Search Results (28)

Since the turn of the century, we have witnessed an extremely intensive development of biotechnology and nanotechnology, which, in terms of intensity can only be compared to the development of information technology and the resulting emergence of artificial intelligence. In the present review, we deliberately omit the development of information technology and artificial intelligence. Instead, our interest is focused on bionanomaterials and nanobiomaterials, their production and applications, and, in particular, the different meanings of these terms. We adopted an analysis of the literature published between January 2000 and May 2025, available in PubMed. The database was searched for selected areas: types (origin, structure, and function), manufacturing methods (chemical, physicochemical, and biological), and applications (medicine/pharmacy, textile technology, cosmetology, and agriculture/environment). Our findings revealed a significant increase in the number of publications for both terms, with nanobiomaterials predominating. The authors of the publications included in PubMed clearly outline the separation of meanings of both concepts, despite the lack of normative regulations in this regard. Nanoparticles are the most commonly represented type in the use of both terms, and drug delivery is a dominant application. However, it is worth noting the lack of nanobiomaterials in the agricultural/environmental application categories. Despite the enormous similarity between the terms “nanobiomaterials” and “bionanomaterials,” both in terms of nomenclature and application, there is a significant difference resulting from the manufacturing technologies and applications used. The term “nanobiomaterials” should be assigned only to biomaterials, in accordance with the definition of a biomaterial, regardless of their manufacturing technology, while the term “bionanomaterials” should be applied to all products of bionanotechnology, excluding products used as biomaterials. Full article

The integration of Artificial Intelligence and Robotics (AI/R) in healthcare presents both opportunities and challenges, especially in developing countries. This study assessed the attitudes and perceptions of Vietnamese healthcare undergraduates towards AI/R applications in healthcare and elderly care. In 2023, a cross-sectional survey was conducted among 1221 Vietnamese healthcare undergraduates. The questionnaire covered demographic, academic, social, and mental factors, as well as attitudes towards AI/R applications measured by a five-level Likert scale. Key findings revealed that respondents were primarily majoring in medicine (60.9%) and pharmacy (29.4%). Awareness and interest in AI/R were high (89.9% and 88.3%, respectively), but formal training was significantly lacking (5.9%). A substantial majority (89.9%) expressed a need for AI/R training. Respondents perceived considerable benefits of AI/R, particularly in data synchronization (mean [M] = 3.83), workload reduction for medical staff (M = 3.79), and delivering multiple healthcare benefits (M = 3.82). Moderate concerns were noted regarding security and privacy (M = 3.46), potential over-reliance on technology (M = 3.43), and AI/R potentially replacing medical staff (M = 3.38). Overall, perceived benefits (M = 3.67) outweighed concerns (M = 3.38), (p < 0.001). Additionally, participants aware of AI/R and those planning to study abroad showed greater interest and training needs in AI/R. Higher GPA and self-esteem were associated with a greater interest in AI/R research. The study highlights a significant gap in formal AI/R training, not only in availability but also in the absence of structured, outcome-based curricula, despite the strong interest among healthcare students in acquiring knowledge and skills in this area. These findings suggest the need for enhanced educational programs to train healthcare students with the necessary competencies to apply AI/R technologies effectively. Full article

Background/Objectives: Smart Formulation is an artificial intelligence-based platform designed to predict the Beyond Use Dates (BUDs) of compounded oral solid dosage forms. The study aims to develop a decision-support tool for pharmacists by integrating molecular, formulation, and environmental parameters to assist in optimizing the stability of extemporaneous preparations. Methods: A tree ensemble regression model was trained using a curated dataset of 55 experimental BUD values collected from the Stabilis database. Each formulation was encoded with molecular descriptors, excipient composition, packaging type, and storage conditions. The model was implemented using the KNIME platform, allowing the integration of cheminformatics and machine learning workflows. After training, the model was used to predict BUDs for 3166 APIs under various formulation and storage scenarios. Results: The analysis revealed a significant impact of excipient type, number, and environmental conditions on API stability. APIs with lower LogP values generally exhibited greater stability, particularly when formulated with a single excipient. Excipients such as cellulose, silica, sucrose, and mannitol were associated with improved stability, whereas HPMC and lactose contributed to faster degradation. The use of two excipients instead of one frequently resulted in reduced BUDs, possibly due to moisture redistribution or phase separation effects. Conclusions: Smart Formulation represents a valuable contribution to computational pharmaceutics, bridging theoretical formulation design with practical compounding needs. The platform offers a scalable, cost-effective alternative to traditional stability testing and is already available for use by healthcare professionals. Its implementation in hospital and community pharmacies may help mitigate drug shortages, support formulation standardization, and improve patient care. Future developments will focus on real-time stability monitoring and adaptive learning for enhanced precision. Full article

