Generative AI tools have proliferated across the market, garnered significant media attention, and increasingly found incorporation into the radiology practice setting. However, they raise a number of unanswered questions concerning governance and appropriate use. By their nature as general-purpose technologies, they strain the limits of existing FDA premarket review pathways to regulate them and introduce new sources of liability, privacy, and clinical risk. A multilayered governance approach is needed to balance innovation with safety. To address gaps in oversight, this piece establishes a trilaminar governance model for generative AI technologies. This treats federal regulations as a scaffold, upon which tiers of institutional guidelines and industry self-regulatory frameworks are added to create a comprehensive paradigm composed of interlocking parts. Doing so would provide radiologists with an effective risk management strategy for the future, foster continued technical development, and ultimately, promote patient care.

Recent advances in Artificial Intelligence (AI) methodologies and their application to medical imaging has led to an explosion of related research programs utilizing AI to produce state-of-the-art classification performance. Ideally, research culminates in dissemination of the findings in peer-reviewed journals. To date, acceptance or rejection criteria are often subjective; however, reproducible science requires reproducible review. The Machine Learning Education Sub-Committee of the Society for Imaging Informatics in Medicine (SIIM) has identified a knowledge gap and need to establish guidelines for reviewing these studies. This present work, written from the machine learning practitioner standpoint, follows a similar approach to our previous paper related to segmentation. In this series, the committee will address best practices to follow in AI-based studies and present the required sections with examples and discussion of requirements to make the studies cohesive, reproducible, accurate, and self-contained. This entry in the series focuses on image classification. Elements like dataset curation, data pre-processing steps, reference standard identification, data partitioning, model architecture, and training are discussed. Sections are presented as in a typical manuscript. The content describes the information necessary to ensure the study is of sufficient quality for publication consideration and, compared with other checklists, provides a focused approach with application to image classification tasks. The goal of this series is to provide resources to not only help improve the review process for AI-based medical imaging papers, but to facilitate a standard for the information that should be presented within all components of the research study.

As many radiology departments embark on adopting artificial intelligence (AI) solutions in their clinical practice, they face the challenge that commercial applications often do not fit with their needs. As a result, they engage in a co-creation process with technology companies to collaboratively develop and implement AI solutions. Despite its importance, the process of co-creating AI solutions is under-researched, particularly regarding the range of challenges that may occur and how medical and technological parties can monitor, assess, and guide their co-creation process through an effective collaboration framework. Drawing on the multi-case study of three co-creation projects at an academic medical center in the Netherlands, we examine how co-creation processes happen through different scenarios, depending on the extent to which the two parties engage in "resourcing," "adaptation," and "reconfiguration." We offer a relational framework that helps involved parties monitor, assess, and guide their collaborations in co-creating AI solutions. The framework allows them to discover novel use-cases and reconsider their established assumptions and practices for developing AI solutions, also for redesigning their technological systems, clinical workflow, and their legal and organizational arrangements. Using the proposed framework, we identified distinct co-creation journeys with varying outcomes, which could be mapped onto the framework to diagnose, monitor, and guide collaborations toward desired results. The outcomes of co-creation can vary widely. The proposed framework enables medical institutions and technology companies to assess challenges and make adjustments. It can assist in steering their collaboration toward desired goals. Question How can medical institutions and AI startups effectively co-create AI solutions for radiology, ensuring alignment with clinical needs while steering collaboration effectively? Findings This study provides a co-creation framework allowing assessment of project progress, stakeholder engagement, as well as guidelines for radiology departments to steer co-creation of AI. Clinical relevance By actively involving radiology professionals in AI co-creation, this study demonstrates how co-creation helps bridge the gap between clinical needs and AI development, leading to clinically relevant, user-friendly solutions that enhance the radiology workflow.

Cancer continues to be a significant international health issue, which demands the invention of new methods for early detection, precise diagnoses, and personalized treatments. Artificial intelligence (AI) has rapidly become a groundbreaking component in the modern era of oncology, offering sophisticated tools across the range of cancer care. In this review, we performed a systematic survey of the current status of AI technologies used for cancer diagnoses and therapeutic approaches. We discuss AI-facilitated imaging diagnostics using a range of modalities such as computed tomography, magnetic resonance imaging, positron emission tomography, ultrasound, and digital pathology, highlighting the growing role of deep learning in detecting early-stage cancers. We also explore applications of AI in genomics and biomarker discovery, liquid biopsies, and non-invasive diagnoses. In therapeutic interventions, AI-based clinical decision support systems, individualized treatment planning, and AI-facilitated drug discovery are transforming precision cancer therapies. The review also evaluates the effects of AI on radiation therapy, robotic surgery, and patient management, including survival predictions, remote monitoring, and AI-facilitated clinical trials. Finally, we discuss important challenges such as data privacy, interpretability, and regulatory issues, and recommend future directions that involve the use of federated learning, synthetic biology, and quantum-boosted AI. This review highlights the groundbreaking potential of AI to revolutionize cancer care by making diagnostics, treatments, and patient management more precise, efficient, and personalized.

