Anatomy is taught in the early years of an undergraduate medical curriculum. The subject is volatile and of voluminous content, given the complex nature of the human body. Students frequently face learning constraints in these fledgling years of medical education, often resulting in a spiraling dwindling academic performance. Hence, there have been continued efforts directed at developing new curricula and incorporating new methods of teaching, learning and assessment that are aimed at logical learning and long-term retention of anatomical knowledge, which is a mainstay of all medical practice. In recent years, artificial intelligence (AI) has gained in popularity. AI uses machine learning models to store, compute, analyze and even augment huge amounts of data to be retrieved when needed, while simultaneously the machine itself can be programmed for deep learning, improving its own efficiency through complex neural networks. There are numerous specific benefits to incorporating AI in education, which include in-depth learning, storage of large electronic data, teaching from remote locations, engagement of fewer personnel in teaching, quick feedback from responders, innovative assessment methods and user-friendly alternatives. AI has long been a part of medical diagnostics and treatment planning. Extensive literature is available on uses of AI in clinical settings, e.g., in Radiology, but to the best of our knowledge there is a paucity of published data on AI used for teaching, learning and assessment in anatomy. In the present review, we highlight recent novel and advanced AI techniques such as Artificial Neural Networks (ANN), or more complex Convoluted Neural Networks (CNN) and Bayesian U-Net, which are used for teaching anatomy. We also address the main advantages and limitations of the use of AI in medical education and lessons learnt from AI application during the COVID-19 pandemic. In the future, studies with AI in anatomy education could be advantageous for both students to develop professional expertise and for instructors to develop improved teaching methods for this vast and complex subject, especially with the increasing paucity of cadavers in many medical schools. We also suggest some novel examples of how AI could be incorporated to deliver augmented reality experiences, especially with reference to complex regions in the human body, such as neural pathways in the brain, complex developmental processes in the embryo or in complicated miniature regions such as the middle and inner ear. AI can change the face of assessment techniques and broaden their dimensions to suit individual learners.

Finding robust culture conditions for in vitro maturation (IVM) of male germ cells is still a challenge. Recently, a testis organ culture method, using Knockout Serum Replacement (KSR), was suggested as a promising approach. However, the efficiency of that model is still not optimal. Hence, we have tried to establish the culture conditions in two laboratories, and to improve the reliability of the culture system to generate mature germ cells. Male mice at three days of age were sacrificed. Testes were cut into small pieces which were cultured atop agarose stands, using Minimum Essential Medium alpha supplemented with different supplements; melatonin, Glutamax, and different concentrations of KSR. The results showed that the duration of culture beyond 18 days had an impact on the number of differentiated germ cells. Supplementation with melatonin and Glutamax revealed a positive influence on the efficiency of male germ cell differentiation in vitro. Furthermore, the results confirmed that KSR had a positive effect on germ cell maturation and testosterone production, with a concentration of at least 10%. In conclusion, this study emphasizes the beneficial role of at least 10% KSR in the IVM of germ cells.

In recent times, online teaching and assessment have provided a great opportunity to explore better methods in medical education. There are inconsistent views concerning the effectiveness of online assessment. Hence, the present study aimed to evaluate online teaching and assessment methods in relation to face-to-face methods by comparing students’ performances. The students’ assessment results in two basic anatomy courses, which are part of the Doctor of Medicine and Biomedical Sciences programs at Sultan Qaboos University, were analysed. We compared the students’ mean scores and coefficient of variance in the multiple-choice written exams and the objective structured practical exams during the spring semesters of 2019, 2020, and 2021, containing face-to-face teaching and exams, partial online teaching and online exams, and online teaching and both proctored online and face-to-face exams, respectively. The sudden transition to online teaching and assessment halfway through the semester resulted in higher means and a lower coefficient of variance among students’ scores in both theory and practical exams. However, when the fully adopted online method of teaching and assessment was employed, the mean scores decreased, and the coefficient of variance increased to figures close to those witnessed before the pandemic, when teaching and assessment were face-to-face. This trend applied to both the Doctor of Medicine and Biomedical Sciences programs’ anatomy courses. The results indicate that online assessment of theoretical and practical anatomical knowledge is comparable to that of face-to-face assessment. However, proper planning and preparedness are mandatory to achieve the desired outcomes.

