Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning and natural language processing, and their applications in HNC. The integration of AI with imaging techniques, genomics, and electronic health records is explored, emphasizing its role in early detection, biomarker discovery, and treatment planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, and the need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, and real-time monitoring systems are poised to further advance the field. Addressing these challenges and fostering collaboration among AI experts, clinicians, and researchers is crucial for developing equitable and effective AI applications. The future of AI in HNC holds significant promise, offering potential breakthroughs in diagnostics, personalized therapies, and improved patient outcomes.

Patients with facial trauma may suffer from injuries such as broken bones, bleeding, swelling, bruising, lacerations, burns, and deformity in the face. Common causes of facial-bone fractures are the results of road accidents, violence, and sports injuries. Surgery is needed if the trauma patient would be deprived of normal functioning or subject to facial deformity based on findings from radiology. Although the image reading by radiologists is useful for evaluating suspected facial fractures, there are certain challenges in human-based diagnostics. Artificial intelligence (AI) is making a quantum leap in radiology, producing significant improvements of reports and workflows. Here, an updated literature review is presented on the impact of AI in facial trauma with a special reference to fracture detection in radiology. The purpose is to gain insights into the current development and demand for future research in facial trauma. This review also discusses limitations to be overcome and current important issues for investigation in order to make AI applications to the trauma more effective and realistic in practical settings. The publications selected for review were based on their clinical significance, journal metrics, and journal indexing.

Background/Objectives: Fracture-related infections (FRIs) are a significant complication in maxillofacial trauma, leading to adverse outcomes such as prolonged healing, nonunion, and osteomyelitis. Despite advancements in surgical techniques, the incidence of FRIs remains concerning, particularly for mandibular fractures. This systematic review and meta-analysis aims to evaluate the incidence, risk factors, and management strategies for FRIs in oral and maxillofacial trauma. Methods: A systematic search of Medline and Embase databases was conducted, including studies up to February 2024, adhering to PRISMA guidelines. Eligible studies included randomized controlled trials, cohort studies, and case-control studies focusing on the incidence, risk factors, or treatment outcomes of FRIs. Data on patient demographics, fracture type, infection rates, and management strategies were extracted and analyzed. Statistical analyses included pooled infection rates, stratified by anatomical sites, using fixed and random-effects models. Results: A total of 72 studies were included, with a pooled FRI rate of 5.6%. Mandibular fractures exhibited the highest infection rate at 8.9%, while midface fractures had the lowest at 0.9%. The significant risk factors identified included smoking, substance abuse, and comorbidities such as diabetes. Delayed surgical intervention and poor periodontal health were also associated with higher infection rates. The use of prophylactic antibiotics did not show significant differences in infection prevention. Conclusions: FRIs in maxillofacial trauma are influenced by multifactorial risks, including anatomical, patient-specific, and procedural factors. Mandibular fractures are particularly vulnerable, requiring targeted preventive strategies and timely intervention. Standardized definitions and guidelines are essential for improving consistency in diagnosis and management. Future research should focus on high-quality trials to establish evidence-based approaches for FRI prevention and treatment.