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.

Introduction: Esophageal achalasia is a rare motility disorder characterized by impaired lower esophageal sphincter relaxation and absent peristalsis. Diagnostic tools such as high-resolution manometry (HRM) and functional lumen imaging probe (FLIP) have improved disease recognition; however, interpretation remains complex and highly operator dependent. Artificial intelligence (AI) has emerged as a promising approach to automate data analysis and enhance diagnostic accuracy, but its specific role in achalasia is not yet clearly defined. Content: A narrative review was conducted using PubMed, Scopus, and Web of Science, searching for studies published up to June 2025 that investigated AI applications in esophageal motility disorders, with particular attention to achalasia. Search terms included “artificial intelligence,” “machine learning,” “achalasia,” “esophageal motility,” and “high-resolution manometry.” Although no prospective or interventional studies directly evaluating AI in achalasia were identified, several retrospective proof-of-concept studies applied AI algorithms to HRM and FLIP data. These studies demonstrated the feasibility of automated classification of esophageal motility disorders, with high accuracy in differentiating motility subtypes potentially applicable to achalasia. Exploratory research on AI-assisted imaging and outcome prediction also showed encouraging results. Summary: Current evidence suggests that AI-based models can accurately analyze complex esophageal motility data and reduce interobserver variability. While direct clinical evidence in achalasia remains limited, existing studies provide a solid methodological foundation for AI-assisted diagnosis, classification, and clinical decision support in this condition. Outlook: Future research should focus on prospective validation, multicenter data collection, and multimodal integration of clinical, physiologic, and imaging data. With targeted development and ethical governance, AI has the potential to enhance diagnostic precision, support personalized treatment strategies, and advance precision motility care in patients with achalasia.

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.

Esophageal perforation in Zollinger–Ellison syndrome (ZES) is an exceedingly rare and life‐threatening manifestation of gastrinoma‐related acid hypersecretion. While peptic ulceration and severe reflux are recognized complications of ZES, perforation represents the ultimate consequence of uncontrolled hyperacidity. Evidence remains confined to isolated case reports with no prior systematic synthesis. A scoping review was conducted in accordance with PRISMA‐ScR and the Joanna Briggs Institute framework. A comprehensive search of PubMed, Embase, Scopus, and Web of Science was performed from database inception to September 2025. Eligible studies included any report describing esophageal perforation in confirmed or suspected ZES. Iatrogenic perforations were excluded. Data were extracted on patient demographics, clinical presentation, diagnostic modalities, management strategies, and outcomes. S even eligible cases published between 2001 and 2025 were identified. Patients were aged 44–63 years (4 males, 3 females). The distal esophagus was affected in approximately 70% of cases, usually after chronic peptic injury; three patients (≈43%) presented with spontaneous Boerhaave‐type rupture. Computed tomography was diagnostic in all cases, and endoscopy was used in six. Surgical repair with mediastinal drainage or T‐tube repair was the mainstay of management, while conservative therapy failed. Endoscopic stenting achieved successful leak control in one patient, whereas overall survival across all cases was approximately 86%, with one death due to delayed diagnosis and sepsis. Esophageal perforation in ZES represents a predictable but preventable endpoint of chronic acid injury. Rapid imaging, decisive surgical or endoscopic repair, and definitive acid suppression are essential to survival. Awareness of this rare complication among surgeons and gastroenterologists may facilitate early recognition and improve outcomes.

Purpose: Giant inguinal hernias (GIHs) are rare and technically demanding conditions, associated with loss of domain and abdominal wall compliance. Preoperative botulinum toxin type A (BtxA) has been increasingly used in complex ventral hernia repair to facilitate abdominal wall relaxation; however, its role in GIHs surgery remains poorly defined. This scoping review aimed to map the literature on preoperative BtxA use in GIHs repair, focusing on technical protocols, patient selection, and areas of variability. Methods: A scoping review was conducted in accordance with PRISMA-ScR guidelines. MEDLINE, Embase, Web of Science, and Scopus were searched from inception to 10 October 2025. Studies reporting preoperative BtxA administration in adult patients undergoing GIHs repair were included. Data were extracted descriptively and synthesized narratively. Results: Seven observational and non-comparative studies published between 2019–2025 were included, comprising a total of 16 patients. Substantial heterogeneity was observed in BtxA protocols, with total doses ranging from 100 to 450 units, injection timing between 2 and 8 weeks preoperatively, and injection sites varying from 6 to 18. In several reports, BtxA was used as part of a multimodal preoperative strategy including progressive pneumoperitoneum. All studies targeted the lateral abdominal wall musculature, employed imaging guidance, and performed bilateral injections. Patient selection criteria and outcome reporting were inconsistent. Conclusions: Preoperative BtxA use in GIHs repair is limited and heterogeneous. No standardized protocol can be identified. Further anatomically focused and systematically designed studies are required to clarify the role of BtxA and to establish standardized preoperative protocols for this challenging surgical condition.