The integration of artificial intelligence (AI) into medical image analysis has transformed healthcare, offering unprecedented precision in diagnosis, treatment planning, and disease monitoring. However, its adoption within the Internet of Medical Things (IoMT) raises significant challenges related to transparency, trustworthiness, and security. This paper introduces a novel Explainable AI (XAI) framework tailored for Medical Cyber-Physical Systems (MCPS), addressing these challenges by combining deep neural networks with symbolic knowledge reasoning to deliver clinically interpretable insights. The framework incorporates an Enhanced Dynamic Confidence-Weighted Attention (Enhanced DCWA) mechanism, which improves interpretability and robustness by dynamically refining attention maps through adaptive normalization and multi-level confidence weighting. Additionally, a Resilient Observability and Detection Engine (RODE) leverages sparse observability principles to detect and mitigate adversarial threats, ensuring reliable performance in dynamic IoMT environments. Evaluations conducted on benchmark datasets, including CheXpert, RSNA Pneumonia Detection Challenge, and NIH Chest X-ray Dataset, demonstrate significant advancements in classification accuracy, adversarial robustness, and explainability. The framework achieves a 15% increase in lesion classification accuracy, a 30% reduction in robustness loss, and a 20% improvement in the Explainability Index compared to state-of-the-art methods.

The rapid advancement of artificial intelligence (AI) in healthcare has accelerated innovations in medical algorithms, yet its broader adoption faces critical ethical and technical barriers. A key challenge lies in algorithmic bias stemming from heterogeneous medical data across institutions, equipment, and workflows, which may perpetuate disparities in AI-driven diagnoses and exacerbate inequities in patient care. While AI's ability to extract deep features from large-scale data offers transformative potential, its effectiveness heavily depends on standardized, high-quality datasets. Current standardization gaps not only limit model generalizability but also raise concerns about reliability and fairness in real-world clinical settings, particularly for marginalized populations. Addressing these urgent issues, this paper proposes an ethical AI framework centered on a novel self-supervised medical image standardization method. By integrating self-supervised image style conversion, channel attention mechanisms, and contrastive learning-based loss functions, our approach enhances structural and style consistency in diverse datasets while preserving patient privacy through decentralized learning paradigms. Experiments across multi-institutional medical image datasets demonstrate that our method significantly improves AI generalizability without requiring centralized data sharing. By bridging the data standardization gap, this work advances technical foundations for trustworthy AI in healthcare.

Clinical evaluation of Artificial Intelligence (AI)-based Precise Image (PI) algorithm in brain imaging remains limited. PI is a deep-learning reconstruction (DLR) technique that reduces image noise while maintaining a familiar Filtered Back Projection (FBP)-like appearance at low doses. This study aims to compare PI, Iterative Reconstruction (IR), and FBP-in improving image quality and enhancing lesion detection in 1.0 mm thin-slice brain computed tomography (CT) images. A retrospective analysis was conducted on brain non-contrast CT scans from August to September 2024 at our institution. Each scan was reconstructed using four methods: routine 5.0 mm FBP (Group A), thin-slice 1.0 mm FBP (Group B), thin-slice 1.0 mm IR (Group C), and thin-slice 1.0 mm PI (Group D). Subjective image quality was assessed by two radiologists using a 4- or 5‑point Likert scale. Objective metrics included contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and image noise across designated regions of interest (ROIs). 60 patients (65.47 years ± 18.40; 29 males and 31 females) were included. Among these, 39 patients had lesions, primarily low-density lacunar infarcts. Thin-slice PI images demonstrated the lowest image noise and artifacts, alongside the highest CNR and SNR values (p < 0.001) compared to Groups A, B, and C. Subjective assessments revealed that both PI and IR provided significantly improved image quality over routine FBP (p < 0.05). Specifically, Group D (PI) achieved superior lesion conspicuity and diagnostic confidence, with a 100% detection rate for lacunar lesions, outperforming Groups B and A. PI reconstruction significantly enhances image quality and lesion detectability in thin-slice brain CT scans compared to IR and FBP, suggesting its potential as a new clinical standard.

This article, on the 60th anniversary of the journal Investigative Radiology, a journal dedicated to cutting-edge imaging technology, discusses key historical milestones in CT and MRI technology, as well as the ongoing advancement of contrast agent development for cardiovascular imaging over the past decades. It specifically highlights recent developments and the current state-of-the-art technology, including photon-counting detector CT and artificial intelligence, which will further push the boundaries of cardiovascular imaging. What were once ideas and visions have become today's clinical reality for the benefit of patients, and imaging technology will continue to evolve and transform modern medicine.

