Artificial Intelligence (AI) in Pharmacy: An Overview of Innovations

The biomedical applications of artificial intelligence: an overview of decades of research

A significant area of computer science called artificial intelligence (AI) is successfully applied to the analysis of intricate biological data and the extraction of substantial associations from datasets for a variety of biomedical uses. AI has attracted significant interest in biomedical research due to its features: (i) better patient care through early diagnosis and detection; (ii) enhanced workflow; (iii) lowering medical errors; (v) lowering medical costs; (vi) reducing morbidity and mortality; (vii) enhancing performance; (viii) enhancing precision; and (ix) time efficiency. Quantitative metrics are crucial for evaluating AI implementations, providing insights, enabling informed decisions, and measuring the impact of AI-driven initiatives, thereby enhancing transparency, accountability, and overall impact. The implementation of AI in biomedical fields faces challenges such as ethical and privacy concerns, lack of awareness, technology unreliability, and professional liability. A brief discussion is given of the AI techniques, which include Virtual screening (VS), DL, ML, Hidden Markov models (HMMs), Neural networks (NNs), Generative models (GMs), Molecular dynamics (MD), and Structure-activity relationship (SAR) models. The study explores the application of AI in biomedical fields, highlighting its enhanced predictive accuracy, treatment efficacy, diagnostic efficiency, faster decision-making, personalised treatment strategies, and precise medical interventions.

Evaluating ChatGPT's Role in Supporting Military Readiness Assessment

INTRODUCTION

Joint Knowledge, Skills, and Abilities (JKSA) scoring of clinical workload data is a robust metric for military readiness assessment. However, the calculation of these scores requires a data analysis skillset that is not widely available. To address this gap, we developed a custom-generative pretrained transformer (GPT) model for JKSA scoring and compared performance to a "gold standard."

MATERIALS AND METHODS

To conduct the study, we utilized de-identified, clinical workload data from a single military treatment facility's military-civilian partnership program collected from January to December 2023. First, the data were divided into training, validation, and test datasets. Second, the custom GPT was trained for JKSA calculation on the training set. Then, it was refined using the validation set. Finally, the 3 authors used the GPT to independently calculate JKSA scores, patient encounters, procedure counts, and critical care encounters for the test set. The correlation coefficient (CC) was calculated to quantify the agreement between the GPT's scoring and scoring using traditional techniques within the SAS, version 9.4 environment.

RESULTS

The overall dataset contained information for 22,811 patient encounters performed by 40 providers in 8 critical wartime specialties. The GPT-calculated diagnostic (CC = 0.92, P < .001) and procedural (CC = 0.76, P < .001) JKSA scores were significantly correlated with those from standard processes. Similarly, GPT-calculated patient encounters (CC = 0.98, P< .001), procedures (CC = 1.00, P < .001), and critical care encounters (CC = 1.00, P < .001) were significantly correlated with SAS calculations. When using the GPT model, we identified key lessons learned for data management, prompt engineering, and cross-checking to facilitate the model's success.

CONCLUSIONS

The custom-GPT model proved an accurate method to calculate JKSA scores from clinical workload data to support military readiness assessment. This project represents a step forward in making the JKSA metric more widely accessible. Further research is required to test performance among new, less homogenous datasets and additional users.

Artificial intelligence, pharmaceutical development and dual-use research of concern: a call to action

Fervent attention was paid to what is coined dual-use research (DUR), or research that can both benefit and harm humanity, and dual-use research of concern (DURC), a particular subset of DUR that is reasonably anticipated to be a safety and security concern if misapplied. The aim of this paper is not to reiterate the challenges of DURC governance but to look at a new turn in DURC, namely the challenges posed by the use of artificial intelligence (AI) in pharmaceutical development. This is important, as AI is increasingly being used for pharmaceutical development in the industry. There is growing recognition that AI is DURC, and there is a dearth of industry and governmental guidance.

Applications of artificial intelligence in current pharmacy practice: A scoping review

BACKGROUND

Artificial intelligence (AI), a branch of computer science, has been of growing research interest since its introduction to healthcare disciplines in the 1970s. Research has demonstrated that the application of such technologies has allowed for greater task accuracy and efficiency in medical disciplines such as diagnostics, treatment protocols and clinical decision-making. Application in pharmacy practice is reportedly narrower in scope; with greater emphasis placed on stock management and day-to-day function optimisation than enhancing patient outcomes. Despite this, new studies are underway to explore how AI technologies may be utilised in areas such as pharmacist interventions, medication adherence, and personalised medicine. Objective/s: The aim of this study was to identify current use of AI in measuring performance outcomes in pharmacy practice.

METHODS

A scoping review was conducted in accordance with PRISMA Extension for Scoping Reviews (PRISMA-ScR). A comprehensive literature search was conducted in MEDLINE, Embase, IPA (International Pharmaceutical Abstracts), and Web of Science databases for articles published between January 1, 2018 to September 11, 2023, relevant to the aim. The final search strategy included the following terms: ("artificial intelligence") AND ("pharmacy" OR "pharmacist" OR "pharmaceutical service" OR "pharmacy service"). Reference lists of identified review articles were also screened.

RESULTS

The literature search identified 560 studies, of which seven met the inclusion criteria. These studies described the use of AI in pharmacy practice. All seven studies utilised models derived from machine learning AI techniques. AI identification of prescriptions requiring pharmacist intervention was the most frequent (n = 4), followed by screening services (n = 2), and patient-facing mobile applications (n = 1). These results indicated a workflow- and productivity-focused application of AI within current pharmacy practice, with minimal intention for direct patient health outcome improvement. Despite this, the review also revealed AI's potential in data collation and analytics to aid in pharmacist contribution towards the healthcare team and improvement of health outcomes.

CONCLUSIONS

This scoping review has identified, from the literature available, three main areas of focus, (1) identification and classification of atypical or inappropriate medication orders, (2) improving efficiency of mass screening services, and (3) improving adherence and quality use of medicines. It also identified gaps in AI's current utility within the profession and its potential for day-to-day practice, as our understanding of general AI techniques continues to advance.

