Role Of Artificial Intelligence Research Articles

Role of artificial intelligence in pharmaceutical drug development and drug delivery: An updated review

Role of artificial intelligence in pharmaceutical drug development and drug delivery: An updated review

The Role of Artificial Intelligence in Drug Discovery and Pharmaceutical Development: A Paradigm Shift in the History of Pharmaceutical Industries.

In today's world, with an increasing patient population, the need for medications is increasing rapidly. However, the current practice of drug development is time-consuming and requires a lot of investment by the pharmaceutical industries. Currently, it takes around 8-10years and $3 billion of investment to develop a medication. Pharmaceutical industries and regulatory authorities are continuing to adopt new technologies to improve the efficiency of the drug development process. However, over the decades the pharmaceutical industries were not able to accelerate the drug development process. The pandemic (COVID-19) has taught the pharmaceutical industries and regulatory agencies an expensive lesson showing the need for emergency preparedness by accelerating the drug development process. Over the last few years, the pharmaceutical industries have been collaborating with artificial intelligence (AI) companies to develop algorithms and models that can be implemented at various stages of the drug development process to improve efficiency and reduce the developmental timelines significantly. In recent years, AI-screened drug candidates have entered clinical testing in human subjects which shows the interest of pharmaceutical companies and regulatory agencies. End-end integration of AI within the drug development process will benefit the industries for predicting the pharmacokinetic and pharmacodynamic profiles, toxicity, acceleration of clinical trials, study design, virtual monitoring of subjects, optimization of manufacturing process, analyzing and real-time monitoring of product quality, and regulatory preparedness. This review article discusses in detail the role of AI in various avenues of the pharmaceutical drug development process, its limitations, regulatory and future perspectives.

Role of Artificial Intelligence in Musculoskeletal Interventions

Artificial intelligence (AI) has rapidly emerged as a transformative force in musculoskeletal imaging and interventional radiology. This article explores how AI-based methods—including machine learning (ML) and deep learning (DL)—streamline diagnostic processes, guide interventions, and improve patient outcomes. Key applications discussed include ultrasound-guided procedures for joints, nerves, and tumor-targeted interventions, along with CT-guided biopsies and ablations, and fluoroscopy-guided facet joint and nerve block injections. AI-powered segmentation algorithms, real-time feedback systems, and dose-optimization protocols collectively enable greater precision, operator consistency, and patient safety. In rehabilitation, AI-driven wearables and predictive models facilitate personalized exercise programs that can accelerate recovery and enhance long-term function. While challenges persist—such as data standardization, regulatory hurdles, and clinical adoption—ongoing interdisciplinary collaboration, federated learning models, and the integration of genomic and environmental data hold promise for expanding AI’s capabilities. As personalized medicine continues to advance, AI is poised to refine risk stratification, reduce radiation exposure, and support minimally invasive, patient-specific interventions, ultimately reshaping musculoskeletal care from early detection and diagnosis to individualized treatment and rehabilitation.

Effect of ChatGPTon Various Aspects of Undergraduate Medical Education

Background: Artificial intelligence (AI) has emerged as a transformative tool across various domains, including medical education. AI applications, such as ChatGPT, present new opportunities for enhancing student learning, instructional strategies, clinical scenario simulations, and collaborative learning. Objective: This study examines the impact of Artificial Intelligence on various aspects of undergraduate medical education.Materials and Methods: This study employed a quantitative cross-sectional design to assess the impact of AI in medical education. A total of 300 participants, including medical students, educators, and healthcare professionals, were surveyed. The study was conducted over six months (June to December 2023) across multiple medical institutions. Scenario modeling was used to simulate clinical cases, where participants engaged in AI-assisted diagnostic decision-making and learning activities in a controlled academic setting. Ethical approval was obtained from the Institutional Review Board (IRB) of Women Medical & Dental College Abbottabad. Data were collected using a structured questionnaire, which evaluated AI's role in student learning, collaborative education, and instructional methods. Factor analysis and Cronbach's alpha were used to validate the questionnaire's reliability and construct validity.Results: It revealed significant positive impacts, with 80% showed improved learning experiences and 85% acknowledging the benefits of simulated clinical scenarios. Collaborative learning effectiveness was recognized by 70%, mainly among educators who noted adaptability. Reliability measures showed strong internal consistency (Cronbach's alpha values), and factor analysis highlighted key dimensions such as Personalized Learning. Conclusion: This research provides empirical evidence in support of using AI in medical education. The results indicate that AI applications can improve student learning outcomes, enhance teaching, and even assist in the simulation of clinical scenarios. These finding further indicate research directed towards optimising advanced technology in medical education.Keywords: Artificial intelligence (AI), AI in healthcare, Clinical simulations, Collaborative learning, Medical education, Student learning.

