This article explores the transformative potential of artificial intelligence (AI) in critical care ultrasound AI technologies, notably deep learning and convolutional neural networks, now assisting in image acquisition, interpretation, and quality assessment, streamlining workflow and reducing operator variability. By automating routine tasks, AI enhances diagnostic accuracy and bridges training gaps, potentially democratizing advanced ultrasound techniques. Furthermore, AI's integration into tele-ultrasound systems shows promise in extending expert-level diagnostics to underserved areas, significantly broadening access to quality care. The article highlights the ongoing need for explainable AI systems to gain clinician trust and facilitate broader adoption.

This bibliometric analysis aims to explore scientific data on Artificial Intelligence (AI) and Head and Neck Cancer (HNC). AI-related HNC articles from the Web of Science Core Collection were searched. VosViewer and Biblioshiny/Bibiometrix for R Studio were used for data synthesis. This analysis covered key characteristics such as sources, authors, affiliations, countries, citations and top cited articles, keyword analysis, and trending topics. A total of 1,019 papers from 1995 to 2024 were included. Among them, 71.6% were original research articles, 7.6% were reviews, and 20.8% took other forms. The fifty most cited documents highlighted radiology as the most explored specialty, with an emphasis on deep learning models for segmentation. The publications have been increasing, with an annual growth rate of 94.4% after 2016. Among the 20 most productive countries, 14 are high-income economies. The keywords of strong citation revealed 2 main clusters: radiomics and radiotherapy. The most frequently keywords include machine learning, deep learning, artificial intelligence, and head and neck cancer, with recent emphasis on diagnosis, survival prediction, and histopathology. There has been an increase in the use of AI in HNC research since 2016 and indicated a notable disparity in publication quantity between high-income and low/middle-income countries. Future research should prioritize clinical validation and standardization to facilitate the integration of AI in HNC management, particularly in underrepresented regions.

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths in the United States, largely due to its poor five-year survival rate and frequent late-stage diagnosis. A significant barrier to early detection even in high-risk cohorts is that the pancreas often appears morphologically normal during the pre-diagnostic phase. Yet, the disease can progress rapidly from subclinical stages to widespread metastasis, undermining the effectiveness of screening. Recently, artificial intelligence (AI) applied to cross-sectional imaging has shown significant potential in identifying subtle, early-stage changes in pancreatic tissue that are often imperceptible to the human eye. Moreover, AI-driven imaging also aids in the discovery of prognostic and predictive biomarkers, essential for personalized treatment planning. This article uniquely integrates a critical discussion on AI's role in detecting visually occult PDAC on pre-diagnostic imaging, addresses challenges of model generalizability, and emphasizes solutions like standardized datasets and clinical workflows. By focusing on both technical advancements and practical implementation, this article provides a forward-thinking conceptual framework that bridges current gaps in AI-driven PDAC research.

The aim of this commentary review was to summarize the main research evidences on radiation exposure and to underline the best clinical and radiological practices to limit radiation exposure in ICU patients. Radiological imaging is essential for management of patients in the ICU despite the risk of ionizing radiation exposure in monitoring critically ill patients, especially in those with prolonged hospitalization. In optimizing radiation exposure reduction for ICU patients, multiple parties and professionals must be considered, including hospital management, clinicians, radiographers, and radiologists. Modified diagnostic reference levels for ICU patients, based on UK guidance, may be proposed, especially considering the frequent repetition of x-ray diagnostic procedures in ICU patients. Best practices may reduce radiation exposure in ICU patients with particular emphasis on justification and radiation exposure optimization in conventional radiology, interventional radiology and fluoroscopy, CT, and nuclear medicine. CT contributes most predominately to radiation exposure in ICU patients. Low-dose (<1 mSv in effective dose) or even ultra-low-dose CT protocols, iterative reconstruction algorithms, and artificial intelligence-based innovative dose-reduction strategies could reduce radiation exposure and related oncogenic risks.

Given artificial intelligence's transformative effects, studying safety is important to ensure it is implemented in a beneficial way. Convolutional neural networks are used in radiology research for prediction but can be corrupted through adversarial attacks. This study investigates the effect of an adversarial attack, through poisoned data. To improve generalizability, we create a generic ResNet pneumonia classification model and then use it as an example by subjecting it to BadNets adversarial attacks. The study uses various poisoned datasets of different compositions (2%, 16.7% and 100% ratios of poisoned data) and two different test sets (a normal set of test data and one that contained poisoned images) to study the effects of BadNets. To provide a visual effect of the progressing corruption of the models, SHapley Additive exPlanations (SHAP) were used. As corruption progressed, interval analysis revealed that performance on a valid test set decreased while the model learned to predict better on a poisoned test set. SHAP visualization showed focus on the trigger. In the 16.7% poisoned model, SHAP focus did not fixate on the trigger in the normal test set. Minimal effects were seen in the 2% model. SHAP visualization showed decreasing performance was correlated with increasing focus on the trigger. Corruption could potentially be masked in the 16.7% model unless subjected specifically to poisoned data. A minimum threshold for corruption may exist. The study demonstrates insights that can be further studied in future work and with future models. It also identifies areas of potential intervention for safeguarding models against adversarial attacks.

