Breast cancer (BC) caused 685,000 deaths globally in 2020, earning the title of the most common type of tumor among females. With a multifactorial genesis, BC is influenced by several factors such as age, genetic and epigenetic predisposition, and an individual’s exposome, and its classification is based on morphological/histological, invasiveness, and molecular futures. Extracellular vesicles (EVs) are cell-derived lipid-bilayer-delimited nanoparticles, which are distinguishable by size, genesis, and the markers expressed in exosomes (40 to 150 nm), microvesicles (40 to 10,000 nm), and apoptotic bodies (100–5000 nm). Produced in physiological and pathological cellular contexts, EVs are shuttles of biological material and are implicated in cell-to-cell communications, thus attracting significant interest in diagnostic and drug delivery research. We report and discuss the latest evidence regarding the important role of EVs in BC, deepening their implication in tumorigenesis and metastatic mechanisms. On the other hand, the use of BC-derived EVs as prognostic biomarkers and therapeutic approaches is undergoing investigation. Hence, EVs have become new weapons in precision medicine; however, only with the support of advanced algorithms such as artificial intelligence (AI) can we develop a wide range of information. Looking ahead, it is possible to see the application of AI in the prognosis and diagnosis of different pathologies.

The progress of artificial intelligence (AI), particularly its core algorithms—machine learning (ML) and deep learning (DL)—has been significant in the medical field, impacting both scientific research and clinical practice. These algorithms are now capable of analyzing ultrasound images, processing them, and providing outcomes, such as determining the benignity or malignancy of thyroid nodules. This integration into ultrasound machines is referred to as computer-aided diagnosis (CAD). The use of such software extends beyond ultrasound to include cytopathological and molecular assessments, enhancing the estimation of malignancy risk. AI’s considerable potential in cancer diagnosis and prevention is evident. This article provides an overview of AI models based on ML and DL algorithms used in thyroid diagnostics. Recent studies demonstrate their effectiveness and diagnostic role in ultrasound, pathology, and molecular fields. Notable advancements include content-based image retrieval (CBIR), enhanced saliency CBIR (SE-CBIR), Restore-Generative Adversarial Networks (GANs), and Vision Transformers (ViTs). These new algorithms show remarkable results, indicating their potential as diagnostic and prognostic tools for thyroid pathology. The future trend points to these AI systems becoming the preferred choice for thyroid diagnostics.

Neuroendocrine neoplasms (NENs) comprise a heterogeneous tumor group arising from neuroendocrine cells, commonly originating in the gastroenteropancreatic tract and bronchopulmonary system. Their incidence has risen significantly, owing to improved diagnostic techniques and increased clinical recognition. While previous reviews have explored the molecular and genetic basis of NENs, limited attention has been given to the role of epigenetic modifications, particularly DNA methylation, in tumorigenesis and disease progression. This review focuses on lung, pancreas, and thyroid well-differentiated neuroendocrine tumors (NETs), highlighting epigenetic mechanisms, particularly DNA methylation, as promising biomarkers for early diagnosis and risk stratification. Aberrant DNA methylation can silence key tumor suppressor genes, including RASSF1A and CDKN2A, thereby promoting tumorigenesis. Integrating DNA methylation profiles with conventional biomarkers such as chromogranin A (CgA) may enhance diagnostic accuracy and inform therapeutic strategies. Emerging epigenetic therapies offer potential avenues for personalized treatment based on molecular profiling. Unlike prior reviews that broadly cover genetic and epigenetic changes in NENs, this review uniquely emphasizes the translational potential of epigenetic biomarkers in clinical practice. By synthesizing recent findings and evaluating their clinical implications, we aim to bridge the gap between molecular research and practical applications in diagnosis, prognosis, and therapy.

While the frequency of papillary thyroid carcinoma (PTC) has increased in recent decades, both due to improvements in diagnostic procedures and a real, effective percentage increase in cases, the frequency of medullary thyroid carcinoma (MTC), however, has remained almost unchanged, representing 3–5% of thyroid cancer cases. Our experience relates to the observation of cases with the synchronous presence of PTC and MTC, also in chronic autoimmune thyroiditis, and this led us to carry out a brief review of the literature on the subject, with the aim above all of identifying the most correct postoperative therapeutic process.

Thyroid nodule evaluation relies heavily on ultrasound imaging, yet it suffers from significant inter-operator variability. To address this, we present a preliminary validation of the Synergy-Net platform, an AI-driven Computer-Aided Diagnosis (CAD) system designed to standardize acquisition and improve diagnostic accuracy. The system integrates a U-Net architecture for anatomical segmentation and a ResNet-50 classifier for lesion characterization within a Human-in-the-Loop (HITL) workflow. The study enrolled 110 patients (71 benign, 39 malignant) undergoing surgery. Performance was evaluated against histopathological ground truth. The system achieved an Accuracy of 90.35% (95% CI: 88.2–92.5%), Sensitivity of 90.64% (95% CI: 87.9–93.4%), and an AUC of 0.90. Furthermore, the framework introduces a multimodal approach, performing late fusion of imaging features with genomic profiles (TruSight One panel). While current results validate the 2D diagnostic pipeline, the discussion outlines the transition to the ANTHEM framework, incorporating future 3D volumetric analysis and digital pathology integration. These findings suggest that AI-assisted standardization can significantly enhance diagnostic precision, though multi-center validation remains necessary.

Autoimmune thyroid diseases (AITDs) represent T cell-mediated, organ-specific autoimmune disorders caused by immune dysregulation, culminating in an immune-mediated attack on thyroid tissue. AITD etiopathogenesis is the result of the interplay between a genetic susceptibility and environmental factors; hypothyroidism and thyrotoxicosis are the respective clinical hallmarks of autoimmune thyroiditis and Graves’disease, the two main forms of AITD. The application of nanomedicine in the context of thyroid disorders ranges from nanodiagnosis and nanotherapy to nanotheranostics. Nanomedicine has been used to develop new sensitive methods for the determination of the TSH, iodine and TSAb. Furthermore, other studies have used nanomedicine to explore new treatments of autoimmune thyroiditis, Graves’disease and also thyroid eye disease. In the future, the application of nanomedicine will be personalized in accordance with individual genetic profiles, thus improving the therapeutic effectiveness and reducing the undesirable side effects with improved patient outcomes.