Spatial memory, a fundamental cognitive function, enables individuals to encode, store, and retrieve information about their surroundings. Traditional assessment methods, such as paper-based tests and laboratory paradigms, often lack ecological validity and fail to capture the complexities of real-world navigation. Recent advancements in digital technologies, particularly virtual reality (VR) and mixed reality (MR), have introduced innovative tools for more immersive and accurate spatial memory assessments. VR provides controlled, replicable environments that simulate real-world navigation, while MR enhances engagement by blending virtual elements with physical spaces. This narrative review explores the cognitive mechanisms underlying spatial memory, highlighting the roles of egocentric and allocentric reference frames, as well as the neural substrates involved. The review also examines key factors influencing spatial memory performance, such as age, sex, neurological and neurodegenerative diseases. Digital tools such as the virtual Morris water maze and the VR Supermarket Test have been shown to possess enhanced ecological validity and diagnostic potential, particularly in the context of detecting early cognitive decline in Alzheimer’s disease. However, the field confronts several challenges, including the necessity for standardized protocols, the potential for adverse effects such as cybersickness, and the substantial cost associated with VR and MR systems. Future research directions in this field should include the integration of artificial intelligence for personalized assessments, and the combination of VR and MR tasks with neurophysiological techniques to advance understanding of spatial memory. Standardization, accessibility, and the creation of adaptive assessment for clinical populations will be crucial for optimizing the use of digital technologies in spatial memory research.

The enormous global burden of cancer has created the need to develop cutting-edge strategies for enhancing public education on cancer. Over the years, conventional educational strategies, such as the use of posters and leaflets, have been preferentially employed as public education strategies on oral cancer; however, the use of digital education-based strategies has been largely underutilized. Notably, the use of digital education-based strategies, particularly serious games, has proven to be a superior cancer education strategy, when compared to conventional strategies, due to their rigorous design and features. This commentary discusses serious games as a game changer in cancer education, itemizing their diverse roles in cancer prevention, advocacy, and management. Also, this commentary also detailed those factors that might limit the use and availability of serious games in resource-limited settings.

In recent years, patient engagement has emerged as a cornerstone in clinical decision-making, medical research, and health policy development, with its multifaceted value widely recognized by stakeholders across the healthcare continuum. However, digital health technologies, which are designed to enhance patient engagement, often fall short of their full potential due to developers’ limited understanding of patients’ needs and preferences. This perspective paper argues for adopting a patient-centered approach, emphasizing the critical importance of developers immersing themselves in patient communities to gain richer insights into patients’ lived experiences. Such an approach can lead to improved usability of digital health tools, enhanced user experience, and increased patient motivation, ultimately fostering more effective patient engagement in medical practice. Although challenges persist in the effective collection, analysis, and implementation of user feedback, prioritizing patient engagement remains crucial for optimizing health outcomes and enhancing the overall patient experience. By embracing this approach, developers can bridge the gap between technological innovation and patient needs, promoting more meaningful interactions and ultimately contributing to the advancement of healthcare systems and improved population health.

This narrative review aims to appraise the evidence on artificial intelligence models for early diagnosis and risk stratification of oral cancer, focusing on data modalities, methodology differences, applications in the diagnostic flow and models’ performance. Models for early diagnosis and screening provide non-invasive diagnosis without the need for specialized instruments, which is ideal for early detection as a low-cost system. Supervised learning with well-annotated data provides reliable references for training the models, and therefore, reliable and promising results. Risk prediction models can be built based on medical record data, demographic data, clinical/histopathological descriptors, highly standardized images or a combination of these. Insights on which patients have a greater chance of malignancy development or disease recurrence can aid in providing personalized care, which can improve the patient’s prognosis. Artificial intelligence models demonstrate promising results in early diagnosis and risk stratification of oral cancer.