Background: Artificial intelligence (AI) is reshaping neuroimaging workflows for Alzheimer’s disease (AD) diagnosis, particularly through PET and MRI analysis advances. Since the FDA approval of Tauvid, a PET tracer targeting tau pathology, there has been a notable increase in studies applying AI to neuroimaging data. This narrative review synthesizes recent, high-impact literature to highlight clinically relevant AI applications in AD imaging. Methods: This review examined peer-reviewed studies published between January 2020 and January 2025, focusing on the use of AI, including machine learning, deep learning, and hybrid models for diagnostic and prognostic tasks in AD using PET and/or MRI. Studies were identified through targeted PubMed, Scopus, and Embase searches, emphasizing methodological diversity and clinical relevance. Results: A total of 109 studies were categorized into five thematic areas: Image preprocessing and segmentation, diagnostic classification, prognosis and disease staging, multimodal data fusion, and emerging innovations. Deep learning models such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer-based architectures were widely employed by the research community in the field of AD. At the same time, several models reported strong diagnostic performance, but methodological challenges such as reproducibility, small sample sizes, and lack of external validation limit clinical translation. Trends in explainable AI, synthetic imaging, and integration of clinical biomarkers are also discussed. Conclusions: AI is rapidly advancing the field of AD imaging, offering tools for enhanced segmentation, staging, and early diagnosis. Multimodal approaches and biomarker-guided models show particular promise. However, future research must focus on reproducibility, interpretability, and standardized validation to bridge the gap between research and clinical practice.

Background: This narrative review summarizes recent progress in artificial intelligence (AI), especially radiomics and deep learning, for non-invasive diagnosis and molecular profiling of gliomas. Methodology: A thorough literature search was conducted on PubMed, Scopus, and Embase for studies published from January 2020 to July 2025, focusing on clinical and technical research. In key areas, these studies examine AI models’ predictive capabilities with multi-parametric Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Results: The domains identified in the literature include the advancement of radiomic models for tumor grading and biomarker prediction, such as Isocitrate Dehydrogenase (IDH) mutation, O6-methylguanine-dna methyltransferase (MGMT) promoter methylation, and 1p/19q codeletion. The growing use of convolutional neural networks (CNNs) and generative adversarial networks (GANs) in tumor segmentation, classification, and prognosis was also a significant topic discussed in the literature. Deep learning (DL) methods are evaluated against traditional radiomics regarding feature extraction, scalability, and robustness to imaging protocol differences across institutions. Conclusions: This review analyzes emerging efforts to combine clinical, imaging, and histology data within hybrid or transformer-based AI systems to enhance diagnostic accuracy. Significant findings include the application of DL to predict cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) deletion and chemokine CCL2 expression. These highlight the expanding capabilities of imaging-based genomic inference and the importance of clinical data in multimodal fusion. Challenges such as data harmonization, model interpretability, and external validation still need to be addressed.

Large language models (LLMs) rapidly transform healthcare by automating tasks, streamlining administration, and enhancing clinical decision support. This rapid review assesses current and emerging applications of LLMs in diagnostic-related group (DRG) assignment and clinical decision support systems (CDSS), with emphasis on radiology and nuclear medicine. Evidence shows that LLMs, particularly those tailored for medical domains, improve efficiency and accuracy in DRG coding and radiology report generation, providing clinicians with actionable, context-sensitive insights by integrating diverse data sources. Advances like retrieval-augmented generation and multimodal architecture further increase reliability and minimize incorrect or misleading results that AI models generate, a term that is known as hallucination. Despite these benefits, challenges remain regarding safety, explainability, bias, and regulatory compliance, necessitating ongoing validation and oversight. The review prioritizes recent, peer-reviewed literature on radiology and nuclear medicine to provide a practical synthesis for clinicians, administrators, and researchers. While LLMs show strong promise for enhancing DRG assignment and radiological decision-making, their integration into clinical workflows requires careful management. Ongoing technological advances and emerging evidence may quickly change the landscape, so findings should be interpreted in context. This review offers a timely overview of the evolving role of LLMs while recognizing the need for continuous re-evaluation.

