Saudi Arabia has made significant progress in its healthcare system through increased healthcare spending, improved healthcare infrastructure, and better quality of care. The government has introduced initiatives such as universal health coverage, accreditation programs, and healthcare technology adoption. As a result, there has been an increase in access to healthcare services and improvements in healthcare indicators. However, the system still faces challenges such as a shortage of healthcare workers, a lack of preventive care, and health disparities between urban and rural areas. Addressing these challenges is crucial for achieving a more equitable and sustainable healthcare system in Saudi Arabia.

Candida is the largest genus of medically significant fungi. Although most of its members are commensals, residing harmlessly in human bodies, some are opportunistic and dangerously invasive. These have the ability to cause severe nosocomial candidiasis and candidemia that affect the viscera and bloodstream. A prompt diagnosis will lead to a successful treatment modality. The smart solution of biosensing technologies for rapid and precise detection of Candida species has made remarkable progress. The development of point-of-care (POC) biosensor devices involves sensor precision down to pico-/femtogram level, cost-effectiveness, portability, rapidity, and user-friendliness. However, futuristic diagnostics will depend on exploiting technologies such as multiplexing for high-throughput screening, CRISPR, artificial intelligence (AI), neural networks, the Internet of Things (IoT), and cloud computing of medical databases. This review gives an insight into different biosensor technologies designed for the detection of medically significant Candida species, especially Candida albicans and C. auris, and their applications in the medical setting.

Medical anatomy is an essential preclinical course for medical undergraduates and provides a fundamental basis for various medical and surgical specializations. Frequently, students encounter difficulties when it comes to studying and comprehending the subject matter. Several pedagogical strategies have been devised and utilized throughout the years to enhance the process of teaching and learning in the field of anatomy. Artificial intelligence (AI) is now transforming anatomy education by utilizing modern technologies such as virtual reality, augmented reality, machine learning, and AI-powered evaluation tools. Recent research explored the AI in anatomy teaching, emphasizing its advantages and constraints. This review provides a thorough overview of the latest developments in the use of AI in anatomical education. It explores how AI-powered technologies can improve the educational experience for anatomy students, including personalized learning, automated grading, and intelligent tutoring systems, and examines the effects of these technologies on student engagement, learning outcomes, and teaching methods.

Microbiological spectrum and antimicrobial susceptibility patterns of various isolates from endotracheal tube aspirates in a tertiary care hospital, Hyderabad, Telangana-IJMR-Print ISSN No:-2394-546X Online ISSN No:-2394-5478Article DOI No:-10.18231/2394-5478.2018.0043,Indian Journal of Microbiology Research-IP Innovat

Human anatomy is not just a subject at the level of basic sciences, but also it forms the foundation of medical and allied health sciences. A thorough understanding of the structure of the human body is crucial for students to diagnose and treat various health conditions. Hence, providing a deep insight into the subject with more hours dedicated to practical sessions was the trend followed in previous times. The reduction of teaching hours for anatomy as a part of curriculum change, the difficulty faced by the students to learn the subject within a short span, and the emphasis on anatomy faculty to make it interesting have led to the adoption of various modern teaching strategies and technology to complete the task. The article intends to discuss the contemporary methods of teaching and learning anatomy, elaborating the wide range of approaches and techniques.

Artificial Intelligence (AI) has transformed numerous domains, including the discipline of medical microbiology. Artificial intelligence is currently being used to assist in clinical decision-making and the monitoring of diseases, with the possibility of being used for genomic information and extensive digital datasets. Through the utilization of advanced algorithms, machine learning (ML), and deep learning (DL) methods, artificial intelligence (AI) can improve disease diagnoses, forecast outbreaks, and customize medical treatments. Moreover, AI is revolutionizing the field of medical and pharmaceutical microbiology, specifically in the areas of pathogen identification, development of point-of-care diagnostics, and drug discovery. Machine learning (ML) is of great use for image analysis since it improves the effectiveness and accuracy of clinical microbiology practice. Despite these developments, it is imperative to tackle issues related to the accuracy of data and limitations of algorithms. Additionally, it is crucial to focus on creating AI models that can be easily understood and interpreted. This review examines the present uses, advantages, and obstacles of AI in medical microbiology, emphasizing its revolutionary impact on enhancing healthcare results.