Visionary Health Architects and Leaders: The Strategic Role of Medical Advisors in Modern Pharma

This paper reviews the pivotal role of the Medical Advisor (MA) within the medical department of the pharmaceutical industry, highlighting their essential contribution to the development, commercialization, and appropriate use of medicines. In an environment characterised by stringent regulations and increasing demands for transparency, the MA role has become an indispensable position for ensuring optimal integration between scientific innovation and commercial strategy. The work of MAs facilitates cross-functional collaboration between a board spectrum of internal teams and external stakeholders, ensuring that scientific and clinical knowledge is effectively integrated at every stage of the product lifecycle. This includes leveraging emerging new technologies such as artificial intelligence and digital health solutions, as well as using machine learning to enhance predictive analytics. Such integration is critical to addressing unmet medical needs while aligning these initiatives with broader business objectives to drive innovation, market competitiveness, and patient outcomes. We explore the key responsibilities of today's MAs, which include contributing to the generation of data through clinical trials and post-authorization studies, providing continuing medical education, and communicating accurate information to diverse audiences, including healthcare providers, regulators, and patients. Furthermore, MAs promote innovation and therapeutic progress, acting as guardians of medical ethics. This review aims to provide a comprehensive understanding of the strategic role of MAs in bridging the gap between scientific research and clinical practice. Their contributions are critical to addressing current industry challenges, ensuring that companies remain competitive and making a significant contribution to patient wellbeing and medical progress.

Assessment of Automation Models in Hospital Pharmacy: Systematic Review of Technologies, Practices, and Clinical Impacts

Medication management in hospitals is a complex process that encompasses every step from prescription to administration, involving multiple healthcare professionals. This process is prone to various errors that can compromise patient safety and generate significant human and financial costs. Automation in hospital pharmacies represents a major advancement, enhancing patient safety, optimizing professional practices, and reducing hospital expenses. This study aims to analyze the different types of automation systems used in hospital pharmacies, assess the impact of automation, and explore its benefits as well as the challenges and limitations associated with its implementation. A literature search was conducted using the ScienceDirect, PubMed, and Scopus databases, covering the period from 1992 to 2024. A total of 129 relevant articles related to the automation of medication preparation and distribution, as well as its challenges and perspectives were included in this study. Automated technologies significantly contribute to reducing medication errors, strengthening traceability, optimizing inventory management, and alleviating the workload of healthcare professionals. However, challenges persist, particularly in terms of costs, integration with existing processes, and staff training. The use of artificial intelligence offers promising prospects for improving the accuracy and operational efficiency of automation systems.

Pharmacists' perceptions of artificial intelligence: A national survey

BACKGROUND

Artificial intelligence (AI) is a rapidly growing and evolving field impacting pharmacy research, education, and professional practice. The development and adaptation of AI technologies have the potential to radically shape the future of the pharmacy profession. However, it remains unclear how aware pharmacists are of these technologies or their perceptions regarding current and future utilization.

OBJECTIVE

The purpose of this study is to describe the perceptions and awareness of artificial intelligence technologies in a population of practicing pharmacists across the United States.

METHODS

A 19-question electronic survey was administered via Qualtrics to assess various AI perceptions among U.S. pharmacists. The survey ran from September 5th to November 22nd, 2023 and targeted practicing pharmacists through professional organizations and publicly available email lists. Responses were analyzed for descriptive trends and demographic analysis focusing on factors predicting AI use and were categorized into sub-focuses for detailed analysis.

RESULTS

A total of 1363 practicing pharmacists completed the survey. 82.5% of respondents expressed some degree of familiarity with AI software, but only 38.7% reported having used AI. Of those using AI software, the most common applications were Large Language Models (33.7%) and Image Generation (10%). 56.1% of pharmacists feel that AI will decrease the number of pharmacy jobs, and 34.9% of pharmacists express some degree of distrust of AI. Despite this, 64.1% of pharmacists feel that AI could enhance their professional effectiveness and productivity. Males appear much more likely than females to use AI (50.1% Vs. 31%, P < .001). Younger responders also reported higher AI utilization with the highest utilization aged 23-29 (47.5%) and lowest in 60+ (25.6%, P < .001) CONCLUSION: Understanding pharmacists' current awareness, concerns, and perspectives on AI is crucial for navigating its potential impact on the profession including potential professional utilization, addressing concerns regarding job security, ethical considerations, and regulatory uncertainty.

The impact of artificial intelligence on the knowledge, attitude, and practice of pharmacists across diverse settings: A cross-sectional study

The pharmacy practice landscape is undergoing a significant transformation with the increasing integration of artificial intelligence (AI). As essential members of the healthcare team, pharmacists' readiness and willingness to adopt AI technologies is critical. This cross-sectional study explores pharmacists' knowledge, attitudes, and practices (KAP) regarding AI in various practice settings. Utilizing a descriptive survey methodology, we collected data through a structured questionnaire targeting pharmacists across diverse working environments. Statistical analyses were conducted to calculate KAP scores. Results revealed that 44.8 % of participants possessed a moderate level of knowledge about AI, while 49.1 % expressed positive attitudes toward its potential applications in pharmacy. However, their current practices related to AI were rated as adequate (57.3 %). Notably, a significant association was found between knowledge, attitudes, and practices (p < 0.001). This study provides valuable insights into pharmacists' readiness to incorporate AI into their practice, emphasizing the need for targeted educational interventions to enhance knowledge and promote positive attitudes. Furthermore, efforts must be directed towards facilitating the integration of AI into pharmacy workflows to fully leverage this transformative technology and improve patient care outcomes.

Students' perception of the use of artificial intelligence (AI) in pharmacy school

INTRODUCTION

The increasing adoption of artificial intelligence (AI) among college students, particularly in pharmacy education, raises ethical concerns and prompts debates on responsible usage. The promise of the potential to reduce workload is met with concerns of accuracy issues, algorithmic bias, and the lack of AI education and training. This study aims to understand pharmacy students' perspectives on the use of AI in pharmacy education.

METHODS

This study used an anonymous 14-question survey distributed among second, third, and fourth-year pharmacy students at four schools of pharmacy in the United States.