The Role of Artificial Intelligence in Early Tumor Detection: An XGBoost Risk Assessment Model for Egyptian Patients

This study developed an XGBoost-based risk assessment model to enhance early tumor detection among Egyptian patients, addressing the challenges of late diagnosis and limited healthcare resources. Utilizing a retrospective dataset of 178 patients, the model incorporated demographic, clinical, and biochemical variables, including AFP levels, viral hepatitis status (HBV/HCV), and liver function markers. The model demonstrated strong predictive performance, achieving an accuracy of 0.833, precision of 0.846, and an AUC of 0.86, though recall remained moderate (0.688), indicating room for improvement in identifying high-risk cases. Feature importance analysis highlighted AFP levels and hepatitis status as the most influential predictors, aligning with Egypt’s high prevalence of liver cancer. The findings underscore the potential of AI-driven tools for early cancer screening in resource-limited settings, offering a scalable and cost-effective solution. However, future work should focus on expanding datasets, optimizing recall, and validating the model across diverse populations to ensure clinical applicability. This research contributes to the growing integration of AI in oncology, providing a framework for tailored risk stratification in high-burden regions

The present and future of lung cancer screening: latest evidence.

Lung cancer is the leading cause of cancer-related mortality worldwide. Early lung cancer detection improves lung cancer-related mortality and survival. This report summarizes presentations and panel discussions from a webinar, "The Present and Future of Lung Cancer Screening: Latest Evidence and AI Perspectives." The webinar provided the perspectives of experts from the United States, United Kingdom, and China on evidence-based recommendations and management in lung cancer screening (LCS), barriers, and the role of artificial intelligence (AI). With several countries now incorporating the utilization of AI in their screening programs, AI offers potential solutions to some of the challenges associated with LCS.

A Review on the Role of Artificial Intelligence in Livestock Health Management

Artificial Intelligence (AI) is transforming livestock health management by introducing practices that are guided by data, efficient, and environmentally sustainable. This paper examines how AI technologies are being applied in animal agriculture, emphasizing their contributions to live monitoring, illness detection, behavior tracking, and improving breeding processes. Utilizing machine learning, computer vision, and Internet of Things (IoT) tools, AI supports early health issue identification, enhances animal well-being, and boosts overall productivity. The discussion includes applications like automated health evaluations and advancements in reproductive success, alongside the significant impact of AI in managing poultry. Additionally, the study considers the hurdles of implementing AI, such as substantial costs, information security, and moral implications. In spite of these challenges, AI presents valuable opportunities for the advancement of precision livestock farming by promoting informed decisions and optimized use of resources. The paper finishes by projecting how AI could influence the future of livestock oversight through ongoing research, technological innovation, and supportive policies, all while maintaining ethical and sustainable practices in farming.

Perceptions of artificial intelligence in healthcare curricula: insights from a nationwide survey of medical students

IntroductionIn an era where artificial intelligence (AI) is rapidly transforming the healthcare sector, understanding the perceptions of future healthcare professionals is vital. This study aims to assess the attitudes of healthcare students in Saudi Arabia toward AI and to evaluate their views on its impact on medical education and future healthcare careers.MethodsA nationwide survey was conducted across 21 universities in Saudi Arabia. The study targeted healthcare students from various academic years and disciplines. Data were collected on students' exposure to AI, their educational backgrounds, and their perceptions of AI's role in healthcare.ResultsThe survey revealed significant gender-based differences in perceptions of AI. There was a strong consensus on the importance of integrating AI into healthcare curricula. However, respondents also expressed caution regarding the application of AI in clinical practice. Attitudes varied based on students' year of study, level of AI exposure, and educational background, indicating a complex set of influences on their views.DiscussionThe findings highlight the multifaceted perspectives of future healthcare professionals regarding AI. The results suggest the necessity of developing tailored educational strategies that incorporate AI into the curriculum while addressing concerns about its clinical implementation. These insights are essential for preparing students for an AI-integrated healthcare system in the Kingdom of Saudi Arabia.

