceutical technologies and patient care solutions.

· Successful AI implementation involves collaboration between pharmacists, technological experts and healthcare professionals to ensure seamless integration and optimized outcomes.

· The continuous evolution of AI in pharmacy promises to address current limitations and further enhance healthcare outcomes.

· The ethical use of AI in pharmacy remains a critical aspect, necessitating careful evaluation and guidance to uphold patient well-being.

· Addressing the limitations while leveraging the potential of AI presents a critical balance in pharmacy management.

· Embracing the potential of AI in pharmacy is essential for the advancement of the field and the well-being of patients worldwide.

· The future of AI in pharmacy looks promising, with ongoing research aimed at addressing existing gaps and enhancing the integration of AI tools into everyday pharmacy practice. As technology advances, pharmacists are likely to play an increasingly vital role in patient care through the effective use of AI, ultimately leading to improved health outcomes and more personalized healthcare services.

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Received on 17.02.2024 Revised on 03.07.2024

Accepted on 18.10.2024 Published on 18.12.2024

Available online on December 21, 2024

Asian J. Pharm. Tech. 2024; 14(4):386-394.