RESULTS

A total of 171 responses were analyzed. Demographic information included institution, class identification (P2, P3, P4), and age range. Regarding the use of AI, 43% of respondents were unaware of limitations of AI tools. Many respondents (45%) had used AI tools to complete assignments, while 42% considered it academic dishonesty. Fifty-six percent believed AI tools could be used ethically. Student perspectives on AI were varied but many expressed that it will be integral to pharmacy education and future practice.

CONCLUSIONS

This study highlights the nuances of AI usage among pharmacy students. Despite limited education and training on AI, students utilized tools for various tasks. This survey provides evidence that pharmacy students are exploring the use of AI and would likely benefit from education on using AI as a supplement to critical thinking.

Student Pharmacists' Perceptions of Artificial Intelligence and Machine Learning in Pharmacy Practice and Pharmacy Education

OBJECTIVE

This study explored student pharmacists' perceptions and attitudes regarding artificial intelligence (AI) and machine learning (ML) in pharmacy practice. Due to AI/ML's promising prospects, understanding students' current awareness, comprehension, and hopes for their use in this field is essential.

METHODS

In April 2024, a Zoom focus group discussion was conducted with 6 student pharmacists using a self-developed interview guide. The guide included questions about the benefits, challenges, and ethical considerations of implementing AI/ML in pharmacy practice and education. The participants' demographic information was collected through a questionnaire. The research team conducted a thematic analysis of the discussion transcript. The results generated by a team member using NVivo were compared with those generated by ChatGPT, and all discrepancies were addressed.

RESULTS

Student pharmacists displayed a generally positive attitude toward the implementation of AI/ML in pharmacy practice but lacked knowledge about AI/ML applications. Participants recognized several advantages of AI/ML implementation in pharmacy practice, including improved accuracy and time-saving for pharmacists. Some identified challenges were alert fatigue, AI/ML-generated errors, and the potential obstacle to person-centered care. The study participants expressed their interest in learning about AI/ML and their desire to integrate these technologies into pharmacy education.

CONCLUSION

The demand for integrating AI/ML into pharmacy practice is increasing. Student and professional pharmacists need additional AI/ML training to equip them with knowledge and practical skills. Collaboration between pharmacists, institutions, and AI/ML companies is essential to address barriers and advance AI/ML implementation in the pharmacy field.

Artificial intelligence in community pharmacy practice: Pharmacists' perceptions, willingness to utilize, and barriers to implementation

Background

Artificial intelligence (AI) has a significant potential to impact pharmacy practices worldwide. This study investigates pharmacists' perceptions of AI's role in pharmacy practices, their willingness to adopt it, and perceived barriers to its implementation at community pharmacies in Ethiopia.

Methods

A cross-sectional study was conducted among community pharmacists in Ethiopia. Data were collected using a self-administered questionnaire. Independent samples t-test, one-way ANOVA, and post-hoc analyses were used to compare pharmacists' perception and willingness scores. A linear regression analysis examined the association of independent variables with pharmacists' perception of AI and willingness to utilize AI. A p-value <0.05 was considered statistically significant.

Results

Of 241 pharmacists approached, 225 (93.3 %) completed the survey. Overall, about two-thirds (67.1 % and 66.2 %) of community pharmacists had a high level of perception and willingness to use AI applications in pharmacy, respectively. Pharmacists with bachelor's degrees and above (β = 2.76: 95 % CI: 0.09, 5.01 vs. β = 1.79: 95 % CI: 0.05, 4.21), those who utilized scientific drug information sources (β = 2.45, 95 %: 0.17, 4.45 vs. β = 1.76, 95 % CI: 0.91, 3.89), pharmacists who had a previous exposure of AI (β = 1.02, 95 %: 0.03, 3.24 vs. β =1.13, 95 % CI: 0.07, 2.93), and those who with higher perceived AI knowledge (β =1.09, 95 % CI: 0.02, 2.46 vs. β = 1.14, 95 %CI: 0.17, 3.11) had significantly higher perception of AI and willingness to utilize it, respectively compared to their counterparts. Lack of internet availability (89.3 %), lack of AI-related software/hardware (88.2 %), and limited training (80.9 %) were the most frequently reported barriers by pharmacists to AI adoption. Over 90 % of pharmacists agreed on the importance of internet availability (93.3 %), policies/frameworks (91.6 %), and research/learning from others (89.3 %) for successful AI integration.

Conclusion

Despite positive perceptions and willingness from pharmacists, AI implementation in community pharmacies could be hindered by resource limitations, training gaps, skill constraints, and infrastructure issues. To facilitate adoption, enhancing knowledge and skills, and developing policies/frameworks are crucial.

Artificial intelligence, medications, pharmacogenomics, and ethics

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing various scientific and clinical disciplines including pharmacogenomics (PGx) by enabling the analysis of complex datasets and the development of predictive models. The integration of AI and ML with PGx has the potential to provide more precise, data-driven insights into new drug targets, drug efficacy, drug selection, and risk of adverse events. While significant effort to develop and validate these tools remain, ongoing advancements in AI technologies, coupled with improvements in data quality and depth is anticipated to drive the transition of these tools into clinical practice and delivery of individualized treatments and improved patient outcomes. The successful development and integration of AI-assisted PGx tools will require careful consideration of ethical, legal, and social issues (ELSI) in research and clinical practice. This paper explores the intersection of PGx with AI, highlighting current research and potential clinical applications, and ELSI including privacy, oversight, patient and provider knowledge and acceptance, and the impact on patient-provider relationship and new roles.

Gender and ethnicity bias in generative artificial intelligence text-to-image depiction of pharmacists

INTRODUCTION

In Australia, 64% of pharmacists are women but continue to be under-represented. Generative artificial intelligence (AI) is potentially transformative but also has the potential for errors, misrepresentations, and bias. Generative AI text-to-image production using DALL-E 3 (OpenAI) is readily accessible and user-friendly but may reinforce gender and ethnicity biases.

