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: 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.