Background/Objectives: To evaluate the diagnostic accuracy of artificial intelligence (AI)-based imaging techniques for liver fibrosis and metabolic dysfunction-associated steatotic liver disease (MASLD). Materials and Methods: We performed a comprehensive search in PubMed, Embase, Cochrane Library, and Web of Science until August 2025. A total of 15 studies (mean age of patients 56 years, 60% male) were included. The risk of bias in the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Diagnostic performance metrics were calculated using a random-effects bivariate model, including the area under the curve (AUC), sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio. Meta-regression analysis was conducted to investigate potential sources of heterogeneity when I² was ≥50%. A p-value < 0.05 was considered statistically significant. Results: For liver fibrosis, pooled sensitivity was 0.85, specificity was 0.81, and AUC was 0.92. For MASLD, sensitivity was 0.86, specificity was 0.95, and AUC was 0.99. Different imaging modalities and AI classifiers caused significant study heterogeneity. To avoid misleading pooled estimates across varied datasets, imaging modality and AI model subgroup analyses were performed. Only three studies were used to estimate MASLD; therefore, considerable between-study heterogeneity should be considered. Conclusions: AI-based imaging modalities demonstrate promising diagnostic accuracy for liver fibrosis and MASLD, warranting further standardization to enhance diagnostic consistency.

Artificial intelligence (AI)-based imaging modalities are next-generation diagnostic devices for abdominal infections that promise to provide enhanced diagnostic speed and accuracy. This systematic review and meta-analysis critically analyze the diagnostic accuracy of AI-assisted imaging modalities, including for appendicitis, pneumoperitoneum, and cholecystitis, to present a balanced estimate of their clinical utility. A systematic literature search was undertaken in PubMed, Scopus, and Cochrane databases to search for studies that assessed the diagnostic accuracy of AI-based imaging modalities for abdominal infections. Eleven studies were included based on the inclusion criteria, and data were pooled for analysis. Diagnostic performance was measured by estimating sensitivity, specificity, likelihood ratios, diagnostic odds ratio (DOR), and area under the curve (AUC) using a random-effects model. Subgroup analyses were done to investigate the effect of infection type on diagnostic accuracy. AI-assisted imaging demonstrated an overall sensitivity of 0.891 (95% CI: 0.824-0.944) and specificity of 0.860 (95% CI: 0.784-0.922) for diagnosing abdominal infections. Subgroup analysis revealed that AI-aided computed tomography (CT) exhibited a sensitivity of 0.902 (95% CI: 0.850-0.948), while ultrasound (US) showed a sensitivity of 0.864 (95% CI: 0.792-0.922). The highest AUCs were observed for pneumoperitoneum (0.985) and appendicitis (0.947), underscoring AI's robust diagnostic capabilities across multiple pathologies. AI-imaging modalities significantly enhance diagnostic accuracy for abdominal infections, particularly for appendicitis and pneumoperitoneum. This meta-analysis underscores AI's clinical potential, though future research should prioritize multicenter studies to validate AI models' generalizability and ensure consistent performance across diverse healthcare settings.

Diffusion-weighted imaging (DWI) is a functional MRI technique reflecting tissue cellularity and water diffusivity and shows promise for evaluating malignant from benign ovarian masses. However, reported diagnostic performance remains inconsistent. This systematic review and meta-analysis aimed to determine the pooled diagnostic accuracy of DWI for ovarian cancer. A comprehensive literature search was conducted in PubMed, Embase, Scopus, Web of Science, Cochrane Library, CINAHL, and Google Scholar from inception to October 2025. Studies reporting sensitivity and specificity of DWI for ovarian cancer were included. Two reviewers independently performed data extraction and quality assessment using the QUADAS-2 tool. Pooled sensitivity, specificity, positive and negative likelihood ratios (PLR, NLR), diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic curve (AUC) were calculated using a random-effects model. Heterogeneity was assessed with the I² statistic, and subgroup and meta-regression analyses explored potential sources. Seventeen studies comprising 1486 patients were included. Pooled sensitivity and specificity were 0.89 (95% CI: 0.86-0.91) and 0.83 (95% CI: 0.80-0.85), respectively. The pooled PLR was 5.38 (95% CI: 3.72-7.77), NLR 0.12 (95% CI: 0.07-0.20), and DOR 50.85 (95% CI: 27.33-94.61). The AUC was 0.93, indicating excellent diagnostic performance. Substantial heterogeneity was observed (I² > 75%) and was not explained by study design, MRI field strength, or ADC thresholds. DWI demonstrates high pooled sensitivity and specificity for differentiating benign from malignant ovarian lesions with substantial heterogeneity across studies. Therefore, DWI should be considered a promising non-invasive, not standalone diagnostic tool.

Prostate cancer represents a highly prevalent malignancy affecting men globally, necessitating precise staging and risk stratification for effective patient management. Multiparametric magnetic resonance imaging (mpMRI) and prostate-specific membrane antigen positron emission tomography (PSMA PET) have individually revolutionized the diagnosis and management of prostate cancer. Recent developments emphasize the integration of these imaging modalities to improve detection capabilities, inform therapeutic interventions, and facilitate personalized management. This narrative article reviews existing literature on the clinical utilization of mpMRI and PSMA PET in prostate cancer. Key areas encompass initial diagnosis, both local and systemic staging, detection of biochemical recurrence, and their influence in treatment strategies. The integration of mpMRI and PSMA PET offers complementary insights, with mpMRI demonstrating superior capability in local tumor characterization and PSMA PET enhancing the detection of nodal and distant metastases. Quantitative imaging biomarkers, including apparent diffusion coefficient (ADC) and standardized uptake values (SUV), have the potential to improve risk stratification and inform personalized treatment strategies. Hybrid imaging techniques may improve diagnostic accuracy and guide decisions regarding surgery, radiotherapy, and systemic treatment. The integration of mpMRI and PSMA PET allows a potentially transformative advancement in the realm of precision imaging for prostate cancer. This integrated approach can improve diagnostic accuracy, better define disease extent, and support personalized management strategies.