METHODS

In March 2024, DALL-E 3 was utilized to generate individual and group images of Australian pharmacists. Collectively, 40 images were produced with DALL-E 3 for evaluation of which 30 were individual characters and the remaining 10 images were comprised of multiple characters (N = 155). All images were independently analysed by two reviewers for apparent gender, age, ethnicity, skin tone, and body habitus. Discrepancies in responses were resolved by third-observer consensus.

RESULTS

Collectively for DALL-E 3, 69.7% of pharmacists were depicted as men, 29.7% as women, 93.5% as a light skin tone, 6.5% as mid skin tone, and 0% as dark skin tone. The gender distribution was a statistically significant variation from that of actual Australian pharmacists (P < .001). Among the images of individual pharmacists, DALL-E 3 generated 100% as men and 100% were light skin tone.

CONCLUSIONS

This evaluation reveals the gender and ethnicity bias associated with generative AI text-to-image generation using DALL-E 3 among Australian pharmacists. Generated images have a disproportionately high representation of white men as pharmacists which is not representative of the diversity of pharmacists in Australia today.

Generative artificial intelligence (Gen-AI) in pharmacy education: Utilization and implications for academic integrity: A scoping review

Introduction

Generative artificial intelligence (Gen-AI), exemplified by the widely adopted ChatGPT, has garnered significant attention in recent years. Its application spans various health education domains, including pharmacy, where its potential benefits and drawbacks have become increasingly apparent. Despite the growing adoption of Gen-AIsuch as ChatGPT in pharmacy education, there remains a critical need to assess and mitigate associated risks. This review exploresthe literature and potential strategies for mitigating risks associated with the integration of Gen-AI in pharmacy education.

Aim

To conduct a scoping review to identify implications of Gen-AI in pharmacy education, identify its use and emerging evidence, with a particular focus on strategies which mitigate potential risks to academic integrity.

Methods

A scoping review strategy was employed in accordance with the PRISMA-ScR guidelines. Databases searched includedPubMed, ERIC [Education Resources Information Center], Scopus and ProQuestfrom August 2023 to 20 February 2024 and included all relevant records from 1 January 2000 to 20 February 2024 relating specifically to LLM use within pharmacy education. A grey literature search was also conducted due to the emerging nature of this topic. Policies, procedures, and documents from institutions such as universities and colleges, including standards, guidelines, and policy documents, were hand searched and reviewed in their most updated form. These documents were not published in the scientific literature or indexed in academic search engines.

Results

Articles (n = 12) were derived from the scientific data bases and Records (n = 9) derived from the grey literature. Potential use and benefits of Gen-AI within pharmacy education were identified in all included published articles however there was a paucity of published articles related the degree of consideration to the potential risks to academic integrity. Grey literature recordsheld the largest proportion of risk mitigation strategies largely focusing on increased academic and student education and training relating to the ethical use of Gen-AI as well considerations for redesigning of current assessments likely to be a risk for Gen-AI use to academic integrity.

Conclusion

Drawing upon existing literature, this review highlights the importance of evidence-based approaches to address the challenges posed by Gen-AI such as ChatGPT in pharmacy education settings. Additionally, whilst mitigation strategies are suggested, primarily drawn from the grey literature, there is a paucity of traditionally published scientific literature outlining strategies for the practical and ethical implementation of Gen-AI within pharmacy education. Further research related to the responsible and ethical use of Gen-AIin pharmacy curricula; and studies related to strategies adopted to mitigate risks to academic integrity would be beneficial.

Advancing Pharmacy Practice: The Role of Intelligence-Driven Pharmacy Practice and the Emergence of Pharmacointelligence

The field of healthcare is experiencing a significant transformation driven by technological advancements, scientific breakthroughs, and a focus on personalized patient care. At the forefront of this evolution is artificial intelligence-driven pharmacy practice (IDPP), which integrates data science and technology to enhance pharmacists' capabilities. This prospective article introduces the concept of "pharmacointelligence", a paradigm shift that synergizes artificial intelligence (AI), data integration, clinical decision support systems (CDSS), and pharmacy informatics to optimize medication-related processes. Through a comprehensive literature review and analysis, this research highlights the potential of pharmacointelligence to revolutionize pharmacy practice by addressing the complexity of pharmaceutical data, changing healthcare demands, and technological advancements. This article identifies the critical need for integrating these technologies to enhance medication management, improve patient outcomes, and streamline pharmacy operations. It also underscores the importance of regulatory and ethical considerations in implementing pharmacointelligence, ensuring patient privacy, data security, and equitable healthcare delivery.

The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review

Currently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) are gaining increased interest in many fields, particularly in pharmaceutical research and development, where they assist in decision-making in complex situations. Numerous research studies and advancements have demonstrated how these computational technologies are used in various pharmaceutical research and development aspects, including drug discovery, personalized medicine, drug formulation, optimization, predictions, drug interactions, pharmacokinetics/ pharmacodynamics, quality control/quality assurance, and manufacturing processes. Using advanced modeling techniques, these computational technologies can enhance efficiency and accuracy, handle complex data, and facilitate novel discoveries within minutes. Furthermore, these technologies offer several advantages over conventional statistics. They allow for pattern recognition from complex datasets, and the models, typically developed from data-driven algorithms, can predict a given outcome (model output) from a set of features (model inputs). Additionally, this review discusses emerging trends and provides perspectives on the application of AI with quality by design (QbD) and the future role of AI in this field. Ethical and regulatory considerations associated with integrating AI into pharmaceutical technology were also examined. This review aims to offer insights to researchers, professionals, and others on the current state of AI applications in pharmaceutical research and development and their potential role in the future of research and the era of pharmaceutical Industry 4.0 and 5.0.

[Translated article] Introducing artificial intelligence to hospital pharmacy departments

Artificial intelligence is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, Artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks, or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. Artificial intelligence has been introduced in biomedicine, accelerating processes, improving accuracy and efficiency, and improving patient care. By using Artificial intelligence algorithms and machine learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. Artificial intelligence integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master Artificial intelligence will play a crucial role in this transformation.

