Background: Artificial intelligence (AI) is increasingly investigated for radiographic assessment of peri-implant bone conditions, although radiographic marginal bone loss does not alone establish a clinical diagnosis of peri-implantitis. This narrative review aimed to critically synthesize current evidence on AI-assisted radiographic assessment of peri-implant marginal bone levels and bone loss. Methods: A structured literature search was conducted in PubMed/MEDLINE and Scopus. Eligible original studies applying AI or machine-learning methods to human dental radiographs for peri-implant bone assessment were included in a qualitative synthesis. Results: Seventeen studies were included. Periapical radiographs were the predominant imaging modality, while panoramic radiographs and CBCT were less frequently investigated. AI applications included implant and landmark localization, marginal bone-level measurement and quantification, bone-loss severity and defect-morphology classification, and prediction using imaging-derived features. Reported performance was generally promising, although substantial heterogeneity was observed in datasets, target tasks, reference standards, validation strategies, and performance metrics. Conclusions: Current radiograph-based AI systems show potential for image analysis, quantitative assessment, and clinical decision support. However, radiographic AI performance should not be interpreted as autonomous diagnostic accuracy for peri-implantitis. External validation, longitudinal assessment, and integration with relevant clinical parameters remain priorities for future research.
Biological data have grown exponentially due to single-cell multi-omics, high-content imaging, and large-scale clinical records; this has transformed modern biology. The landscape has shifted to one of data abundance, and the current approaches to computational analysis are not up to the task of dealing with the scale and complexity of this change. The information in big biological data can be characterized by the volume it occupies, velocity, variety, veracity, and value, and each of these features presents its own challenges to the researcher. Artificial intelligence (AI), including machine learning (ML), deep learning (DL), and natural language processing (NLP), has progressively turned into a requirement in tackling these issues. This chapter discusses how AI can be used to curate, represent, learn, and model the analysis of big biological data. Questions include data curation via methods to compress, represent, and outline big biological data, how to learn representations, how to model with big biological data, and how to translate into clinical problems. Using the conversation of genomics, proteomics, transcriptomics, structural biology, imaging, and electronic health records, this chapter reveals how AI is transformative in bioinformatics. In conclusion, the chapter points to enduring challenges, namely interpretability, data governance, and reproducibility, and provides a step into the future of explainable, trustworthy, and reproducible AI in biology.
(1) Introduction: Artificial intelligence (AI) is transforming healthcare and has particular relevance in military medicine, where austere environments, uncertainty, and time-critical decisions are common; (2) Methods: A literature review was conducted in PubMed/MEDLINE, Scopus, and Web of Science, covering studies published between 2000 and 2026. Relevant articles on AI in military medicine were identified and grouped into three domains: clinical, operational, and educational; (3) Results: Forty-one studies were included. In the clinical domain, AI supports decision-making, trauma care, medical imaging, and mental health, including early detection of post-traumatic stress disorder (PTSD). Operational applications include physiological monitoring, triage optimization, and medical evacuation. In education, AI enhances medical simulation and adaptive learning. Key limitations include poor data quality and availability, algorithmic bias, and limited validation in real operational settings; (4) Conclusions: AI can substantially improve military medical practice by increasing diagnostic accuracy, supporting rapid decisions, and improving operational efficiency. However, implementation requires rigorous clinical validation, transparent and reliable systems, and careful attention to ethical and organizational issues. AI should be used as a complementary tool that supports, rather than replaces, clinical judgment.
Background: Digital transformation in cardiology is reshaping diagnosis, monitoring, and therapy through connected devices, digital biomarkers, telemedicine, and artificial intelligence. This paper synthesizes recent advances and emphasizes the shift from episodic assessment to continuous surveillance and personalized predictive care. Methods: The article included 81 studies published between 2015 and 2026, identified in PubMed, Google Scholar, and ScienceDirect, encompassing clinical trials, meta- analyses, observational studies, and reviews focused upon continuous cardiovascular monitoring, remote care, and AI-supported analysis. According to the selection criteria, studies with weak methodology or limited clinical relevance were removed. Results: Continuous monitoring enables early detection of physiological changes, individualized prevention, reduces hospitalizations, and supports cost efficiency and the expansion of remote services. Artificial intelligence and multimodal analytics facilitate dynamic risk stratification, clinical decision support, and the development of cardiovascular digital twin models for precision medicine. Implementation remains constrained by requirements for robust validation, potential algorithmic bias, data protection and cybersecurity concerns, and regulatory or acceptance barriers. Conclusions: Digital cardiology enables anticipatory, patient- centered healthcare based on continuous monitoring and AI as core clinical tools, promoting integrated patient–hospital–home ecosystems, real-time therapeutic guidance, and sustained cardiovascular prevention. The priorities defined by these directions are subjects for further research and healthcare development worldwide in the next decades. improves
Recent advances in technology and artificial intelligence have transformed radiology. Artificial intelligence, which uses machine learning, deep learning algorithms, and cellular neural networks, is now applicable to image analysis, enabling the analysis of large volumes of data and the early identification of lesion features, such as tumors. Integrating artificial intelligence into clinical workflows increases the efficiency of medical report generation and enables efficient prioritization of complex or urgent cases. Predictive analytics based on artificial intelligence uses extensive patient data, including demographics, medical history, tests, and imaging, to build more accurate predictive models. Among the areas where artificial intelligence is successfully implemented in radiology is the automated detection of lung nodules. In the field of neurology, artificial intelligence helps track progressive lesions through volumetric analysis of the brain and surveillance of demyelinating diseases. In oncologic radiology, artificial intelligence is used for automated image segmentation, lesion review, and standardized report generation. Therefore, in this ever-evolving landscape, radiologists must embrace emerging technological advances in order to occupy a unique position at the intersection of different diagnostic fields.
The SARS-CoV-2 pandemic challenged the authorities into taking measures that limit the spread of the virus in the community. TAMEC project (Advanced Community Approaches to Epidemic Management) developed a system that detects the contacts of patients infected with the SARS-CoV-2 virus and alerts them using mobile applications. It allows real-time analysis of confirmed patients and identified contacts, early detection of new outbreaks, and provides decision- making information to prevent viral transmission. The system uses its patent for homomorphic encryption, a method that allows to process and manipulate data in an encrypted format, without having access to the original data. Thus, the solution proposed in the TAMEC project fully respects the privacy rules of citizens imposed by the EU, using the concept of privacy preservation. Our solution offers the possibility to identify and validate a risk score that could become an extremely helpful tool in the stratification of COVID-19 patients. The TAMEC system is innovative in its simplicity and ability to facilitate the prevention of the spread of the SARS-CoV-2 virus in the community