As artificial intelligence (AI) becomes increasingly utilized to perform tasks requiring human intelligence, patients who are pregnant may turn to AI for advice on over-the-counter (OTC) medications. However, medications used in pregnancy may pose profound safety concerns limited by data availability. This study focuses on a chatbot’s ability to accurately provide information regarding OTC medications as it relates to patients that are pregnant. A prospective, descriptive design was used to compare the responses generated by the Chat Generative Pre-Trained Transformer 3.5 (ChatGPT-3.5) to the information provided by UpToDate^®. Eighty-seven of the top pharmacist-recommended OTC drugs in the United States (U.S.) as identified by Pharmacy Times were assessed for safe use in pregnancy using ChatGPT-3.5. A piloted, standard prompt was input into ChatGPT-3.5, and the responses were recorded. Two groups independently rated the responses compared to UpToDate on their correctness, completeness, and safety using a 5-point Likert scale. After independent evaluations, the groups discussed the findings to reach a consensus, with a third independent investigator giving final ratings. For correctness, the median score was 5 (interquartile range [IQR]: 5–5). For completeness, the median score was 4 (IQR: 4–5). For safety, the median score was 5 (IQR: 5–5). Despite high overall scores, the safety errors in 9% of the evaluations (n = 8), including omissions that pose a risk of serious complications, currently renders the chatbot an unsafe standalone resource for this purpose. Full article

Background and Objectives: Investigating the adherence to antihypertensive medication and identifying patients with low adherence allows targeted interventions to improve therapeutic outcomes. Artificial intelligence (AI) offers advanced tools for analyzing medication adherence data. This study aimed to develop and validate several predictive models for non-adherence, using patient-reported data collected via a structured questionnaire. Materials and Methods: A cross-sectional, multi-center study was conducted on 3095 hypertensive patients from community pharmacies. A structured questionnaire was administered, collecting data on sociodemographic factors, medical history, self-monitoring behaviors, and informational exposure, alongside medication adherence measured using the Romanian-translated and validated ARMS (Adherence to Refills and Medications Scale). Five machine learning models were developed to predict non-adherence, defined by ARMS quartile-based thresholds. The models included Logistic Regression, Random Forest, and boosting algorithms (CatBoost, LightGBM, and XGBoost). Models were evaluated based on their ability to stratify patients according to adherence risk. Results: A total of 79.13% of respondents had an ARMS Score ≥ 15, indicating a high prevalence of suboptimal adherence. Better adherence was statistically associated (adjusted for age and sex) with more frequent blood pressure self-monitoring, a reduced salt intake, fewer daily supplements, more frequent reading of medication leaflets, and the receipt of specific information from pharmacists. Among the ML models, CatBoost achieved the highest ROC AUC Scores across the non-adherence classifications, although none exceeded 0.75. Conclusions: Several machine learning models were developed and validated to estimate levels of medication non-adherence. While the performance was moderate, the results demonstrate the potential of AI in identifying and stratifying patients by adherence profiles. Notably, to our knowledge, this study represents the first application of permutation and SHapley Additive exPlanations feature importance in combination with probability-based adherence stratification, offering a novel framework for predictive adherence modelling. Full article

The rapid expansion of infectious diseases in urban environments presents a significant public health challenge, as traditional surveillance methods rely on delayed case reporting, limiting proactive response capabilities. With the increasing availability of real-time health data, artificial intelligence (AI) has emerged as a powerful tool for disease monitoring, anomaly detection, and outbreak prediction. This study proposes SmartHealth-Track, an AI-powered real-time infectious disease monitoring framework that integrates machine learning models with IoT-enabled surveillance, smart pharmacy analytics, wearable health tracking, and wastewater surveillance to enhance early outbreak detection and predictive forecasting. The system leverages time series forecasting with long short-term memory (LSTM) networks, logistic regression for outbreak probability estimation, anomaly detection with isolation forests, and natural language processing (NLP) for extracting epidemiological insights from public health reports and social media trends. Experimental validation using real-world datasets demonstrated that SmartHealth-Track achieves high accuracy, with an outbreak detection accuracy of 92.4%, wearable-based fever detection accuracy of 93.5%, AI-driven contact tracing precision of 91.2%, and AI-enhanced wastewater pathogen classification accuracy of 94.1%. The findings confirm that AI-driven real-time surveillance significantly improves outbreak detection and forecasting, enabling timely public health interventions. Future research should focus on federated learning for secure data collaboration and reinforcement learning for adaptive decision making. Full article