Approaching artificial intelligence to Hospital Pharmacy

Artificial intelligence (AI) is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. AI has been introduced in biomedicine, accelerating processes, improving safety and efficiency, and improving patient care. By using AI algorithms and Machine Learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. AI integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master AI will play a crucial role in this transformation.

Ethical considerations and concerns in the implementation of AI in pharmacy practice: a cross-sectional study

BACKGROUND

Integrating artificial intelligence (AI) into healthcare has raised significant ethical concerns. In pharmacy practice, AI offers promising advances but also poses ethical challenges.

METHODS

A cross-sectional study was conducted in countries from the Middle East and North Africa (MENA) region on 501 pharmacy professionals. A 12-item online questionnaire assessed ethical concerns related to the adoption of AI in pharmacy practice. Demographic factors associated with ethical concerns were analyzed via SPSS v.27 software using appropriate statistical tests.

RESULTS

Participants expressed concerns about patient data privacy (58.9%), cybersecurity threats (58.9%), potential job displacement (62.9%), and lack of legal regulation (67.0%). Tech-savviness and basic AI understanding were correlated with higher concern scores (p < 0.001). Ethical implications include the need for informed consent, beneficence, justice, and transparency in the use of AI.

CONCLUSION

The findings emphasize the importance of ethical guidelines, education, and patient autonomy in adopting AI. Collaboration, data privacy, and equitable access are crucial to the responsible use of AI in pharmacy practice.

The digital transformation in pharmacy: embracing online platforms and the cosmeceutical paradigm shift

In the face of rapid technological advancement, the pharmacy sector is undergoing a significant digital transformation. This review explores the transformative impact of digitalization in the global pharmacy sector. We illustrated how advancements in technologies like artificial intelligence, blockchain, and online platforms are reshaping pharmacy services and education. The paper provides a comprehensive overview of the growth of online pharmacy platforms and the pivotal role of telepharmacy and telehealth during the COVID-19 pandemic. Additionally, it discusses the burgeoning cosmeceutical market within online pharmacies, the regulatory challenges faced globally, and the private sector's influence on healthcare technology. Conclusively, the paper highlights future trends and technological innovations, underscoring the dynamic evolution of the pharmacy landscape in response to digital transformation.

Perspectives of Pharmacy Students on Ethical Issues Related to Artificial Intelligence: A Comprehensive Survey Study

Background

The integration of artificial intelligence (AI) into pharmacy education and practice holds the potential to advance learning experiences and prepare future pharmacists for evolving healthcare practice. However, it also raises ethical considerations that need to be addressed carefully. This study aimed to explore pharmacy students' attitudes regarding AI integration into pharmacy education and practice.

Methods

A cross-sectional design was employed, utilizing a validated online questionnaire administered to 702 pharmacy students from diverse demographic backgrounds. The questionnaire gathered data on participants' attitudes and concerns regarding AI integration, as well as demographic information and factors influencing their attitudes.

Results

Most participants were female students (72.8%), from public universities (55.6%) and not working (64.2%). Participants expressed a generally negative attitude toward AI integration, citing concerns and barriers such as patient data privacy (62.0%), susceptibility to hacking (56.2%), potential job displacement (69.3%), cost limitations (66.8%), access (69.1%) and the absence of regulations (48.1% agree), training (70.4%), physicians' reluctance (65.1%) and patient apprehension (70.8%). Factors including country of residence, academic year, cumulative GPA, work status, technology literacy, and AI understanding significantly influenced participants' attitudes (p < 0.05).

Conclusion

The study highlights the need for comprehensive AI education in pharmacy curricula including related ethical concerns. Addressing students' concerns is crucial to ensuring ethical, equitable, and beneficial AI integration in pharmacy education and practice.

International pharmacy students' perceptions towards artificial intelligence in medicine-A multinational, multicentre cross-sectional study

AIMS

To explore international undergraduate pharmacy students' views on integrating artificial intelligence (AI) into pharmacy education and practice.

METHODS

This cross-sectional institutional review board-approved multinational, multicentre study comprised an anonymous online survey of 14 multiple-choice items to assess pharmacy students' preferences for AI events in the pharmacy curriculum, the current state of AI education, and students' AI knowledge and attitudes towards using AI in the pharmacy profession, supplemented by 8 demographic queries. Subgroup analyses were performed considering sex, study year, tech-savviness, and prior AI knowledge and AI events in the curriculum using the Mann-Whitney U-test. Variances were reported for responses in Likert scale format.

RESULTS

The survey gathered 387 pharmacy student opinions across 16 faculties and 12 countries. Students showed predominantly positive attitudes towards AI in medicine (58%, n = 225) and expressed a strong desire for more AI education (72%, n = 276). However, they reported limited general knowledge of AI (63%, n = 242) and felt inadequately prepared to use AI in their future careers (51%, n = 197). Male students showed more positive attitudes towards increasing efficiency through AI (P = .011), while tech-savvy and advanced-year students expressed heightened concerns about potential legal and ethical issues related to AI (P < .001/P = .025, respectively). Students who had AI courses as part of their studies reported better AI knowledge (P < .001) and felt more prepared to apply it professionally (P < .001).

CONCLUSIONS

Our findings underline the generally positive attitude of international pharmacy students towards AI application in medicine and highlight the necessity for a greater emphasis on AI education within pharmacy curricula.

Knowledge, attitude and practice among pharmacy students and faculty members towards artificial intelligence in pharmacy practice: A multinational cross-sectional study

BACKGROUND

Modern patient care depends on the continuous improvement of community and clinical pharmacy services, and artificial intelligence (AI) has the potential to play a key role in this evolution. Although AI has been increasingly implemented in various fields of pharmacy, little is known about the knowledge, attitudes, and practices (KAP) of pharmacy students and faculty members towards this technology.

OBJECTIVES

The primary objective of this study was to investigate the KAP of pharmacy students and faculty members regarding AI in six countries in the Middle East as well as to identify the predictive factors behind the understanding of the principles and practical applications of AI in healthcare processes.

