Hospital Practices and Research

Abstract Background: Artificial Intelligence (AI) and Machine Learning (ML) have transformed computer-aided drug design (CADD) by leveraging big data and advanced algorithms to accelerate drug design. These technologies enhance the exploration of chemical spaces, prediction of drug-target interactions, and development of personalized therapeutics, particularly for complex diseases like cancer.
Objectives: This review aims to evaluate the role of AI and ML in CADD, focusing on their applications in high-throughput screening (HTS), three-dimensional (3D) protein structure prediction, and drug-target identification, while addressing challenges and future prospects.
Methods: A comprehensive analysis of recent literature (2005–2024) was conducted using scientometric tools like VOSviewer to identify trends and keywords in CADD. AI-driven methods, including deep learning frameworks (e.g., TensorFlow, AlphaFold2) and computational techniques (e.g., molecular dynamics simulations), were reviewed for their contributions to drug design.
Results: AI and ML have streamlined HTS, improved 3D protein structure prediction, and enhanced drug-target identification, reducing development timelines and costs. Tools like AlphaFold 2 and QuoteTarget have identified novel drug targets with high accuracy. However, challenges such as data quality, model interpretability, and ethical concerns persist. Interdisciplinary collaboration has driven innovation in personalized therapeutics.
Conclusion: AI and ML have revolutionized CADD, offering efficient and precise solutions for drug design. Overcoming data and ethical challenges through interdisciplinary efforts and advanced algorithms will further enhance the development of targeted therapies, reshaping therapeutic paradigms for complex diseases.

Abstract Background: Artificial Intelligence (AI), as a transformative technology, has found widespread applications in the health and hospital sectors.
Objectives: The present study aimed to analyze scientific articles related to AI in hospitals using text mining methods to identify dominant topics and emerging trends.
Methods: In the present study, text mining and topic modeling approaches were used to analyze research trends and identify dominant topics. The research steps included data collection from Scopus, text preprocessing, extraction of frequent words, topic modeling using Latent Dirichlet Allocation (LDA), and visualization. All steps were performed using the Python programming language and open-source libraries, such as NLTK, Gensim, Matplotlib, scikit-learn, and pyLDAvis.
Results: A total of 2238 records related to AI in hospitals were collected from Scopus since 2000. The terms "patient," "model," "machine learning," and "artificial intelligence" were identified as the most frequently used terms. The dominant topic clusters included "patient monitoring," "data-driven systems," "service innovation and emerging technologies," "clinical outcome prediction," "COVID-19 risk prediction," "mortality and hospitalization prediction," "health tourism," "management and implementation," and "hospital death prediction." Most articles were in the clusters "clinical outcome prediction modeling" (663 documents) and "mortality and hospitalization prediction" (335 documents). The publication trend has accelerated significantly since 2018, especially in the clusters "clinical outcome prediction" and "management and implementation."
Conclusion: Conclusion: Artificial intelligence in hospitals has grown rapidly over the last two decades. The shift from limited applications in modeling and prediction to interdisciplinary areas and innovative services indicates the gradual growth of this technology and its role in improving the quality of care, optimizing organizational processes, and developing new services.

Abstract Background: Human rights, which are very important for the health system and hospital accreditation requirements, include the issue of patient rights in hospitals. Artificial Intelligence (AI) is one of the advanced technologies in this industry, which aims to improve the standard of medical services for patients.
Objectives: This study was conducted with the aim of improving the enhancing patient's rights criteria in hospital accreditation standard by using AI (case study: selected hospitals in Zahedan county), which examines the factors affecting patient rights and the benefits of using AI.
Methods: According to the subject and purpose, the research approach is cross-sectional and descriptive, which was conducted in several hospitals in Zahedan in 2023. Experts in this field were given a questionnaire to complete as part of this study. Then the most important effects in this field were ranked.
Results: Experts ranked the factors of better disease diagnosis, prevention and prediction, advanced treatment methods, and easier access to medical data as the most important factors. The findings of this study indicate that if AI is used in selected hospitals of Zahedan, the rights of patients will be consistent with international validation criteria.
Conclusion: The factors covered in this study are necessary for the successful integration of AI in the health system, as well as following the guidelines that apply to any other intelligent system, including technical training and considering organizational, managerial and economic aspects. In order to intelligently adapt these systems, stakeholders in the health sector must use these components.

Abstract Background: Digital transformation through the use of technologies like blockchain, Artificial Intelligence (AI), and the Internet of Things (IoT) is the main focus of the 4th generation technology. In smart hospitals, the 4th generation technology boosts productivity while cutting expenses, permits monitoring and early diagnosis, and enhances the quality of medical services.
Objectives: This study was carried out with the intention of implementing the 4th generation technology tools in smart hospitals, given the current technological breakthroughs and the need for smart hospitals in Iran.
Methods: The current research is bibliometric in nature. All the papers published between 2000 and 2024 that had focused on the themes of smart hospitals and the 4th generation technologies which contained keywords like blockchain, AI, the IoT, and smart hospitals make up the study's population. Advanced searches in the citation ScienceDirect and PubMed databases were used to gather the data for this investigation. The citation network was visualized and examined using VOSviewer software.
Results: According to this survey, when it comes to the use of the 4th generation technology tools in smart hospitals, 40% of the articles mention improving patient care, 35% highlight operational efficiency, and 25% stress data driven decision making as important elements.
Conclusion: In addition to boosting patient safety, monitoring, early diagnosis, and the quality of medical services, the 4th generation technology tools are essential for converting a conventional hospital into a smart hospital and cutting expenses. These instruments are therefore crucial for enhancing patients' comfort and well-being in smart hospitals.

Abstract Background: Smart hospitals today use Artificial Intelligence to improve the quality of their services. In this sense, optimizing the patient's electronic medical record is one of the most significant issues that these hospitals face.
Objectives: This study aimed to determine the role of AI in patient electronic records in a smart hospital.
Methods: This study was a systematic review, with keywords searched in PubMed, Scopus, Google Scholar, and SID databases. In Persian and English, the keywords were artificial intelligence algorithms, electronic medical records, service quality, and hospital. The inclusion criteria included publication in Persian or English, full-text papers, current publications, and a focus on the use of AI in electronic medical records. Finally, about 57 papers related to the investigation were picked.
Results: After reviewing previous related studies, it was discovered that AI can play a role in various aspects of electronic patient records, such as disease diagnosis, predicting relapse and recovery periods, improving treatment accuracy and reducing medical errors, digital care, and decision-support systems. This can result in a 20-30% improvement in resource planning, a 30% decrease in wait times, better resource use, and more accurate predictions.
Conclusion: Leveraging AI in electronic patient records is critical for maximizing benefits while minimizing hazards. Despite the limitations, AI has the potential to become a critical tool for smart hospitals in improving healthcare delivery and efficiency. Accordingly, healthcare leaders that incorporate AI algorithms into their systems can give more effective and up-to-date care to their patients.