Diabetes is a growing chronic disease with complications that impose a significant burden on healthcare systems worldwide. Pharmacists are readily accessible for diabetes management beyond simply dispensing medications. Consequently, they are involved in disease prevention and detection, therapy management, and patient monitoring. However, with the current escalating impact of diabetes, pharmacists must upgrade their strategies by integrating guidelines from sources like the American Diabetes Association (ADA) 2024 with pharmacy expertise. This perspective serves as a guide for pharmacists, identifying key foundations involved in diabetes management, highlighting five crucial steps for optimal disease control, ranging from prevention strategies to pharmacist-led counseling interventions. We employed PubMed, CDC, WHO guidelines, and key reference texts. Searches were performed using combinations of terms such as “pharmacist”, “type 2 diabetes”, “diabetes prevention”, “pharmacist intervention”, and “diabetes management”, covering publications from January 2010 to March 2025. Studies were included if they focused on pharmacist-led prevention, intervention, or management strategies related to type 2 diabetes (T2D) and were published in English. Studies focusing exclusively on type 1 diabetes were excluded. Generative artificial intelligence was employed to order and structure information as described in the acknowledgments. Conflicting evidence was resolved by giving relevance to recent systematic reviews, randomized trials, and major guidelines. Additional insights were gained through consultations with PharmD professionals experienced in diabetes care. Evidence from selected studies suggests that pharmacist-led care models may enhance and promote the early detection of T2D, improve therapy adherence, enhance glycemic control, and increase overall treatment efficiency. This work suggests that pharmacists must play a key role in diagnosing, preventing, managing, and mitigating the consequences associated with T2D. They must contribute to early treatments with appropriate training and involvement to improve therapeutic outcomes and reduce diabetes-related complications. Full article

Background: Artificial Intelligence (AI) is transforming healthcare education, offering innovative tools to enhance learning and clinical decision-making. AI-driven platforms like ChatGPT are increasingly integrated into pharmacy education, enabling students to access vast medical knowledge, refine decision-making skills, and simulate clinical scenarios. Objective: This study examines pharmacy students’ attitudes, knowledge, and practices regarding ChatGPT’s use in clinical decision-making, evaluates its perceived benefits and limitations, and identifies factors influencing AI integration in pharmacy education. Methodology: A cross-sectional study was conducted among 512 pharmacy students at King Khalid University. A structured questionnaire assessed demographics, knowledge, attitudes, and practices. Data were analyzed using SPSS, employing descriptive statistics, chi-square tests, and logistic regression. Results: The majority (82.4%) supported AI integration in pharmacy education, while 74.6% believed that ChatGPT could enhance clinical decision-making. Primary applications included drug information retrieval (72.3%) and exam preparation (66.7%). However, concerns about AI accuracy (55.2%) and ethical implications were noted. Conclusions: Pharmacy students at King Khalid University exhibit positive attitudes toward AI, recognizing its educational benefits while acknowledging challenges. Addressing accuracy concerns and ethical considerations through structured AI training programs is essential to optimize AI’s role in pharmacy education and practice. Full article

One area of study within machine learning and artificial intelligence (AI) seeks to create computer programs with intelligence that can mimic human focal processes in order to produce results. This technique includes data collection, effective data usage system development, conclusion illustration, and arrangements. Analysis algorithms that are learning to mimic human cognitive activities are the most widespread application of AI. Artificial intelligence (AI) studies have proliferated, and the field is quickly beginning to understand its potential impact on medical services and investigation. This review delves deeper into the pros and cons of AI across the healthcare and pharmaceutical research industries. Research and review articles published throughout the last few years were selected from PubMed, Google Scholar, and Science Direct, using search terms like ‘artificial intelligence’, ‘drug discovery’, ‘pharmacy research’, ‘clinical trial’, etc. This article provides a comprehensive overview of how artificial intelligence (AI) is being used to diagnose diseases, treat patients digitally, find new drugs, and predict when outbreaks or pandemics may occur. In artificial intelligence, neural networks and deep learning are some of the most popular tools; in clinical research, Bayesian non-parametric approaches hold promise for better results, while smartphones and the processing of natural languages are employed in recognizing patients and trial monitoring. Seasonal flu, Ebola, Zika, COVID-19, tuberculosis, and outbreak predictions were made using deep computation and artificial intelligence. The academic world is hopeful that AI development will lead to more efficient and less expensive medical and pharmaceutical investigations and better public services. Full article