MATERIAL AND METHODS

This study was a descriptive cross-sectional survey. A total of 875 pharmacy students and faculty members in the faculty of pharmacy in Jordan, Palestine, Lebanon, Egypt, Saudi Arabia, and Libya participated in the study. Data was collected through an online electronic questionnaire. The data collected included information about socio-demographics, understanding of AI basic principles, participants' attitudes toward AI, the participants' AI practices.

RESULTS

Most participants (92.6%) reported having heard of AI technology in their practice, but only a small proportion (39.5%) had a good understanding of its concepts. The overall level of knowledge about AI among the study participants was moderate, with the mean knowledge score being 42.3 ± 21.8 out of 100 and students having a significantly higher knowledge score than faculty members. The attitude towards AI among pharmacy students and faculty members was positive, but there were still concerns about the impact of AI on job security and patient safety. Pharmacy students and faculty members had limited experience using AI tools in their practice. The majority of respondents (96.2%) believed that AI could improve patient care and pharmacy services. However, only a minority (18.6%) reported having received education or training on AI technology. High income, a strong educational level and background, and previous experience with technologies were predictors of KAP toward using AI in pharmacy practice. Finally, there was a positive correlation between knowledge about AI and attitudes towards AI as well as a significant positive correlation between AI knowledge and overall KAP scores.

CONCLUSION

The findings suggest that while there is a growing awareness of AI technology among pharmacy professionals in the Middle East and North Africa (MENA) region, there are still significant gaps in understanding and adopting AI in pharmacy Practice.

Exploring the matrix: knowledge, perceptions and prospects of artificial intelligence and machine learning in Nigerian healthcare

Background

Artificial intelligence technology can be applied in several aspects of healthcare delivery and its integration into the Nigerian healthcare value chain is expected to bring about new opportunities. This study aimed at assessing the knowledge and perception of healthcare professionals in Nigeria regarding the application of artificial intelligence and machine learning in the health sector.

Methods

A cross-sectional study was undertaken amongst healthcare professionals in Nigeria with the use of a questionnaire. Data were collected across the six geopolitical zones in the Country using a stratified multistage sampling method. Descriptive and inferential statistical analyses were undertaken for the data obtained.

Results

Female participants (55.7%) were slightly higher in proportion compared to the male respondents (44.3%). Pharmacists accounted for 27.7% of the participants, and this was closely followed by medical doctors (24.5%) and nurses (19.3%). The majority of the respondents (57.2%) reported good knowledge regarding artificial intelligence and machine learning, about a third of the participants (32.2%) were of average knowledge, and 10.6% of the sample had poor knowledge. More than half of the respondents (57.8%) disagreed with the notion that the adoption of artificial intelligence in the Nigerian healthcare sector could result in job losses. Two-thirds of the participants (66.7%) were of the view that the integration of artificial intelligence in healthcare will augment human intelligence. Three-quarters (77%) of the respondents agreed that the use of machine learning in Nigerian healthcare could facilitate efficient service delivery.

Conclusion

This study provides novel insights regarding healthcare professionals' knowledge and perception with respect to the application of artificial intelligence and machine learning in healthcare. The emergent findings from this study can guide government and policymakers in decision-making as regards deployment of artificial intelligence and machine learning for healthcare delivery.

Advancements and Applications of Artificial Intelligence in Pharmaceutical Sciences: A Comprehensive Review

Artificial intelligence (AI) has revolutionized the pharmaceutical industry, improving drug discovery, development, and personalized patient care. Through machine learning (ML), deep learning, natural language processing (NLP), and robotic automation, AI has enhanced efficiency, accuracy, and innovation in the field. The purpose of this review is to shed light on the practical applications and potential of AI in various pharmaceutical fields. These fields include medicinal chemistry, pharmaceutics, pharmacology and toxicology, clinical pharmacy, pharmaceutical biotechnology, pharmaceutical nanotechnology, pharmacognosy, and pharmaceutical management and economics. By leveraging AI technologies such as ML, deep learning, NLP, and robotic automation, this review delves into the role of AI in enhancing drug discovery, development processes, and personalized patient care. It analyzes AI's impact in specific areas such as drug synthesis planning, formulation development, toxicology predictions, pharmacy automation, and market analysis. Artificial intelligence integration into pharmaceutical sciences has significantly improved medicinal chemistry, drug discovery, and synthesis planning. In pharmaceutics, AI has advanced personalized medicine and formulation development. In pharmacology and toxicology, AI offers predictive capabilities for drug mechanisms and toxic effects. In clinical pharmacy, AI has facilitated automation and enhanced patient care. Additionally, AI has contributed to protein engineering, gene therapy, nanocarrier design, discovery of natural product therapeutics, and pharmaceutical management and economics, including marketing research and clinical trials management. Artificial intelligence has transformed pharmaceuticals, improving efficiency, accuracy, and innovation. This review highlights AI's role in drug development and personalized care, serving as a reference for professionals. The future promises a revolutionized field with AI-driven methodologies.

Community pharmacists awareness, perceptions, and opinions of artificial intelligence: A cross-sectional study in Riyadh, Saudi Arabia

BACKGROUND

Several revolutions are currently taking place in the healthcare industry to provide accurate, reliable, and valid healthcare to patients. Among these is artificial intelligence (AI).

OBJECTIVE

This study aimed to assess the CP's awareness, perceptions, and opinions of AI in health care among community pharmacists.

METHODS

This cross-sectional survey-based study was conducted over 3 months in 2023 using structured prevalidated 34 items questionnaires.

RESULTS

In this study, 94.5% (n= 258) of the CPs were aware of AI, yet 25.6% (n= 70) believed that AI would eventually replace healthcare professionals. However, 63.4% (n= 173) of the CPs concurred that AI is a technology that supports healthcare workers. 12.8% of the CPs believed that there is a risk of losing their jobs if AI is widely used in Saudi Arabia, but 68.9% (n= 188) of them considered that healthcare professionals will benefit from the extensive use of AI. Eighty-four percent of CPs (n= 232) agreed or strongly agreed that AI decreases drug mistakes in clinical practice. Similarly, 86% of the CPs (n= 235) concurred that AI makes it easier for patients to access the service. In contrast, almost 58% of the CPs (n= 232) agreed that AI makes it easier for healthcare professionals to acquire information, and 87.9% of the CPs (n= 240) said that AI helps them make better decisions.