Organoids are self-organizing three-dimensional (3D) cellular structures derived from stem cells. They can mimic the anatomical and functional properties of real organs. They have transformed in vitro disease modeling by closely replicating the structural and functional characteristics of human tissues. The complexity and variability of organoid-derived data pose significant challenges for analysis and clinical translation. Artificial Intelligence (AI) has emerged as a crucial enabler, offering scalable and high-throughput tools for interpreting imaging data, integrating multi-omics profiles, and guiding experimental workflows. This review aims to discuss how AI is reshaping organoid-based research by enhancing morphological image analysis, enabling dynamic modeling of organoid development, and facilitating the integration of genomics, transcriptomics, and proteomics for disease classification. Moreover, AI is increasingly used to support drug screening and personalize therapeutic strategies by analyzing patient-derived organoids. The integration of AI with organoid-on-chip systems further allows for real-time feedback and physiologically relevant modeling. Drawing on peer-reviewed literature from the past decade, Furthermore, CNNs have been used to analyze colonoscopy and histopathological images in colorectal cancer with over 95% diagnostic accuracy. We examine key tools, innovations, and case studies that illustrate this evolving interface. As this interdisciplinary field matures, the future of AI-integrated organoid platforms depends on establishing open data standards, advancing algorithms, and addressing ethical and regulatory considerations to unlock their clinical and translational potential.

Human embryonic stem (hES) cells represent an important tool to study early cell development. The previously described use of human recombinant laminin (LN) 521 represented a step forward in generating clinically safe culture conditions. To test the short-term effect of LN521 on cultured hES cells, five male hES cell lines were cultured on human foreskin fibroblasts (hFFs), Matrigel, LN521, and LN121 and characterized by qPCR, immunofluorescence analysis, as well as their potential for three-germ layer differentiation. Variations in gene expression related to pluripotency, stemness, and testicular cells at different passages and culture conditions were evaluated by qPCR. All cell lines expressed pluripotency markers at protein and RNA level and were able to differentiate into cell types of the three germ layers after being cultured on LN521 for nine passages. Reduction in variation of pluripotency marker expression could be observed after culturing the cells on LN521 for nine passages. hES cells cultured on LN521 exhibited less differentiation, faster cell growth, and attachment when compared to hES cells cultured on LN121 or Matrigel. Our results indicate a positive effect of LN521 in stabilizing pluripotency gene expression and might be the first step towards more controllable and robust culture conditions for hES cells.

Background: ChatGPT has emerged as a valuable tool for enhancing scientific writing. It is the first openly available Large Language Model (LLM) with unrestricted access to its capabilities. ChatGPT has the potential to alleviate researchers’ workload and enhance various aspects of research, from planning to execution and presentation. However, due to the rapid growth of publications and diverse opinions surrounding ChatGPT, a comprehensive review is necessary to understand its benefits, risks, and safe utilization in scientific research. This review aims to provide a comprehensive overview of the topic by extensively examining existing literature on the utilization of ChatGPT in academic research. The goal is to gain insights into the potential benefits and risks of using ChatGPT in scientific research, exploring secure and efficient methods for its application while identifying potential pitfalls to minimize negative consequences. Method: The search was conducted in PubMed/MEDLINE, SCOPUS, and Google Scholar, yielding a total of 1279 articles and concluded on April 23 ^rd , 2023. After full screening of titles/abstracts and removing duplicates and irrelevant articles, a total of 181 articles were included for analysis. Information collected included publication details, purposes, benefits, risks, and recommendation regarding ChatGPT’s use in scientific research. Results: The majority of existing literature consists of editorials expressing thoughts and concerns, followed by original research articles analyzing ChatGPT’s performance in scientific research. The most significant advantage of using ChatGPT in scientific writing is its ability to expedite the writing process, enabling researchers to draft their work more efficiently. It also proves beneficial in improving writing style and proofreading by offering suggestions for sentence structure, grammar, and overall clarity. Additional benefits identified include support in data analysis, the formulation of protocols for clinical trials, and the design of scientific studies. Concerns mainly revolve around the accuracy and superficiality of the generated content, leading to what is referred to as “hallucinations.” Researchers have also expressed concerns about the tool providing citations to nonexistent sources. Other concerns discussed include authorship and plagiarism issues, accountability, copyright considerations, potential loss of diverse writing styles, privacy and security, transparency, credibility, validity, presence of bias, and the potential impact on scientific progress, such as a decrease in groundbreaking discoveries. Conclusion: ChatGPT has the potential to revolutionize scientific writing as a valuable tool for researchers. However, it cannot replace human expertise and critical thinking. Researchers must exercise caution, ensuring the generated content complements their own knowledge. Ethical standards should be upheld, involving knowledgeable human researchers to avoid biases and inaccuracies. Collaboration among stakeholders and training on AI technology are essential for identifying best practices in LLMs use and maintaining scientific integrity.