Agentic rulebooks using active inference: an artificial intelligence application for environmental sustainability

Artificial intelligence (AI) is increasingly proposed as a solution to environmental sustainability challenges, with applications aimed at optimizing resource utilization and energy consumption. However, AI technologies also have significant negative environmental impacts. This duality underscores the need to critically evaluate AI's role in sustainable practices. One example of AI's application in sustainability is the Occupant Controlled Smart Thermostat (OCST). These systems optimize indoor temperature management by responding to dynamic signals, such as energy price fluctuations, which reflect power grid stress. Accordingly, regulatory frameworks have mandated performance standards for such technologies to ensure effective demand responsiveness. While OCSTs are effective in managing energy demand through predefined norms like price signals, their current designs often fail to accommodate the complex interplay of conflicting priorities, such as user comfort and grid optimization, particularly in uncertain climatic conditions. For instance, extreme weather events can amplify energy demands and user needs, necessitating a more context sensitive approach. This adaptability requires OCSTs to dynamically shift between multiple normative constraints (i.e., norms), such as prioritizing userdefined temperature settings over price-based energy restrictions when contextually appropriate. In this paper, we propose an innovative approach that combines the theory of active inference from theoretical neuroscience and robotics with a rulebook formalism to enhance the decision-making capabilities of autonomous AI agents. Using simulation studies, we demonstrate how these AI agents can resolve conflicts among norms under environmental uncertainty. A minimal use case is presented, where an OCST must decide whether to warm a room based on two conflicting rules: a “price” rule that restricts energy use above a cost threshold and a “need” rule that prioritizes maintaining the user's desired temperature. Our findings illustrate the potential for advanced AI-driven OCST systems to navigate conflicting norms, enabling more resilient and user-centered solutions to sustainable energy challenges.

The Role of Artificial Intelligence and Machine Learning in Optimizing Operational Decision-Making Post-Pandemic

The COVID-19 pandemic significantly disrupted global operations, forcing businesses and industries to reevaluate their decision-making processes. In response, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative tools for optimizing operational decision-making in the post-pandemic era. The objective of this study is to explore the role of AI and ML in enhancing efficiency, resilience, and adaptability across various sectors. This research investigates AI-driven decision-making models and their contributions to supply chain management, healthcare, finance, and human resource operations. By understanding the capabilities and challenges associated with AI and ML integration, the study aims to provide actionable insights for organizations seeking to strengthen their operational strategies in an uncertain environment

The Role of Artificial Intelligence in Advancing Healthcare and Longevity Research

The integration of artificial intelligence in healthcare systems represents a transformative advancement in medical practice, fundamentally reshaping approaches to disease prevention, treatment, and longevity articles. This comprehensive review examines the impact of AI across multiple healthcare domains, including diagnostic accuracy, operational efficiency, genetic analysis, personalized medicine, drug discovery, and anti-aging research. The article analyzes how AI-powered systems have revolutionized medical imaging, clinical decision support, and resource allocation while significantly reducing diagnostic times and error rates. Furthermore, it explores AI's role in accelerating drug development processes, optimizing treatment protocols, and advancing precision medicine through improved genetic analysis capabilities. The article demonstrates substantial improvements in healthcare delivery efficiency, patient outcomes, and cost reduction across various medical institutions, highlighting AI's potential to revolutionize healthcare delivery and advance medical research.

The role of artificial intelligence in enhancing personalized learning pathways and clinical training in dental education

The role of artificial intelligence in enhancing personalized learning pathways and clinical training in dental education

ChatGPT in engineering education: a breakthrough or a challenge?