CONCLUSION

This study concluded that most of the CPs were aware of AI and agreed that AI is a tool that helps healthcare professionals. In addition, the majority of the CPs thought that AI adoption in healthcare practice will benefit healthcare practitioners.

Association of the availability of pharmaceutical facilities provided in secondary and tertiary hospitals with clinical pharmacists' work performance

BACKGROUND

Clinical pharmacists always work as the pivotal role in the process of facilitating the proper use of drug. Based on the person-environment fit theory, the availability of facilities required in pharmaceutical service may influence pharmacists' performance, but which of them may have positive or negative impact remains unclear.

OBJECTIVES

This study aims to analysed the quantitative association of the availability of pharmaceutical facilities provided in Chinese hospitals and clinical pharmacists' work performance to assist hospitals formulating plans of the improving pharmaceutical working conditions to enhance clinical pharmacists' performance.

METHOD

Demonstrated by the panel of expert and literature review, the questionnaire for administrators and clinical pharmacists of secondary and tertiary hospitals in China was formed. Then a mixed sampling was adopted to gather data on information of the participants, as well as evaluation indexes of the availability of facilities and clinical pharmacists' work performance.

RESULTS

Overall, 625 questionnaires distributed to administrators of hospitals and 1219 ones distributed to clinical pharmacists were retrieved. As for the Pharmaceutical facilities, while the increased availability of Traditional Chinese medicine pharmacy (p = 0.02) has a significantly positive impact on clinical pharmacists' performance, the great availability of the preparation room (p = 0.07) negatively influences their work performance.

CONCLUSION

Improving the availability of facilities that significantly influence clinical pharmacists' work performance possibly reduce their workload, enhance their efficiency and further promote progress in pharmaceutical service.

Artificial intelligence in the field of pharmacy practice: A literature review

Artificial intelligence (AI) is a transformative technology used in various industrial sectors including healthcare. In pharmacy practice, AI has the potential to significantly improve medication management and patient care. This review explores various AI applications in the field of pharmacy practice. The incorporation of AI technologies provides pharmacists with tools and systems that help them make accurate and evidence-based clinical decisions. By using AI algorithms and Machine Learning, pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations tailored to individual patient requirements. Various AI models have been developed to predict and detect adverse drug events, assist clinical decision support systems with medication-related decisions, automate dispensing processes in community pharmacies, optimize medication dosages, detect drug-drug interactions, improve adherence through smart technologies, detect and prevent medication errors, provide medication therapy management services, and support telemedicine initiatives. By incorporating AI into clinical practice, health care professionals can augment their decision-making processes and provide patients with personalized care. AI allows for greater collaboration between different healthcare services provided to a single patient. For patients, AI may be a useful tool for providing guidance on how and when to take a medication, aiding in patient education, and promoting medication adherence and AI may be used to know how and where to obtain the most cost-effective healthcare and how best to communicate with healthcare professionals, optimize the health monitoring using wearables devices, provide everyday lifestyle and health guidance, and integrate diet and exercise.

Computational methods in glaucoma research: Current status and future outlook

Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.

Evaluating the Efficacy of ChatGPT in Navigating the Spanish Medical Residency Entrance Examination (MIR): Promising Horizons for AI in Clinical Medicine

The rapid progress in artificial intelligence, machine learning, and natural language processing has led to increasingly sophisticated large language models (LLMs) for use in healthcare. This study assesses the performance of two LLMs, the GPT-3.5 and GPT-4 models, in passing the MIR medical examination for access to medical specialist training in Spain. Our objectives included gauging the model's overall performance, analyzing discrepancies across different medical specialties, discerning between theoretical and practical questions, estimating error proportions, and assessing the hypothetical severity of errors committed by a physician.

MATERIAL AND METHODS

We studied the 2022 Spanish MIR examination results after excluding those questions requiring image evaluations or having acknowledged errors. The remaining 182 questions were presented to the LLM GPT-4 and GPT-3.5 in Spanish and English. Logistic regression models analyzed the relationships between question length, sequence, and performance. We also analyzed the 23 questions with images, using GPT-4's new image analysis capability.

RESULTS

GPT-4 outperformed GPT-3.5, scoring 86.81% in Spanish (p < 0.001). English translations had a slightly enhanced performance. GPT-4 scored 26.1% of the questions with images in English. The results were worse when the questions were in Spanish, 13.0%, although the differences were not statistically significant (p = 0.250). Among medical specialties, GPT-4 achieved a 100% correct response rate in several areas, and the Pharmacology, Critical Care, and Infectious Diseases specialties showed lower performance. The error analysis revealed that while a 13.2% error rate existed, the gravest categories, such as "error requiring intervention to sustain life" and "error resulting in death", had a 0% rate.

CONCLUSIONS

GPT-4 performs robustly on the Spanish MIR examination, with varying capabilities to discriminate knowledge across specialties. While the model's high success rate is commendable, understanding the error severity is critical, especially when considering AI's potential role in real-world medical practice and its implications for patient safety.

Human-Robot Collaboration for Healthcare: A Narrative Review

Robotic applications have often quickly transitioned from industrial to social. Because of this, robots can now engage with people in a natural way and blend in with their surroundings. Due to the lack of medical professionals, growing healthcare costs, and the exponential rise in the population of vulnerable groups like the ill, elderly, and children with developmental disabilities, the use of social robots in the healthcare system is expanding. As a result, social robots are employed in the medical field to entertain and educate hospitalized patients about health issues, as well as to assist the elderly and sick. They are also employed in the dispensing of medications, rehabilitation, and emotional and geriatric care. Thus, social robots raise the standard and effectiveness of medical care. This article explains how patients and healthcare professionals collaborate with robots in the healthcare industry. The objectives of this collaboration are to resolve moral and legal concerns, improve patient outcomes, and improve healthcare delivery. It has a broad range of uses, including telemedicine, rehabilitation, and robotic surgical support. Human-robot interaction is the term used to describe interactions between social robots and people. Many obstacles stand in the way of human-robot interaction in healthcare, including safety concerns, acceptability issues, appropriateness, usefulness, and the worry that robots may replace human carers. In the end, these difficulties result in a poor adoption rate for robotic technology. As a result, the applications and difficulties of human-robot interaction in healthcare are thoroughly evaluated in this research. This study also reviews future safety prospects from human-robot interaction in healthcare, as well as ethical and usability issues including privacy, trust, and safety, and our aims to provide a comprehensive overview of the use of robots in healthcare, including their applications, benefits, challenges, and prospects, to facilitate a deeper understanding of this evolving field.