Over the past five years, the application of artificial intelligence (AI) including its significant subset, machine learning (ML), has significantly advanced pharmaceutical procedures in community pharmacies, hospital pharmacies, and pharmaceutical industry settings. Numerous notable healthcare institutions, such as Johns Hopkins University, Cleveland Clinic, and Mayo Clinic, have demonstrated measurable advancements in the use of artificial intelligence in healthcare delivery. Community pharmacies have seen a 40% increase in drug adherence and a 55% reduction in missed prescription refills since implementing artificial intelligence (AI) technologies. According to reports, hospital implementations have reduced prescription distribution errors by up to 75% and enhanced the detection of adverse medication reactions by up to 65%. Numerous businesses, such as Atomwise and Insilico Medicine, assert that they have made noteworthy progress in the creation of AI-based medical therapies. Emerging technologies like federated learning and quantum computing have the potential to boost the prediction of protein–drug interactions by up to 300%, despite challenges including high implementation costs and regulatory compliance. The significance of upholding patient-centred care while encouraging technology innovation is emphasised in this review. Full article

While pharmacy education successfully employs various methodologies including case-based learning and simulated patient interactions, providing consistent, individualized guidance at scale remains challenging in team-based learning environments. Artificial intelligence (AI) offers potential solutions through automated facilitation, but its possible utility in pharmacy education remains unexplored. We developed and evaluated an AI-guided patient case discussion simulation to enhance learners’ ability to integrate foundational science knowledge with clinical decision-making in a didactic immunology course in a US PharmD program. We utilized a large language model programmed with specific educational protocols and rubrics. Here, we present the step-by-step prompt engineering protocol as a toolkit. The system was structured around three core components in an immunology team-based learning activity: (1) symptomatology analysis, (2) laboratory test interpretation, and (3) pharmacist role definition and PPCP. Performance evaluation was conducted using a comprehensive rubric assessing multiple clinical reasoning and pharmaceutical knowledge domains. The standardized evaluation rubric showed reliable assessment across key competencies including condition identification (30% weighting), laboratory test interpretation (40% weighting), and pharmacist role understanding (30% weighting). Our AI patient simulator offers a scalable solution for standardizing clinical case discussions while maintaining individualized learning experiences. Full article

Integrating artificial intelligence (AI) into pharmacy operations and drug discovery represents a groundbreaking milestone in healthcare, offering unparalleled opportunities to revolutionize medication management, accelerate drug development, and deliver truly personalized patient care. This review examines the pivotal impact of AI in critical domains, including drug discovery and development, drug repurposing, clinical trials, and pharmaceutical productivity enhancement. By significantly reducing human workload, improving precision, and shortening timelines, AI empowers the pharmaceutical industry to achieve ambitious objectives efficiently. This study delves into tools and methodologies enabling AI implementation, addressing ongoing challenges such as data privacy, algorithmic transparency, and ethical considerations while proposing actionable strategies to overcome these barriers. Furthermore, it offers insights into the future of AI in pharmacy, highlighting its potential to foster innovation, enhance efficiency, and improve patient outcomes. This research is grounded in a rigorous methodology, employing advanced data collection techniques. A comprehensive literature review was conducted using platforms such as PubMed, Semantic Scholar, and multidisciplinary databases, with AI-driven algorithms refining the retrieval of relevant and up-to-date studies. Systematic data scoping incorporated diverse perspectives from medical, pharmaceutical, and computer science domains, leveraging natural language processing for trend analysis and thematic content coding to identify patterns, challenges, and emerging applications. Modern visualization tools synthesized the findings into explicit graphical representations, offering a comprehensive view of the key role of AI in shaping the future of pharmacy and healthcare. Full article

