We aimed to review the role of functional imaging in cervical cancer to underscore its significance in the diagnosis and management of cervical cancer and in improving patient outcomes. This rapid literature review targeting the clinical guidelines for functional imaging in cervical cancer sourced literature from 2017 to 2023 using PubMed, Google Scholar, MEDLINE and Scopus. Keywords such as cervical cancer, cervical neoplasms, functional imaging, stag*, treatment response, monitor* and New Zealand or NZ were used with Boolean operators to maximise results. Emphasis was on English full research studies pertinent to New Zealand. The study quality of the reviewed articles was assessed using the Joanna Briggs Institute critical appraisal checklists. The search yielded a total of 21 papers after all duplicates and yields that did not meet the inclusion criteria were excluded. Only one paper was found to incorporate the New Zealand context. The papers reviewed yielded results that demonstrate the important role of functional imaging in cervical cancer diagnosis, staging and treatment response monitoring. Techniques such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), diffusion-weighted magnetic resonance imaging (DW-MRI), computed tomography perfusion (CTP) and positron emission tomography computed tomography (PET/CT) provide deep insights into tumour behaviour, facilitating personalised care. Integration of artificial intelligence in image analysis promises increased accuracy of these modalities. Functional imaging could play a significant role in a unified approach in New Zealand to improve patient outcomes for cervical cancer management. Therefore, this study advocates for New Zealand's medical sector to harness functional imaging's potential in cervical cancer management.

Radiomics research has been hindered by inconsistent and often poor methodological quality, limiting its potential for clinical translation. To address this challenge, the METhodological RadiomICs Score (METRICS) was recently introduced as a tool for systematically assessing study rigor. However, its effective application requires clearer guidance. The METRICS-E3 (Explanation and Elaboration with Examples) resource was developed by the European Society of Medical Imaging Informatics-Radiomics Auditing Group in response. This international initiative provides comprehensive support for users by offering detailed rationales, interpretive guidance, scoring recommendations, and illustrative examples for each METRICS item and condition. Each criterion includes positive examples from peer-reviewed, open-access studies and hypothetical negative examples. In total, the finalized METRICS-E3 includes over 200 examples. The complete resource is publicly available through an interactive website. CRITICAL RELEVANCE STATEMENT: METRICS-E3 offers deeper insights into each METRICS item and condition, providing concrete examples with accompanying commentary and recommendations to enhance the evaluation of methodological quality in radiomics research. KEY POINTS: As a complementary initiative to METRICS, METRICS-E3 is intended to support stakeholders in evaluating the methodological aspects of radiomics studies. In METRICS-E3, each METRICS item and condition is supplemented with interpretive guidance, positive literature-based examples, hypothetical negative examples, and scoring recommendations. The complete METRICS-E3 explanation and elaboration resource is accessible at its interactive website.

Prostate MRI has traditionally relied on qualitative interpretation. However, quantitative components hold the potential to markedly improve performance. The ADC from DWI is probably the most widely recognized quantitative MRI biomarker and has shown strong discriminatory value for clinically significant prostate cancer as well as for recurrent cancer after treatment. Advanced diffusion techniques, including intravoxel incoherent motion imaging, diffusion kurtosis imaging, diffusion-tensor imaging, and specific implementations such as restriction spectrum imaging, purport even better discrimination but are more technically challenging. The inherent T1 and T2 of tissue also provide diagnostic value, with more advanced techniques deriving luminal water fraction and hybrid multidimensional MRI metrics. Dynamic contrast-enhanced imaging, primarily using a modified Tofts model, also shows independent discriminatory value. Finally, quantitative lesion size and shape features can be combined with the aforementioned techniques and can be further refined using radiomics, texture analysis, and artificial intelligence. Which technique will ultimately find widespread clinical use will depend on validation across a myriad of platforms and use cases.

This clinically oriented review explores the technical advancements of simultaneous PET/magnetic resonance (MR) imaging to provide an overview of the addressed obstacles over time, current challenges, and future trends in the field. In particular, advanced attenuation and motion correction techniques and MR-guided PET reconstruction frameworks were reviewed, and the state-of-the-art PET/MR systems and their strengths were discussed. Overall, PET/MR holds great potential in various clinical applications, including oncology, neurology, and cardiology. However, it requires continued optimization in hardware, algorithms, and clinical protocols to achieve broader adoption and be included in the routine clinical standards.