Artificial intelligence in pharmacy practice: Attitude and willingness of the community pharmacists and the barriers for its implementation

BACKGROUND

Artificial intelligence (AI) is the capacity of machines to perform tasks that ordinarily require human intelligence. AI can be utilized in various pharmaceutical applications with less time and cost.

OBJECTIVES

To evaluate community pharmacists' willingness and attitudes towards the adoption of AI technology at pharmacy settings, and the barriers that hinder AI implementation.

METHODS

This cross-sectional study was conducted among community pharmacists in Jordan using an online-based questionnaire. In addition to socio-demographics, the survey assessed pharmacists' willingness, attitudes, and barriers to AI adoption in pharmacy. Binary logistic regression was conducted to find the variables that are independently associated with willingness and attitude towards AI implementation.

RESULTS

The present study enrolled 401 pharmacist participants. The median age was 30 (29-33) years. Most of the pharmacists were females (66.6%), had bachelor's degree of pharmacy (56.1%), had low-income (54.6%), and had one to five years of experience (35.9%). The pharmacists showed good willingness and attitude towards AI implementation at pharmacy (n = 401). The most common barriers to AI were lack of AI-related software and hardware (79.2%), the need for human supervision (76.4%), and the high running cost of AI (74.6%). Longer weekly working hours (attitude: OR = 1.072, 95% C.I (1.040-1.104), P < 0.001, willingness: OR = 1.069, 95% Cl. 1.039-1.009, P-value = 0.011), and higher knowledge of AI applications (attitude: OR = 1.697, 95%Cl (1.327-2.170), willingness: OR = 1.790, 95%Cl. (1.396-2.297), P-value < 0.001 for both) were significantly associated with better willingness and attitude towards AI, whereas greater years of experience (OR = 20.859, 95% Cl (5.241-83.017), P-value < 0.001) were associated with higher willingness. In contrast, pharmacists with high income (OR = 0.382, 95% Cl. (0.183-0.795), P-value = 0.010), and those with<10 visitors (OR = 0.172, 95% Cl. (0.035-0.838), P-value = 0.029) or 31-50 visitors daily (OR = 0.392, 95% Cl. (0.162-0.944), P-value = 0.037) had less willingness to adopt AI.

CONCLUSIONS

Despite the pharmacists' positive willingness and attitudes toward AI, several barriers were identified, highlighting the importance of providing educational and training programs to improve pharmacists' knowledge of AI, as well as ensuring adequate funding support to overcome the issue of AI high operating costs.

Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery

Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analyze vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts. Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information. However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations. The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions.

A computer architecture based on disruptive information technologies for drug management in hospitals

The drug management currently carried out in hospitals is inadequate due to several factors, such as processes carried out manually, the lack of visibility of the hospital supply chain, the lack of standardized identification of medicines, inefficient stock management, an inability to follow the traceability of medicines, and poor data exploitation. Disruptive information technologies could be used to develop and implement a drug management system in hospitals that is innovative in all its phases and allows these problems to be overcome. However, there are no examples in the literature that show how these technologies can be used and combined for efficient drug management in hospitals. To help solve this research gap in the literature, this article proposes a computer architecture for the whole drug management process in hospitals that uses and combines different disruptive computer technologies such as blockchain, radio frequency identification (RFID), quick response code (QR), Internet of Things (IoT), artificial intelligence and big data, for data capture, data storage and data exploitation throughout the whole drug management process, from the moment the drug enters the hospital until it is dispensed and eliminated.

Assessment of Awareness, Perceptions, and Opinions towards Artificial Intelligence among Healthcare Students in Riyadh, Saudi Arabia

Background and Objective: The role of the pharmacist in healthcare society is unique, since they are providers of health information and medication counseling to patients. Hence, this study aimed to evaluate Awareness, Perceptions, and Opinions towards Artificial intelligence (AI) among pharmacy undergraduate students at King Saud University (KSU), Riyadh, Saudi Arabia. Materials and Methods: A cross-sectional, questionnaire-based study was conducted between December 2022 and January 2023 using online questionnaires. The data collection was carried out using convenience sampling methods among senior pharmacy students at the College of Pharmacy, King Saud University. Statistical Package for the Social Sciences version 26 was used to analyze the data (SPSS). Results: A total of one hundred and fifty-seven pharmacy students completed the questionnaires. Of these, most of them (n = 118; 75.2%) were males. About 42%, (n = 65) were in their fourth year of study. Most of the students (n = 116; 73.9%) knew about AI. In addition, 69.4% (n = 109) of the students thought that AI is a tool that helps healthcare professionals (HCP). However, more than half 57.3% (n = 90) of the students were aware that AI would assist healthcare professionals in becoming better with the widespread use of AI. Furthermore, 75.1% of the students agreed that AI reduces errors in medical practice. The mean positive perception score was 29.8 (SD = 9.63; range-0-38). The mean score was significantly associated with age (p = 0.030), year of study (p = 0.040), and nationality (p = 0.013). The gender of the participants was found to have no significant association with the mean positive perception score (p = 0.916). Conclusions: Overall, pharmacy students showed good awareness of AI in Saudi Arabia. Moreover, the majority of the students had positive perceptions about the concepts, benefits, and implementation of AI. Moreover, most students indicated that there is a need for more education and training in the field of AI. Consequently, early exposure to content related to AI in the curriculum of pharmacy is an important step to help in the wide use of these technologies in the graduates' future careers.