2026· Journal of Basics and Applied Sciences Research· 0 citations
TL;DR
The key findings show that AI significantly improves diagnostic precision, supports personalized medicine, enhances real-time patient management system, and optimizes medication management through intelligent e-prescribing systems with accuracy of perception of drugs for each patient.
Abstract
Artificial Intelligence (AI) is rapidly transforming healthcare sector by improving diagnostic accuracy, enhancing drug discovery and pharmaceutical research, and advancing electronic prescription processing (e-prescribing; eRx), although its adoption in developing contexts remains limited. This study the role of artificial intelligence in transforming clinical practice and biomedical research: a review of opportunities and challenges presents systematic review of 20 peer-reviewed articles examining the role of AI in clinical practice and biomedical research. The key findings show that AI significantly improves diagnostic precision, supports personalized medicine, enhances real-time patient management system, and optimizes medication management through intelligent e-prescribing systems with accuracy of perception of drugs for each patient. Additionally, AI contributes to faster and more efficient biomedical research processes. However, challenges such as inadequate infrastructure, high implementation costs, data privacy concerns, and limited technical expertise persist, particularly in developing country. The review concludes that effective and sustainable AI integration in healthcare requires context-specific strategies, supportive policy frameworks, increased investment in digital infrastructure, and capacity building among healthcare professionals.
Introduction: The digital transformation of healthcare is accelerating, driven by unprecedented advancements in Artificial Intelligence (AI). From large language models (LLMs) to biomolecular structure prediction, AI is redefining modern diagnostic and therapeutic standards.
Aim: This review evaluates the current state of AI applications in medicine, focusing on clinical knowledge encoding, molecular drug discovery, and administrative workflow optimization, while critically addressing the technical, ethical, and systemic challenges of their institutional implementation.
Materials and Methods: A structured analysis was conducted utilizing a hybrid approach that combines a multi-decade bibliometric trend perspective with a detailed synthesis of 21 landmark publications, clinical trials, and meta-analyses from high-impact journals.
Results: AI demonstrates expert-level performance in medical knowledge retrieval and spatiotemporal diagnostics. AlphaFold 3 has revolutionized computational therapeutics through all-atom biomolecular interaction prediction, while ambient AI scribes significantly reduce physician burnout by automating clinical documentation workflows. However, data-driven "hallucinations" in LLMs and the inherent "black box" nature of deep learning architectures remain critical barriers to autonomous deployment.
Conclusions: AI is successfully transitioning from an isolated research tool into an essential clinical "co-pilot." Achieving its full potential in Medicine 4.0 requires robust frameworks for algorithmic explainability, global dataset diversification, and a strategic synergy between machine precision and human clinical judgment.
Aleksandra Stańczyk, Kinga Haduch, Zuzanna Michalska et al.· International Journal of Inn...· 0 citations
The transformative role of artificial intelligence (AI) in the pharmaceutical industry is examined, with a focus on its significant contributions to drug discovery, development, and clinical trial processes. It highlights the inefficiencies and high costs associated with traditional drug development and explores how AI and machine learning (ML) can enhance these processes by analyzing extensive biological datasets. The historical context of AI in pharmaceutical development is examined, noting how advances in computational power and data accessibility have facilitated innovative methodologies, such as predictive analytics and natural language processing. Contemporary trends reveal the integration of AI technologies in drug design, repurposing, and patient response forecasting. This study also addresses the challenges of participant recruitment for clinical trials and proposes AI-driven solutions to optimize patient selection and data management. Furthermore, it discusses AI's role in tailored medicine, emphasizing its potential for advancing precision therapy through targeted drug development and personalized treatment strategies. The importance of digital tools, genomic data analysis, and AI-driven imaging technologies for customizing therapeutic approaches is underscored, along with the regulatory and ethical challenges posed by AI deployment in healthcare. This study illustrates the complexities of AI applications in the pharmaceutical sector, offering insights into both successful and unsuccessful initiatives. The findings suggest that the digitalization of the pharmaceutical industry and enhanced AI integration hold promise for developing safer and more effective therapeutic strategies, while also identifying obstacles to their widespread adoption and optimal functionality.
Krishna Chandra Panda, B. R. Ravi Kumar, J. Sruti et al.· Reviews on recent clinical t...· 0 citations
Artificial intelligence (AI) is widely regarded as one of the most promising innovations in healthcare, yet its adoption in routine clinical practice remains limited. Only a small proportion of AI applications developed in research settings are successfully integrated into healthcare delivery. Major barriers include poor interoperability with existing health information systems, complex regulatory requirements, limited scientific evidence, and the lack of clear clinical guidelines. Many AI tools have been evaluated through methodologically weak studies, often retrospective and lacking external validation, contributing to skepticism among healthcare professionals. Additional challenges involve healthcare professionals' education and training, algorithm transparency, and the ability of healthcare organizations to effectively incorporate these technologies into clinical workflows. To promote the safe and effective adoption of AI, stronger clinical evidence, structured training programs, and organizational models capable of supporting its implementation are required. Addressing these issues is essential to ensure that AI can deliver meaningful benefits for patients, healthcare professionals, and healthcare systems.
Eugenio Santoro· Recenti progressi in medicin...· 0 citations
This study systematically analyzes research trends in health AI over the past six years through a systematic literature review (SLR) and a bibliometric analysis using VOSviewer to highlight dominant research areas, including machine learning for diagnosis, AI-driven hospital management, and predictive analytics.
Irwan Bastian, Aqilla Rahman Musyaffa, Lukman Nulhakim et al.· IAES International Journal o...· 0 citations
Artificial intelligence (AI) is becoming an important technology in modern healthcare because of its ability to analyze large volumes of clinical, biomedical, and patient-generated data. AI-based systems are being applied in disease diagnosis, medical imaging, drug discovery, personalized medicine, clinical decision support, remote monitoring, and healthcare administration. This review examines recent trends in the use of AI in healthcare by analyzing six influential studies published between 2017 and 2024. The reviewed literature indicates that machine learning, deep learning, explainable AI, and human–AI collaboration can improve diagnostic accuracy, efficiency, and patient-centered care. However, challenges related to data privacy, algorithmic bias, explainability, cybersecurity, clinical validation, regulation, and unequal access continue to restrict large-scale implementation. The review concludes that AI should be implemented as a supportive tool under appropriate clinical supervision, ethical standards, and regulatory frameworks.
Keywords: artificial intelligence; machine learning; deep learning; explainable AI; Medical Imaging; precision medicine
Unknown authors· International Journal of Tec...· 0 citations
A narrative review synthesises the current literature, regulatory frameworks, and ethical guidelines about the medicolegal dimensions of AI in healthcare, without adherence to a formal systematic review protocol, to address significant medico-legal and ethical challenges facing AI in healthcare.
M. Niriella, Krishanni Prabagar, I. Wijesingha et al.· AI and Ethics· 0 citations
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