Abstract In engineering education, where hands-on problem-solving and technical proficiency especially in physics-based learning are critical, the role of artificial intelligence (AI) tools like ChatGPT remains debated; whether AI serves as a breakthrough innovation or presents new challenges. This study seeks to bridge that gap by examining the impact of ChatGPT on mechanical engineering students in a project-based course. It explores how students used AI tools to understand key concepts, support group collaboration, and improve coding and writing tasks. Using survey data from first-year students encouraged to integrate AI into their coursework, the research provides insights into the ethical and educational implications of AI in engineering education, considering both its benefits and challenges. The findings indicate that while ChatGPT was widely utilized for coding tasks such as MATLAB programming and enhancing conceptual understanding, its impact on group collaboration was modest. Ethical concerns, including the temptation to misuse AI, highlight the need for structured guidelines to ensure responsible AI usage. The study also identifies the necessity of verifying AI-generated outputs, as AI tools may produce inaccurate or misleading information, particularly in technical problem-solving. This paper offers recommendations for optimizing AI-assisted learning, fostering critical thinking, and adapting assessment practices to balance AI’s educational benefits with academic integrity. These insights aim to guide educators and policymakers in effectively integrating AI into engineering and physics education while addressing its challenges to create a productive learning environment.

Role of Artificial Intelligence and Personalized Medicine in Enhancing HIV Management and Treatment Outcomes

The integration of artificial intelligence and personalized medicine is transforming HIV management by enhancing diagnostics, treatment optimization, and disease monitoring. Advances in machine learning, deep neural networks, and multi-omics data analysis enable precise prognostication, tailored antiretroviral therapy, and early detection of drug resistance. AI-driven models analyze vast genomic, proteomic, and clinical datasets to refine treatment strategies, predict disease progression, and pre-empt therapy failures. Additionally, AI-powered diagnostic tools, including deep learning imaging and natural language processing, improve screening accuracy, particularly in resource-limited settings. Despite these innovations, challenges such as data privacy, algorithmic bias, and the need for clinical validation remain. Successful integration of AI into HIV care requires robust regulatory frameworks, interdisciplinary collaboration, and equitable technology access. This review explores both the potential and limitations of AI in HIV management, emphasizing the need for ethical implementation and expanded research to maximize its impact. AI-driven approaches hold great promise for a more personalized, efficient, and effective future in HIV treatment and care.

Interactive Heritage: The Role of Artificial Intelligence in Digital Museums

Museum use of artificial intelligence (AI) is becoming increasingly common, but its contribution to museum attendance is yet to be confirmed. This paper investigates whether the adoption of AI impacts museum visitation using data from 19 museums. Statistical analyses, including ANOVA and Spearman correlation, were conducted to determine if the use of AI has significant effects on visitors. The findings indicate no statistically significant difference between museums that use AI and those that do not (ANOVA: p = 0.263, F = 1.34), but the Spearman correlation (r = 0.448, p = 0.055) indicates a moderate positive correlation that is not statistically significant. The findings suggest that AI enhances visitor experience rather than increasing attendance. Additionally, this study proposes a conceptual framework for AI prototyping in museums. The study contributes to the ongoing debate on AI in cultural institutions by emphasizing that future research should incorporate longitudinal studies and qualitative visitor feedback in order to capture the overall impact of AI on engagement and sustainability in museums.

Boosting customers’ e-word of mouth in online shopping platforms: the role of artificial intelligence through a mixed-methods approach − a Vietnamese perspective

Purpose Anchoring on the social exchange theory (SET), this study aims to examine the impact of artificial intelligence (AI) on customer behavior within the e-commerce sector. This study investigates the interconnections between perceived benefits of AI, customer trust, customer satisfaction and electronic word of mouth (eWOM). Design/methodology/approach A mixed-methods approach was used in the Vietnamese e-commerce context. A quantitative survey of 291 respondents was conducted to examine the proposed relationships, while qualitative research involving semistructured interviews with 10 participants provided deeper insights. Thematic analysis of the interview data enriched and validated the quantitative findings, offering a holistic understanding of the phenomena. Findings The quantitative analysis indicates that perceived benefits of AI do not directly affect eWOM; however, they significantly influence eWOM indirectly via trust and customer satisfaction, which serve as full mediators. The qualitative findings reveal three primary themes: the impact of AI’s perceived advantages on trust and satisfaction, the indirect connection between AI’s benefits and eWOM and the mediating functions of trust and satisfaction within this framework. Originality/value This research addresses a gap in existing literature by analyzing the influence of AI on consumer behavior in a developing market, specifically focusing on Vietnam as a case study. The integration of quantitative and qualitative methods provides a comprehensive framework for analyzing AI-mediated customer interactions. The findings contribute to the theoretical framework of SET within the realm of AI, while also offering practical insights for businesses and policymakers. They emphasize strategies aimed at improving customer trust, satisfaction and eWOM in the context of e-commerce through the application of AI.