Early detection and precise preoperative staging of early gastric cancer (EGC) are critical. Therefore, this study aims to develop a deep learning model using portal venous phase CT images to accurately distinguish EGC without lymph node metastasis. This study included 3164 patients with gastric cancer (GC) who underwent radical surgery at two medical centers in China from 2006 to 2019. Moreover, 2.5D radiomic data and multi-instance learning (MIL) were novel approaches applied in this study. By basing the selection of features on 2.5D radiomic data and MIL, the ResNet101 model combined with the XGBoost model represented a satisfactory performance for diagnosing pT1N0 GC. Furthermore, the 2.5D MIL-based model demonstrated a markedly superior predictive performance compared to traditional radiomics models and clinical models. We first constructed a deep learning prediction model based on 2.5D radiomics and MIL for effectively diagnosing pT1N0 GC patients, which provides valuable information for the individualized treatment selection.

This review paper explores the integration of ChatGPT, a generative AI model developed by OpenAI, into radiological practices, focusing on its potential to enhance the operational efficiency of radiologists. ChatGPT operates on the GPT architecture, utilizing advanced machine learning techniques, including unsupervised pre-training and reinforcement learning, to generate human-like text responses. While AI applications in radiology predominantly focus on imaging acquisition, reconstruction, and interpretation-commonly embedded directly within hardware-the accessibility and functional breadth of ChatGPT make it a unique tool. This interview-based review should not be intended as a detailed evaluation of all ChatGPT features. Instead, it aims to test its utility in everyday radiological tasks through real-world examples. ChatGPT demonstrated strong capabilities in structuring radiology reports according to international guidelines (e.g., PI-RADS, CT reporting for diverticulitis), designing a complete research protocol, and performing advanced statistical analysis from Excel datasets, including ROC curve generation and intergroup comparison. Although not capable of directly interpreting DICOM images, ChatGPT provided meaningful assistance in image post-processing and interpretation when images were converted to standard formats. These findings highlight its current strengths and limitations as a supportive tool for radiologists.

Recent advancements in four-dimensional (4D) ultrasonography have enabled detailed observation of fetal behavior <i>in utero</i>, including facial movements, limb gestures, and stimulus responses. These developments have prompted renewed inquiry into whether such behaviors are merely reflexive or represent early signs of integrated neural function. However, the relationship between fetal movement patterns and conscious awareness remains scientifically uncertain and ethically contested. A systematic review was conducted in accordance with PRISMA 2020 guidelines. Four databases (PubMed, Scopus, Embase, Web of Science) were searched for English-language articles published from 2000 to 2025, using keywords including "fetal behavior," "4D ultrasound," "neurodevelopment," and "consciousness." Studies were included if they involved human fetuses, used 4D ultrasound or functional imaging modalities, and offered interpretation relevant to neurobehavioral or ethical analysis. A structured appraisal using AMSTAR-2 was applied to assess study quality. Data were synthesized narratively to map fetal behaviors onto developmental milestones and evaluate their interpretive limits. Seventy-four studies met inclusion criteria, with 23 rated as high-quality. Fetal behaviors such as yawning, hand-to-face movement, and startle responses increased in complexity between 24-34 weeks gestation. These patterns aligned with known neurodevelopmental events, including thalamocortical connectivity and cortical folding. However, no study provided definitive evidence linking observed behaviors to conscious experience. Emerging applications of artificial intelligence in ultrasound analysis were found to enhance pattern recognition but lack external validation. Fetal behavior observed via 4D ultrasound may reflect increasing neural integration but should not be equated with awareness. Interpretations must remain cautious, avoiding anthropomorphic assumptions. Ethical engagement requires attention to scientific limits, sociocultural diversity, and respect for maternal autonomy as imaging technologies continue to evolve.

