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.

Background: Artificial intelligence (AI) Healthcare applications are listed in the national visions of some Gulf Cooperation Council countries. A successful use of AI depends on the attitude and perception of medical experts of its applications. Objective: To evaluate physicians and medical students’ attitude and perception on AI applications in healthcare. Method: A web-based survey was disseminated by email to physicians and medical students. Results: A total of 293 (82 physicians and 211 medical students) individuals have participated (response rate is 27%). Seven participants (9%) reported knowing nothing about AI, while 208 (69%) were aware that it is an emerging field and would like to learn about it. Concerns about AI impact on physicians’ employability were not prominent. Instead, the majority (n=159) agreed that new positions will be created and the job market for those who embrace AI will increase. They reported willingness to adapt AI in practice if it was incorporated in international guidelines (30.5%), published in respected scientific journals (17.1%), or included in formal training (12.2%). Almost two of the three participants agreed that dedicated courses will help them to implement AI. The most commonly reported problem of AI is its inability to provide opinions in unexpected scenarios. Half of the participants think that both the manufacturer and physicians should be legally liable for medical errors occur due to AI-based decision support tools while more than one-third (36.77%) think that physicians who make the final decision should be legally liable. Senior physicians were found to be less familiar with AI and more concerned about physicians’ legal liability in case of a medical error. Conclusion: Physicians and medical students showed positive attitudes and willingness to learn about AI applications in healthcare. Introducing AI learning objectives or short courses in medical curriculum would help to equip physicians with the needed skills for AI-augmented healthcare system.

Early Onset Colorectal cancer (EOCRC) incidence is increasing at an alarming pace. An increase of 90% in colon cancer and 124% in rectal cancer is expected by 2030. Patients with EOCRC are not receiving additional attention compared to older patients despite having a unique molecular pattern, majority of cases are sporadic, and related short- and long-term treatment and disease complications. The current management and screening guidelines have been constructed from studies on late onset CRC. Plethora of studies are ongoing to understand this disease entity in order to construct a tailored prevention, detection and management plans. While waiting for a better understanding of the disease, efforts should be directed toward improving the quality of care across the cancer continuum. Here we aim to address the challenges faced by EOCRC patients across the cancer continuum. This will facilitate directing future efforts and research toward construction of a personalized and precise guidelines.

This paper offers an in-depth review of the latest advancements in the automatic generation of medical case-based multiple-choice questions (MCQs). The automatic creation of educational materials, particularly MCQs, is pivotal in enhancing teaching effectiveness and student engagement in medical education. In this review, we explore various algorithms and techniques that have been developed for generating MCQs from medical case studies. Recent innovations in natural language processing (NLP) and machine learning (ML) for automatic language generation have garnered considerable attention. Our analysis evaluates and categorizes the leading approaches, highlighting their generation capabilities and practical applications. Additionally, this paper synthesizes the existing evidence, detailing the strengths, limitations, and gaps in current practices. By contributing to the broader conversation on how technology can support medical education, this review not only assesses the present state but also suggests future directions for improvement. We advocate for the development of more advanced and adaptable mechanisms to enhance the automatic generation of MCQs, thereby supporting more effective learning experiences in medical education.

Early-onset colorectal cancer (EOCRC) incidence is increasing worldwide. Efforts are directed to understand the biological and clinical signatures of EOCRC compared to late-onset colorectal cancer (LOCRC). EOCRC is thought to present differently across different ethnic groups and geographical regions. This study was an attempt to contribute with data from the Arab world toward the understanding of the clinicopathological parameters of EOCRC compared to LOCRC. Data from 254 CRC patients diagnosed at Sultan Qaboos University Hospital from the period 2015–2020 were studied. About 32.6% of all diagnosed CRC patients are below 50 years old, with no differences in gender distribution between EOCRC and LOCRC (p-value 0.417). Rectal involvement and tumor laterality were comparable among the two groups. Adenocarcinoma accounts for 83.3% and 94.2% of EOCRC and LOCRC, respectively. More mucinous and signet ring adenocarcinoma (8.3% each) were reported in EOCRC than LOCRC (2.9% and 2.2%, respectively). MLH1 and PMS2 loss are more common among LOCRC, but MSH6 loss is more frequent in EOCRC. The overall survival of EOCRC and LOCRC was comparable (median survival 64.88 and 67.24 months, respectively). This study showed comparable clinicopathological parameters between EOCRC and LOCRC from Arabs, which adds to the bigger picture of understand the disease.

The rising incidence and mortality of early-onset colorectal cancer (EOCRC) emphasize the urgent need for effective non-invasive screening. Circulating microRNAs (miRNAs) have emerged as promising biomarkers for cancer detection. This systematic review aims to evaluate the diagnostic performance of circulating miRNAs in detecting colorectal cancer (CRC). A literature search was conducted in PubMed and Scopus. Studies that report sensitivity, specificity, or area under the curve (AUC) for CRC detection by miRNA were included. The miRNA miR-21 was the most frequently studied biomarker, with a varying range of AUC from 0.55 to 0.973 attributed to differences in study populations and methodologies. The miRNAs miR-210 and miR-1246 showed potential diagnostic capacity with miR-1246 achieving an AUC of 0.924, 100% sensitivity, and 80% specificity. The miRNA panels offer improved diagnostic performance compared to individual miRNA. The best performing panel for CRC patients below 50 is miR-211 + miR-25 + TGF-β1 with AUC 0.99 and 100 specificity and 97 sensitivity. Circulating miRNAs hold significant promise as non-invasive biomarkers for CRC screening. However, the variability in diagnostic performance highlights the need for a standardized method and robust validation studies. Future research should focus on large-scale, ethnically diverse cohorts to establish clinically relevant miRNA biomarkers for CRC, particularly in younger populations.