Nanotheranostics has garnered significant interest for its capacity to improve customized healthcare via targeted and efficient treatment alternatives. Nanotheranostics promises an innovative approach to precision medicine by integrating therapeutic and diagnostic capabilities into nanoscale devices. Nanotheranostics provides an integrated approach that improves diagnosis and facilitates real-time, tailored treatment, revolutionizing patient care. Through the application of nanotheranostic devices, outcomes can be modified for patients on an individualized therapeutic level by taking into consideration individual differences in disease manifestation as well as treatment response. In this review, no aspect of imaging in nanotheranostics is excluded, thus including MRI and CT as well as PET and OI, which are essential for comprehensive analysis needed in medical decision making. Integration of AI and ML into theranostics facilitates predicting treatment outcomes and personalizing the approaches to the methods, which significantly enhances reproducibility in medicine. In addition, several nanoparticles such as lipid-based and polymeric particles, iron oxide, quantum dots, and mesoporous silica have shown promise in diagnosis and targeted drug delivery. These nanoparticles are capable of treating multiple diseases such as cancers, some other neurological disorders, and infectious diseases. While having potential, the field of nanotheranostics still encounters issues regarding clinical applicability, alongside some regulatory hurdles pertaining to new therapeutic agents. Advanced research in this sphere is bound to enhance existing perspectives and fundamentally aid the integration of nanomedicine into conventional health procedures, especially relating to efficacy and the growing emphasis on safe, personalized healthcare.

Alzheimer's disease (AD) is the most common form of dementia, and it is important to diagnose the disease at an early stage to help people with the condition and their families. Recently, artificial intelligence, especially deep learning approaches applied to medical imaging, has shown potential in enhancing AD diagnosis. This comprehensive review investigates the current state of the art in multimodal deep learning for the early diagnosis of Alzheimer's disease using image processing. The research underpinning this review spanned several months. Numerous deep learning architectures are examined, including CNNs, transfer learning methods, and combined models that use different imaging modalities, such as structural MRI, functional MRI, and amyloid PET. The latest work on explainable AI (XAI) is also reviewed to improve the understandability of the models and identify the particular regions of the brain related to AD pathology. The results indicate that multimodal approaches generally outperform single-modality methods, and three-dimensional (volumetric) data provides a better form of representation compared to two-dimensional images. Current challenges are also discussed, including insufficient and/or poorly prepared datasets, computational expense, and the lack of integration with clinical practice. The findings highlight the potential of applying deep learning approaches for early AD diagnosis and for directing future research pathways. The integration of multimodal imaging with deep learning techniques presents an exciting direction for developing improved AD diagnostic tools. However, significant challenges remain in achieving accurate, reliable, and understandable clinical applications.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide, necessitating accurate and early diagnosis to guide therapy, along with assessment of treatment response. Response assessment criteria have evolved from traditional morphologic approaches, such as WHO criteria and Response Evaluation Criteria in Solid Tumors (RECIST), to more recent methods focused on evaluating viable tumor burden, including European Association for Study of Liver (EASL) criteria, modified RECIST (mRECIST) and Liver Imaging Reporting and Data System (LI-RADS) Treatment Response (LI-TR) algorithm. This shift reflects the complex and evolving landscape of HCC treatment in the context of emerging systemic and locoregional therapies. Each of these criteria have their own nuanced strengths and limitations in capturing the detailed characteristics of HCC treatment and response assessment. The emergence of functional imaging techniques, including dual-energy CT, perfusion imaging, and rising use of radiomics, are enhancing the capabilities of response assessment. Growth in the realm of artificial intelligence and machine learning models provides an opportunity to refine the precision of response assessment by facilitating analysis of complex imaging data patterns. This review article provides a comprehensive overview of existing criteria, discusses functional and emerging imaging techniques, and outlines future directions for advancing HCC tumor response assessment.