<b>BACKGROUND</b>. Coronary artery calcium (CAC) scoring is used to stratify acute coronary syndrome (ACS) risk. Nonetheless, patients with a CAC score of zero (CAC₀) remain at risk from noncalcified plaque components. <b>OBJECTIVE</b>. The purpose of this study was to explore CTA-derived coronary artery plaque characteristics in symptomatic patients with CAC₀ who subsequently have ACS through comparisons with patients with a CAC score greater than 0 (CAC_{> 0}) who subsequently have ACS as well as with patients with CAC₀ who do not subsequently have ACS. <b>METHODS</b>. This study entailed a secondary retrospective analysis of prior prospective registry data. The international multicenter CONFIRM (Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter) registry collected longitudinal observational data on symptomatic patients who underwent clinically indicated coronary CTA from January 2004 to May 2010. ICONIC (Incident Coronary Syndromes Identified by CT) was a nested cohort study conducted within CONFIRM that identified patients without known coronary artery disease (CAD) at the time of CTA who did and did not subsequently have ACS (i.e., the ACS and control groups, respectively) and who were propensity matched in a 1:1 ratio on the basis of CAD risk factors and CAD severity on CTA. The present ICONIC substudy selected matched patients in the ACS and control groups who both had documented CAC scores. CTA examinations were analyzed using artificial intelligence software for automated quantitative plaque assessment. In the ACS group, invasive angiography findings were used to identify culprit lesions. <b>RESULTS</b>. The present study included 216 patients (mean age, 55.6 years; 91 women and 125 men), with 108 patients in each of the ACS and control groups. In the ACS group, 23% (<i>n</i> = 25) of patients had CAC₀. In the ACS group, culprit lesions in the subsets of patients with CAC₀ and CAC_{> 0} showed no significant differences in fibrous, fibrofatty, or necrotic-core plaque volumes (<i>p</i> > .05). In the CAC₀ subset, patients with ACS, compared with control patients, had greater mean (± SD) fibrous plaque volume (29.4 ± 42.0 vs 5.5 ± 15.2 mm³, <i>p</i> < .001), fibrofatty plaque volume (27.3 ± 52.2 vs 1.3 ± 3.7 mm³, <i>p</i> < .001), and necrotic-core plaque volume (2.8 ± 6.4 vs 0.0 ± 0.1 mm³, <i>p</i> < .001). <b>CONCLUSION</b>. After propensity-score matching, 23% of patients with ACS had CAC₀. Patients with CAC₀ in the ACS and control groups showed significant differences in volumes of noncalcified plaque components. <b>CLINICAL IMPACT</b>. Methods that identify and quantify noncalcified plaque forms may help characterize ACS risk in symptomatic patients with CAC₀.

To develop and assess the performance of an anatomically based multitask deep learning radiomics nomogram (AMDRN) system to predict implant failure risk before maxillary sinus floor elevation (MSFE) while incorporating automated segmentation of key anatomical structures. We retrospectively collected patients' preoperative cone beam computed tomography (CBCT) images and electronic medical records (EMRs). First, the nn-UNet v2 model was optimized to segment the maxillary sinus (MS), Schneiderian membrane (SM), and residual alveolar bone (RAB). Based on the segmentation mask, a deep learning model (3D-Attention-ResNet) and a radiomics model were developed to extract 3D features from CBCT scans, generating the DL Score, and Rad Score. Significant clinical features were also extracted from EMRs to build a clinical model. These components were then integrated using logistic regression (LR) to create the AMDRN model, which includes a visualization module to support clinical decision-making. Segmentation results for MS, RAB, and SM achieved high DICE coefficients on the test set, with values of 99.50% ± 0.84%, 92.53% ± 3.78%, and 91.58% ± 7.16%, respectively. On an independent test set, the Clinical model, Radiomics model, 3D-DL model, and AMDRN model achieved prediction accuracies of 60%, 76%, 82%, and 90%, respectively, with AMDRN achieving the highest AUC of 93%. The AMDRN system enables efficient preoperative prediction of implant failure risk in MSFE and accurate segmentation of critical anatomical structures, supporting personalized treatment planning and clinical risk management.

The study investigates the correlation between CD3 T-cell expression levels and cervical cancer (CC) while developing a magnetic resonance (MR) imaging-based radiomics model for preoperative prediction of CD3 T-cell expression levels. Prognostic correlations between CD3D, CD3E, and CD3G gene expressions and various cancers were analyzed using the Cancer Genome Atlas (TCGA) database. Protein-protein interaction (PPI) analysis via the STRING database identified associations between these genes and T lymphocyte activity. Gene Set Enrichment Analysis (GSEA) revealed immune pathway enrichment by categorizing genes based on CD3D expression levels. Correlations between immune checkpoint molecules and CD3 complex genes were also assessed. The study retrospectively included 202 patients with pathologically confirmed early-stage CC who underwent preoperative MRI, divided into training and test groups. Radiomic features were extracted from the whole-lesion tumor region of interest (ROI_tumor) and from peritumoral regions with 3 mm and 5 mm margins (ROI_3mm and ROI_5mm, respectively). Various machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression, Random Forest, AdaBoost, and Decision Tree, were used to construct radiomics models based on different ROIs, and diagnostic performances were compared to identify the optimal approach. The best-performing algorithm was combined with intra- and peritumoral features and clinically relevant independent risk factors to develop a comprehensive predictive model. Analysis of the TCGA database demonstrated significant associations between CD3D, CD3E, and CD3G expressions and several cancers, including CC (p < 0.05). PPI analysis highlighted connections between these genes and T lymphocyte function, while GSEA indicated enrichment of immune-related pathways linked to CD3D. Immune checkpoint correlations showed positive associations with CD3 complex genes. Radiomics analysis selected 18 features from ROI_tumor and ROI_3mm across MRI sequences. The SVM algorithm achieved the highest predictive performance for CD3 T-cell expression status, with an area under the curve (AUC) of 0.93 in the training group and 0.92 in the test group. This MR-based radiomics model effectively predicts CD3 expression status in patients with early-stage CC, offering a non-invasive tool for preoperative assessment of CD3 expression, but its clinical utility needs further prospective validation.