artificial intelligence

Dentistry is an ever-evolving field that has seen significant advances in recent years. This article sheds light on some of the current and emerging trends in oral health care, including digital dentistry, regenerative medicine, and the use of lasers. For example, digital dentistry involves the use of computer-aided design and manufacturing technology, which enables more accurate and efficient production of dental devices. On the other hand, regenerative medicine and nanoDentistry can be considered promising area that combines the use of stem cells, growth factors, biomaterials, and nanotechnology to regenerate damaged tissue and improve treatment outcomes. Lasers are increasingly being used in dentistry for a range of applications, including the treatment of gum disease and teeth whitening. Other developing technologies such as 3D printing and artificial intelligence are also being increasingly incorporated into dentistry, providing improved treatment options for our patients. Last yet definitely would/will not least, controlled drug delivery systems are being developed to deliver drugs to specific target sites in a localized and sustained manner, reducing the risk of adverse effects. Currently, these emerging trends are transforming the landscape of odontology and beyond. Hence, in this mini-Review, we explore such trends in oro-dental and cranio-maxillo-facial indications to highlight the potential benefits, advancements, and opportunities of applications for improved patient care.

This paper shows the developments and directions in feature processing. We begin by revisiting conventional feature processing methods, then focus on deep feature extraction techniques and the application of feature processing. The article also analyzes the current research challenges and outlines future development directions, providing valuable insights in related fields.

Cancer is a highly heterogeneous and dynamic disease whose progression, metastasis, therapeutic resistance, and immune escape are strongly regulated by the tumor microenvironment (TME). However, conventional two-dimensional (2D) cell culture systems and animal models often fail to recapitulate the structural organization, multicellular interactions, biochemical gradients, and mechanical properties of native tumors, thereby limiting the translational efficiency of preclinical cancer research and drug development. In recent years, in vitro three-dimensional (3D) biomimetic tumor models-including tumor spheroids, tumor organoids, and tumor-on-a-chip systems—have emerged as powerful platforms for reconstructing physiologically relevant tumor microenvironments and investigating complex tumor behaviors.In this review, we systematically summarize the construction principles, biological characteristics, advantages, and limitations of major 3D biomimetic tumor models. We further discuss their recent applications in drug screening, precision medicine, tumor heterogeneity research, cancer stem cell investigation, metastasis, therapeutic resistance, and immunotherapy evaluation. Particular emphasis is placed on the comparative advantages of different 3D systems in modeling dynamic tumor–microenvironment interactions and supporting translational oncology research. Additionally, we will discuss the current problems of vascularisation, extracellular matrix biomimetics, experimental reproducibility, standardisation, and large-scale clinical translation. Finally, we present some new directions for future work, including three-dimensional bioprinting, multi-omics technology, artificial intelligence, and multi-organ-on-a-chip platforms, which may further improve the physiological relevance and predictive power of next-generation tumor models.In short, this review has listed the current progress of 3D biomimetic tumour modelling and discussed some prospects for its use in mechanistic studies of cancer, drug discovery, etc.

Psychiatric disorders represent some of the most biologically complex challenges in medicine, arising from intricate interactions among genetic, epigenetic, environmental, developmental, and social factors. Advances in artificial intelligence (AI) have improved our ability to analyze large-scale biological datasets, identify biomarkers, and support precision medicine initiatives. However, the growing volume and complexity of genomic and multi-omic information increasingly challenge the capabilities of even the most advanced conventional supercomputers. Quantum computing offers a potential next step in biomedical discovery by enabling rapid analysis of multidimensional datasets, molecular simulations, and optimization problems relevant to genetic medicine and gene therapy. Extending these findings conceptually, we propose the forward-looking hypothesis that continued advances in quantum computing may eventually complement artificial intelligence and human expertise to facilitate increasingly sophisticated analyses relevant to psychiatric genetics and precision medicine. At present, no direct evidence of which we are aware demonstrates clinical implementation of quantum computing in psychiatric genomics. Accordingly, the concepts discussed in this Opinion should be viewed as a forward-looking scientific perspective that builds upon current advances in computational science and biomedicine while awaiting future experimental and clinical validation. Importantly, these advances should complement rather than replace human expertise. Human-in-the-loop systems remain essential for ensuring scientific rigor, ethical oversight, clinical judgment, and patient-centered care. The convergence of quantum computing, AI, genetic medicine, and human expertise may ultimately establish a transformative framework for future precision psychiatry and mental health therapeutics.

The COVID-19 pandemic accelerated the development and application of artificial intelligence (AI) across disease surveillance, epidemiological forecasting, medical imaging, prognosis, drug discovery, vaccine research, contact tracing, and public-health decision support. This review synthesizes the literature on these applications while critically examining the evidence needed for translation into clinical and public-health practice. A structured literature-search and selection framework was used to organize evidence published from the emergence of COVID-19 through the contemporary literature, with emphasis on peer-reviewed studies and recent reviews. The evidence was synthesized according to the pandemic-response pathway: early warning and surveillance; diagnosis and differential diagnosis; severity and prognosis; clinical and resource decision support; therapeutic and vaccine discovery; genomic surveillance; and privacy-preserving and responsible AI. Across these domains, AI demonstrates potential to process heterogeneous data and support time-sensitive decisions, but reported performance does not by itself establish clinical utility. Recurring limitations include small or non-representative datasets, dataset shift, limited external validation, class imbalance, information leakage, insufficient calibration and interpretability, privacy and security risks, and uncertainty about workflow integration and regulatory accountability. Recent systematic reviews also indicate that multimodal, federated, and privacy-preserving approaches remain less developed than conventional centralized models. The principal contribution of this review is a unified critical framework that links technical performance with data quality, external validation, clinical relevance, fairness, privacy, interpretability, and implementation readiness. The review therefore identifies not only where AI has been applied to COVID-19, but also the conditions required for responsible translation to clinical practice and future pandemic preparedness.