Derivatives as Risk Management Tools: Insights from a Systematic Review of the Literature

This study investigates how the management of corporate risks, particularly through the use of derivatives, influences a company’s capital structure and its overall characteristics. The investigation utilizes a systematic literature review (SLR) approach to analyse previous research on the strategies organizations implement to address financial, operational, and strategic risks using derivatives. The findings indicate that effective risk management, particularly through hedging, stabilizes cash flows, reduces costs linked to financial distress, and enhances the company’s market value. The study emphasizes the necessity of incorporating risk management practices into corporate governance frameworks to improve decision-making quality. Additionally, the study points out areas requiring further investigation and proposes future research on the role of artificial intelligence (AI) and machine learning in risk management. The study provides valuable insights for researchers and practitioners, aiding in the connection between academic knowledge and real-world applications in risk management.

The Role of Artificial Intelligence in Strategic Business Decision Making

Artificial Intelligence (AI) is transforming investment decision-making in financial institutions by leveraging machinelearning, natural language processing, and predictive analytics. These technologies enable rapid processing of vastdatasets, accurate market forecasting, and tailored investment strategies, leading to enhanced returns and operationalefficiencies. AI-driven tools analyze market data, sentiment, and alternative sources to uncover insights, optimizeportfolios, and improve risk management, offering a competitive edge in volatile markets. However, challenges such asalgorithmic biases, which can perpetuate unfair outcomes, cybersecurity vulnerabilities that threaten sensitive data,and regulatory complexities due to opaque AI models require robust oversight. This paper explores AI’s opportunities,including cost reduction and personalized services, alongside risks like over-reliance on automation and ethicalconcerns. Through case studies on hedge funds, robo-advisors, and high-frequency trading, we assess AI’s impact,emphasizing the need for human-AI collaboration to ensure ethical decision-making. We also examine future trends,such as sustainable investing and quantum computing, which promise to further reshape finance. Data analysishighlights AI’s potential and pitfalls, providing recommendations for responsible adoption, including transparency,staff training, and regulatory engagement, to balance innovation with accountability.

Invited Article: Al guided Dual Antiplatelet Therapy and Anticoagulation.

Artificial intelligence (AI) has emerged as a transformative tool in healthcare through data analysis, pattern recognition and predictive modeling capabilities. AI-driven approaches have the potential to positively transform patient care through personalized treatment regimens comprising antiplatelet and anticoagulant therapy. This review explores the integration of AI in guiding antithrombotic therapies, highlighting the potential to improve patient outcomes through personalized medicine. Following a rigorous screening process, a total of 15 studies from the PubMed database were included in the review. We further explore studies investigating the role of Al in anticoagulation choices for acute coronary syndrome, during PCI and for long-term treatment. We also explore studies of antiplatelet agent selection and duration, as well as AI-guided platelet function testing and genotyping. The few studies that exist have demonstrated the integration of AI into antiplatelet and anticoagulation therapy holds substantial promise for enhancing patient-specific treatment strategies in cardiovascular care. AI can provide predictive insights that could surpass less objective traditional approaches in accuracy and personalization. Furthermore, the development of AI-driven tools for therapy duration assessment, genetic testing, and mobile applications for patient monitoring underscores AI's role in supporting real-time clinical decision-making and improving patient adherence. Future studies will be crucial in order to address the current limitations in applicability and validate these AI systems with respect to patient centered outcomes.

The Role of Artificial Intelligence in Improving Maternal and Fetal Health During the Perinatal Period

The Role of Artificial Intelligence in Improving Maternal and Fetal Health During the Perinatal Period