In recent years, Artificial Intelligence (AI) has gained prominence as a robust tool for clinical decision-making and diagnostics, owing to its capacity to process and analyze large datasets with high accuracy. More specifically, Deep Learning, and its subclasses, have shown significant potential in image processing, including medical imaging and histological analysis. In forensic pathology, AI has been employed for the interpretation of histopathological data, identifying conditions such as myocardial infarction, traumatic injuries, and heart rhythm abnormalities. This review aims to highlight key advances in AI's role, particularly machine learning (ML) and deep learning (DL) techniques, in forensic neuropathology, with a focus on its ability to interpret instrumental and histopathological data to support professional diagnostics. A systematic review of the literature regarding applications of Artificial Intelligence in forensic neuropathology was carried out according to the Preferred Reporting Item for Systematic Review (PRISMA) standards. We selected 34 articles regarding the main applications of AI in this field, dividing them into two categories: those addressing traumatic brain injury (TBI), including intracranial hemorrhage or cerebral microbleeds, and those focusing on epilepsy and SUDEP, including brain disorders and central nervous system neoplasms capable of inducing seizure activity. In both cases, the application of AI techniques demonstrated promising results in the forensic investigation of cerebral pathology, providing a valuable computer-assisted diagnostic tool to aid in post-mortem computed tomography (PMCT) assessments of cause of death and histopathological analyses. In conclusion, this paper presents a comprehensive overview of the key neuropathology areas where the application of artificial intelligence can be valuable in investigating causes of death.

Brugada Syndrome (BrS) is an inherited cardiac ion channelopathy associated with an elevated risk of sudden cardiac death, particularly due to ventricular arrhythmias in structurally normal hearts. Affecting approximately 1 in 2,000 individuals, BrS is most prevalent among middle-aged males of Asian descent. Although diagnosis is based on the presence of a Type 1 electrocardiographic (ECG) pattern, either spontaneous or induced, accurately stratifying risk in asymptomatic and borderline patients remains a major clinical challenge. This review explores current and emerging approaches to BrS risk stratification, focusing on electrocardiographic, electrophysiological, imaging, and computational markers. Non-invasive ECG indicators such as the β-angle, fragmented QRS, S wave in lead I, early repolarisation, aVR sign, and transmural dispersion of repolarisation have demonstrated predictive value for arrhythmic events. Adjunctive tools like signal-averaged ECG, Holter monitoring, and exercise stress testing enhance diagnostic yield by capturing dynamic electrophysiological changes. In parallel, imaging modalities, particularly speckle-tracking echocardiography and cardiac magnetic resonance have revealed subclinical structural abnormalities in the right ventricular outflow tract and atria, challenging the paradigm of BrS as a purely electrical disorder. Invasive electrophysiological studies and substrate mapping have further clarified the anatomical basis of arrhythmogenesis, while risk scoring systems (e.g., Sieira, BRUGADA-RISK, PAT) and machine learning models offer new avenues for personalised risk assessment. Together, these advances underscore the importance of an integrated, multimodal approach to BrS risk stratification. Optimising these strategies is essential to guide implantable cardioverter-defibrillator decisions and improve outcomes in patients vulnerable to life-threatening arrhythmias.

Foundation models are large models trained on big data which can be used for downstream tasks. In radiology, these models can potentially address several gaps in fairness and generalization, as they can be trained on massive datasets without labelled data and adapted to tasks requiring data with a small number of descriptions. This reduces one of the limiting bottlenecks in clinical model construction-data annotation-as these models can be trained through a variety of techniques that require little more than radiological images with or without their corresponding radiological reports. However, foundation models may be insufficient as they are affected-to a smaller extent when compared with traditional supervised learning approaches-by the same issues that lead to underperforming models, such as a lack of transparency/explainability, and biases. To address these issues, we advocate that the development of foundation models should not only be pursued but also accompanied by the development of a decentralized clinical validation and continuous training framework. This does not guarantee the resolution of the problems associated with foundation models, but it enables developers, clinicians and patients to know when, how and why models should be updated, creating a clinical AI ecosystem that is better capable of serving all stakeholders. CRITICAL RELEVANCE STATEMENT: Foundation models may mitigate issues like bias and poor generalization in radiology AI, but challenges persist. We propose a decentralized, cross-institutional framework for continuous validation and training to enhance model reliability, safety, and clinical utility. KEY POINTS: Foundation models trained on large datasets reduce annotation burdens and improve fairness and generalization in radiology. Despite improvements, they still face challenges like limited transparency, explainability, and residual biases. A decentralized, cross-institutional framework for clinical validation and continuous training can strengthen reliability and inclusivity in clinical AI.