The application of Artificial Intelligence (AI) facilitates medical activities by automating routine tasks for healthcare professionals. AI augments but does not replace human decision-making, thus complicating the process of addressing legal responsibility. This study investigates the legal challenges associated with the medical use of AI in radiology, analyzing relevant case law and literature, with a specific focus on professional liability attribution. In the case of an error, the primary responsibility remains with the physician, with possible shared liability with developers according to the framework of medical device liability. If there is disagreement with the AI’s findings, the physician must not only pursue but also justify their choices according to prevailing professional standards. Regulations must balance the autonomy of AI systems with the need for responsible clinical practice. Effective use of AI-generated evaluations requires knowledge of data dynamics and metrics like sensitivity and specificity, even without a clear understanding of the underlying algorithms: the opacity (referred to as the “black box phenomenon”) of certain systems raises concerns about the interpretation and actual usability of results for both physicians and patients. AI is redefining healthcare, underscoring the imperative for robust liability frameworks, meticulous updates of systems, and transparent patient communication regarding AI involvement.

Transcatheter arterial chemoembolization (TACE) is a proven and widely accepted treatment option for hepatocellular carcinoma and it is recommended as first-line non-curative therapy for BCLC B/intermediate HCC (preserved liver function, multifocal, no cancer-related symptoms) in patients without vascular involvement. Different types of TACE are available nowadays, including TAE, c-TACE, DEB-TACE, and DSM-TACE, but at present there is insufficient evidence to recommend one TACE technique over another and the choice is left to the operator. This review then aims to provide a comprehensive overview of the current literature on indications, types of procedures, safety, and efficacy of different TACE treatments.

Sarcopenia, an extremely common condition in cancer patients, is described as a progressive and generalized musculoskeletal disorder that is associated with an increased likelihood of adverse outcomes, including falls, fractures, physical disability, and mortality. By contrast, cachexia is defined as a syndrome characterized by weight loss with the concomitant loss of muscle and/or fat mass. Cancer cachexia leads to functional impairment, reduced physical performance, and decreased survival, and is often accompanied by cancer progression and reduced response to therapy. The literature states that cancer patients with cachexia or sarcopenia have many more complications than patients without these conditions. The interplay between physiologic sarcopenia and cancer cachexia is, in part, responsible for the complexity of studying wasting disorders in the cancer population, particularly in the geriatric population. For these reasons, a comprehensive assessment of the body composition and physical function of these patients is necessary. There are several modalities adapted to measure skeletal muscle mass, such as dual-energy X-ray absorptiometry (DEXA), bioelectrical impedance analysis (BIA), computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US). The gold standard for the measurement of quantitative and qualitative changes in body composition in patients with cancer is the analysis of tissue density using a CT scan. However, this technique remains poorly implemented in clinical practice because of the use of ionizing radiation. Similarly, DEXA, MRI, and US have been proposed, but their use is limited. In this review, we present and compare the imaging techniques that have been developed so far for the nutritional assessment of cancer patients.

Pulmonary embolism (PE) remains a critical condition that demands rapid and accurate diagnosis, for which computed tomographic pulmonary angiography (CTPA) is widely recognized as the diagnostic gold standard. However, recent advancements in imaging technologies—such as dual-energy computed tomography (DECT), photon-counting CT (PCD-CT), and artificial intelligence (AI)—offer promising enhancements to traditional diagnostic methods. This study reviews past, current and emerging technologies, focusing on their potential to optimize diagnostic accuracy, reduce contrast volumes and radiation doses, and streamline clinical workflows. DECT, with its dual-energy imaging capabilities, enhances image clarity even with lower contrast media volumes, thus reducing patient risk. Meanwhile, PCD-CT has shown potential for dose reduction and superior image resolution, particularly in challenging cases. AI-based tools further augment diagnostic speed and precision by assisting radiologists in image analysis, consequently decreasing workloads and expediting clinical decision-making. Collectively, these innovations hold promise for improved clinical management of PE, enabling not only more accurate diagnoses but also safer, more efficient patient care. Further research is necessary to fully integrate these advancements into routine clinical practice, potentially redefining diagnostic workflows for PE and enhancing patient outcomes.

Background: Small renal masses (SRMs) are defined as contrast-enhanced renal lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a renal cell carcinomas (RCCs). Currently, 50–61% of all renal tumors are found incidentally. Methods: The characteristics of the lesion influence the choice of the type of management, which include several methods SRM of management, including nephrectomy, partial nephrectomy, ablation, observation, and also stereotactic body radiotherapy. Typical imaging methods available for differentiating benign from malignant renal lesions include ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI). Results: Although ultrasound is the first imaging technique used to detect small renal lesions, it has several limitations. CT is the main and most widely used imaging technique for SRM characterization. The main advantages of MRI compared to CT are the better contrast resolution and tissue characterization, the use of functional imaging sequences, the possibility of performing the examination in patients allergic to iodine-containing contrast medium, and the absence of exposure to ionizing radiation. For a correct evaluation during imaging follow-up, it is necessary to use a reliable method for the assessment of renal lesions, represented by the Bosniak classification system. This classification was initially developed based on contrast-enhanced CT imaging findings, and the 2019 revision proposed the inclusion of MRI features; however, the latest classification has not yet received widespread validation. Conclusions: The use of radiomics in the evaluation of renal masses is an emerging and increasingly central field with several applications such as characterizing renal masses, distinguishing RCC subtypes, monitoring response to targeted therapeutic agents, and prognosis in a metastatic context.

This review is a brief overview of the current status and the potential role of artificial intelligence (AI) in interventional radiology (IR). The literature published in the last decades was reviewed and the technical developments in terms of radiomics, virtual reality, robotics, fusion imaging, cone-beam computed tomography (CBCT) and Imaging Guidance Software were analyzed. The evidence shows that AI significatively improves pre-procedural planning, intra-procedural navigation, and post-procedural assessment. Radiomics extracts features from optical images of personalized treatment strategies. Virtual reality offers innovative tools especially for training and procedural simulation. Robotic systems, combined with AI, could enhance precision and reproducibility of IR procedures while reducing operator exposure to X-ray. Fusion imaging and CBCT, augmented by AI software, improve real-time guidance and procedural outcomes.