With technological advancements, remarkable progress has been made with the convergence of health sciences and Artificial Intelligence (AI). Modern health systems are proposed to ease patient diagnostics. However, the challenge is to provide AI-based precautions to patients and doctors for more accurate risk assessment. The proposed healthcare system aims to integrate patients, doctors, laboratories, pharmacies, and administrative personnel use cases and their primary functions onto a single platform. The proposed framework can also process microscopic images, CT scans, X-rays, and MRI to classify malignancy and give doctors a set of AI precautions for patient risk assessment. The proposed framework incorporates various DCNN models for identifying different forms of tumors and fractures in the human body i.e., brain, bones, lungs, kidneys, and skin, and generating precautions with the help of the Fined-Tuned Large Language Model (LLM) i.e., Generative Pretrained Transformer 4 (GPT-4). With enough training data, DCNN can learn highly representative, data-driven, hierarchical image features. The GPT-4 model is selected for generating precautions due to its explanation, reasoning, memory, and accuracy on prior medical assessments and research studies. Classification models are evaluated by classification report (i.e., Recall, Precision, F1 Score, Support, Accuracy, and Macro and Weighted Average) and confusion matrix and have shown robust performance compared to the conventional schemes. Full article

ChatGPT has emerged as a promising tool for enhancing clinical practice. However, its implementation raises critical questions about its impact on this field. In this scoping review, we explored the utility of ChatGPT in pharmacy practice. A search was conducted in five databases up to 23 May 2024. Studies analyzing the use of ChatGPT with direct or potential applications in pharmacy practice were included. A total of 839 records were identified, of which 14 studies were included: six tested ChatGPT version 3.5, three tested version 4.0, three tested both versions, one used version 3.0, and one did not specify the version. Only half of the studies evaluated ChatGPT in real-world scenarios. A reasonable number of papers analyzed the use of ChatGPT in pharmacy practice, highlighting both benefits and limitations. The studies indicated that ChatGPT is not fully prepared for use in pharmacy practice due to significant limitations. However, there is great potential for its application in this context in the near future, following further improvements to the tool. Further exploration of its use in pharmacy practice is required, along with proposing its conscious and appropriate utilization. Full article

The integration of deep learning into radiology has the potential to enhance diagnostic processes, yet its acceptance in clinical practice remains limited due to various challenges. This study aimed to develop and evaluate a fine-tuned large language model (LLM), based on Llama 3-8B, to automate the generation of accurate and concise conclusions in magnetic resonance imaging (MRI) and computed tomography (CT) radiology reports, thereby assisting radiologists and improving reporting efficiency. A dataset comprising 15,000 radiology reports was collected from the University of Medicine and Pharmacy of Craiova’s Imaging Center, covering a diverse range of MRI and CT examinations made by four experienced radiologists. The Llama 3-8B model was fine-tuned using transfer-learning techniques, incorporating parameter quantization to 4-bit precision and low-rank adaptation (LoRA) with a rank of 16 to optimize computational efficiency on consumer-grade GPUs. The model was trained over five epochs using an NVIDIA RTX 3090 GPU, with intermediary checkpoints saved for monitoring. Performance was evaluated quantitatively using Bidirectional Encoder Representations from Transformers Score (BERTScore), Recall-Oriented Understudy for Gisting Evaluation (ROUGE), Bilingual Evaluation Understudy (BLEU), and Metric for Evaluation of Translation with Explicit Ordering (METEOR) metrics on a held-out test set. Additionally, a qualitative assessment was conducted, involving 13 independent radiologists who participated in a Turing-like test and provided ratings for the AI-generated conclusions. The fine-tuned model demonstrated strong quantitative performance, achieving a BERTScore F1 of 0.8054, a ROUGE-1 F1 of 0.4998, a ROUGE-L F1 of 0.4628, and a METEOR score of 0.4282. In the human evaluation, the artificial intelligence (AI)-generated conclusions were preferred over human-written ones in approximately 21.8% of cases, indicating that the model’s outputs were competitive with those of experienced radiologists. The average rating of the AI-generated conclusions was 3.65 out of 5, reflecting a generally favorable assessment. Notably, the model maintained its consistency across various types of reports and demonstrated the ability to generalize to unseen data. The fine-tuned Llama 3-8B model effectively generates accurate and coherent conclusions for MRI and CT radiology reports. By automating the conclusion-writing process, this approach can assist radiologists in reducing their workload and enhancing report consistency, potentially addressing some barriers to the adoption of deep learning in clinical practice. The positive evaluations from independent radiologists underscore the model’s potential utility. While the model demonstrated strong performance, limitations such as dataset bias, limited sample diversity, a lack of clinical judgment, and the need for large computational resources require further refinement and real-world validation. Future work should explore the integration of such models into clinical workflows, address ethical and legal considerations, and extend this approach to generate complete radiology reports. Full article