Background: Mild Cognitive Impairment (MCI) represents an intermediate stage between normal cognitive aging and Alzheimer’s Disease (AD). Early and accurate identification of MCI is crucial for implementing interventions that may delay or prevent further cognitive decline. This study aims to develop a machine learning-based model for differentiating between Cognitively Normal (CN) individuals and MCI patients using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Methods: An ensemble classification approach was designed by integrating Extra Trees, Random Forest, and Light Gradient Boosting Machine (LightGBM) algorithms. Feature selection emphasized clinically relevant biomarkers, including Amyloid-β 42, phosphorylated tau, diastolic blood pressure, age, and gender. The dataset was split into training and held-out test sets. A probability thresholding strategy was employed to flag uncertain predictions for potential deferral, enhancing model reliability in borderline cases. Results: The final ensemble model achieved an accuracy of 83.2%, a recall of 80.2%, and a precision of 86.3% on the independent test set. The probability thresholding mechanism flagged 23.3% of cases as uncertain, allowing the system to abstain from low-confidence predictions. This strategy improved clinical interpretability and minimized the risk of misclassification in ambiguous cases. Conclusions: The proposed AI-driven ensemble model demonstrates strong performance in classifying MCI versus CN individuals using multimodal ADNI data. Incorporating a deferral mechanism through uncertainty estimation further enhances the model’s clinical utility. These findings support the integration of machine learning tools into early screening workflows for cognitive impairment.

Background: Alzheimer’s disease (AD) involves ongoing neurodegeneration, with phosphorylated tau (PTAU) intracellular accumulation closely associated with cortical shrinking. However, not everyone with high PTAU levels shows the same degree of neurodegeneration, implying that other biological stress factors might influence tau’s harmful effects. This research explores whether cerebrospinal fluid erythrocyte burden (CTRED), a marker indicating vascular–CSF barrier disruption and heme toxicity, affects the link between PTAU181 levels and entorhinal cortex atrophy in AD. Methods: We examined 25 observations from 18 patients with AD using a linear mixed effects model. The dependent variable was entorhinal cortex volume, with fixed effects for PTAU, CTRED, and their interaction. Random intercepts accounted for variability within subjects. A cognitively normal (CN) control group was included for comparison. Results: CTRED is significantly associated with reduced entorhinal volume (p = 0.005). A notable interaction between CTRED and PTAU was also found (p = 0.004), suggesting that higher CTRED enhances PTAU’s atrophic effects. PTAU alone was not a significant predictor. No significant effects were observed in the CN group, which supports the specificity of the disease. Conclusions: CTRED alters the neurotoxic impact of PTAU on the entorhinal cortex in AD, supporting a multi-hit model of degeneration that involves tau pathology and erythrocyte-derived stress. These findings emphasize the clinical importance of vascular–CSF biomarkers in predicting neurodegeneration and guiding targeted treatments.

Background/Objectives: Central nervous system (CNS) involvement in acute myeloid leukemia (AML) is a rare but important complication linked to poor outcomes. Diagnosing it is difficult because neurological symptoms are often subtle or nonspecific, and conventional cytology and imaging have limitations. This review summarizes current evidence on the neuroradiologic features of CNS infiltration in AML and explores the growing role of artificial intelligence (AI) in enhancing detection and characterization. Methods: A thorough narrative review was conducted using PubMed, Scopus, and Embase, employing key terms related to AML, CNS involvement, MRI, CT, PET, AI, machine learning, deep learning, and radiomics. Of several thousand records, 138 relevant studies were selected and analyzed across four main areas: neuroradiologic patterns, imaging biomarkers, AI and radiomics applications, and emerging computational trends. Results: Imaging findings in AML mainly include myeloid sarcomas (isointense on T1, hyperintense on T2/FLAIR, restricted diffusion) and leptomeningeal enhancement. Secondary ischemic or hemorrhagic lesions may indicate brain leukocytosis. MRI proved more sensitive than CT, while PET/CT helped detect extramedullary disease. Recent AI and radiomics models showed high tumor classification and prognosis accuracy in similar CNS conditions, indicating significant potential for application in AML-CNS. Conclusions: Combining AI-based image analysis with multimodal neuroimaging could significantly improve diagnostic accuracy and personalized treatment for CNS involvement in AML. Progress is still challenged by the rarity of the condition and the lack of large, annotated datasets.