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, with early detection playing a crucial role in improving survival rates. Artificial intelligence (AI), particularly in medical image analysis, has emerged as a potential tool for HCC diagnosis and surveillance. Recent advancements in deep learning-driven medical imaging have demonstrated significant potential in enhancing early HCC detection, particularly in ultrasound (US)-based surveillance. This review provides a comprehensive analysis of the current landscape, challenges, and future directions of AI in HCC surveillance, with a specific focus on the application in US imaging. Additionally, it explores AI's transformative potential in clinical practice and its implications for improving patient outcomes. We examine various AI models developed for HCC diagnosis, highlighting their strengths and limitations, with a particular emphasis on deep learning approaches. Among these, convolutional neural networks have shown notable success in detecting and characterising different focal liver lesions on B-mode US often outperforming conventional radiological assessments. Despite these advancements, several challenges hinder AI integration into clinical practice, including data heterogeneity, a lack of standardisation, concerns regarding model interpretability, regulatory constraints, and barriers to real-world clinical adoption. Addressing these issues necessitates the development of large, diverse, and high-quality data sets to enhance the robustness and generalisability of AI models. Emerging trends in AI for HCC surveillance, such as multimodal integration, explainable AI, and real-time diagnostics, offer promising advancements. These innovations have the potential to significantly improve the accuracy, efficiency, and clinical applicability of AI-driven HCC surveillance, ultimately contributing to enhanced patient outcomes.

In recent years, generative artificial intelligence (AI), particularly large language models (LLMs) and their multimodal counterparts, multimodal large language models, including vision language models, have generated considerable interest in the global AI discourse. LLMs, or pre-trained language models (such as ChatGPT, Med-PaLM, LLaMA), are neural network architectures trained on extensive text data, excelling in language comprehension and generation. Multimodal LLMs, a subset of foundation models, are trained on multimodal data sets, integrating text with another modality, such as images, to learn universal representations akin to human cognition better. This versatility enables them to excel in tasks like chatbots, translation, and creative writing while facilitating knowledge sharing through transfer learning, federated learning, and synthetic data creation. Several of these models can have potentially appealing applications in the medical domain, including, but not limited to, enhancing patient care by processing patient data; summarizing reports and relevant literature; providing diagnostic, treatment, and follow-up recommendations; and ancillary tasks like coding and billing. As radiologists enter this promising but uncharted territory, it is imperative for them to be familiar with the basic terminology and processes of LLMs. Herein, we present an overview of the LLMs and their potential applications and challenges in the imaging domain.

Personalized medicine aims to tailor medical treatments to the unique characteristics of each patient, but its effectiveness relies on achieving diagnostic accuracy to fully understand individual variability in disease response and treatment efficacy. This systematic literature review explores the role of large language models (LLMs) in enhancing diagnostic precision and supporting the advancement of personalized medicine. A comprehensive search was conducted across Web of Science, Science Direct, Scopus, and IEEE Xplore, targeting peer-reviewed articles published in English between January 2020 and March 2025 that applied LLMs within personalized medicine contexts. Following PRISMA guidelines, 39 relevant studies were selected and systematically analyzed. The findings indicate a growing integration of LLMs across key domains such as clinical informatics, medical imaging, patient-specific diagnosis, and clinical decision support. LLMs have shown potential in uncovering subtle data patterns critical for accurate diagnosis and personalized treatment planning. This review highlights the expanding role of LLMs in improving diagnostic accuracy in personalized medicine, offering insights into their performance, applications, and challenges, while also acknowledging limitations in generalizability due to variable model performance and dataset biases. The review highlights the importance of addressing challenges related to data privacy, model interpretability, and reliability across diverse clinical scenarios. For successful clinical integration, future research must focus on refining LLM technologies, ensuring ethical standards, and validating models continuously to safeguard effective and responsible use in healthcare environments.

Multimodal large language models (MLLMs) have enormous potential to perform few-shot in-context learning in the context of medical image analysis. However, safe deployment of these models into real-world clinical practice requires an in-depth analysis of the accuracies of their predictions, and their associated calibration errors, particularly across different demographic subgroups. In this work, we present the first investigation into the calibration biases and demographic unfairness of MLLMs' predictions and confidence scores in few-shot in-context learning for medical image classification. We introduce CALIN, an inference-time calibration method designed to mitigate the associated biases. Specifically, CALIN estimates the amount of calibration needed, represented by calibration matrices, using a bi-level procedure: progressing from the population level to the subgroup level prior to inference. It then applies this estimation to calibrate the predicted confidence scores during inference. Experimental results on three medical imaging datasets: PAPILA for fundus image classification, HAM10000 for skin cancer classification, and MIMIC-CXR for chest X-ray classification demonstrate CALIN's effectiveness at ensuring fair confidence calibration in its prediction, while improving its overall prediction accuracies and exhibiting minimum fairness-utility trade-off.

Multimodal large language models (MLLMs) have enormous potential to perform few-shot in-context learning in the context of medical image analysis. However, safe deployment of these models into real-world clinical practice requires an in-depth analysis of the accuracies of their predictions, and their associated calibration errors, particularly across different demographic subgroups. In this work, we present the first investigation into the calibration biases and demographic unfairness of MLLMs' predictions and confidence scores in few-shot in-context learning for medical image classification. We introduce CALIN, an inference-time calibration method designed to mitigate the associated biases. Specifically, CALIN estimates the amount of calibration needed, represented by calibration matrices, using a bi-level procedure: progressing from the population level to the subgroup level prior to inference. It then applies this estimation to calibrate the predicted confidence scores during inference. Experimental results on three medical imaging datasets: PAPILA for fundus image classification, HAM10000 for skin cancer classification, and MIMIC-CXR for chest X-ray classification demonstrate CALIN's effectiveness at ensuring fair confidence calibration in its prediction, while improving its overall prediction accuracies and exhibiting minimum fairness-utility trade-off.