Study DesignLiterature review of key topics related to degenerative cervical myelopathy (DCM) with critical appraisal and clinical recommendations.ObjectiveThis article summarizes several key current topics related to the management of DCM.MethodsRecent literature related to the management of DCM was reviewed. Four articles were selected and critically appraised. Recommendations were graded as Strong or Conditional.ResultsArticle 1: The Relationship Between pre-operative MRI Signal Intensity and outcomes. <b>Conditional</b> recommendation to use diffusion-weighted imaging MR signal changes in the cervical cord to evaluate prognosis following surgical intervention for DCM. Article 2: Efficacy and Safety of Surgery for Mild DCM. <b>Conditional</b> recommendation that surgery is a valid option for mild DCM with favourable clinical outcomes. Article 3: Effect of Ventral vs Dorsal Spinal Surgery on Patient-Reported Physical Functioning in Patients With Cervical Spondylotic Myelopathy: A Randomized Clinical Trial. <b>Strong</b> recommendation that there is equipoise in the outcomes of anterior vs posterior surgical approaches in cases where either technique could be used. Article 4: Machine learning-based cluster analysis of DCM phenotypes. <b>Conditional</b> recommendation that clinicians consider pain, medical frailty, and the impact on health-related quality of life when counselling patients.ConclusionsDCM requires a multidimensional assessment including neurological dysfunction, pain, impact on health-related quality of life, medical frailty and MR imaging changes in the cord. Surgical treatment is effective and is a valid option for mild DCM. In patients where either anterior or posterior surgical approaches can be used, both techniques afford similar clinical benefit albeit with different complication profiles.

The deployment of AI in medical imaging, particularly in areas such as fracture detection, represents a transformative advancement in orthopaedic care. AI-driven systems, leveraging deep-learning algorithms, promise to enhance diagnostic accuracy, reduce variability, and streamline workflows by analyzing radiograph images swiftly and accurately. Despite these potential benefits, the integration of AI into clinical settings faces substantial barriers, including slow adoption across health systems, technical challenges, and a major lag between technology development and clinical implementation. This commentary explores the role of AI in healthcare, highlighting its potential to enhance patient outcomes through more accurate and timely diagnoses. It addresses the necessity of bridging the gap between AI innovation and practical application. It also emphasizes the importance of implementation science in effectively integrating AI technologies into healthcare systems, using frameworks such as the Consolidated Framework for Implementation Research and the Knowledge-to-Action Cycle to guide this process. We call for a structured approach to address the challenges of deploying AI in clinical settings, ensuring that AI's benefits translate into improved healthcare delivery and patient care.

Office-based testing, enhanced by advances in imaging technology, is routinely used in eye care to non-invasively assess ocular structure and function. This type of imaging coupled with autonomous artificial intelligence holds immense opportunity to diagnose eye diseases quickly. Despite the wide availability and use of ocular imaging, there are several factors that hinder optimization of clinical practice and patient care. While some large institutions have developed end-to-end digital workflows that utilize electronic health records, enterprise imaging archives, and dedicated diagnostic viewers, this experience has not yet made its way to smaller and independent eye clinics. Fractured interoperability practices impact patient care in all healthcare domains, including eye care where there is a scarcity of care centers, making collaboration essential among providers, specialists, and primary care who might be treating systemic conditions with profound impact on vision. The purpose of this white paper is to describe the current state of ocular imaging by focusing on the challenges related to interoperability, reporting, and clinical workflow.

AI tools in radiology are revolutionising the diagnosis, evaluation, and management of patients. However, there is a major gap between the large number of developed AI tools and those translated into daily clinical practice, which can be primarily attributed to limited usefulness and trust in current AI tools. Instead of technically driven development, little effort has been put into value-based development to ensure AI tools will have a clinically relevant impact on patient care. An iterative comprehensive value evaluation process covering the complete AI tool lifecycle should be part of radiology AI development. For value assessment of health technologies, health technology assessment (HTA) is an extensively used and comprehensive method. While most aspects of value covered by HTA apply to radiology AI, additional aspects, including transparency, explainability, and robustness, are unique to radiology AI and crucial in its value assessment. Additionally, value assessment should already be included early in the design stage to determine the potential impact and subsequent requirements of the AI tool. Such early assessment should be systematic, transparent, and practical to ensure all stakeholders and value aspects are considered. Hence, early value-based development by incorporating early HTA will lead to more valuable AI tools and thus facilitate translation to clinical practice. CLINICAL RELEVANCE STATEMENT: This paper advocates for the use of early value-based assessments. These assessments promote a comprehensive evaluation on how an AI tool in development can provide value in clinical practice and thus help improve the quality of these tools and the clinical process they support. KEY POINTS: Value in radiology AI should be perceived as a comprehensive term including health technology assessment domains and AI-specific domains. Incorporation of an early health technology assessment for radiology AI during development will lead to more valuable radiology AI tools. Comprehensive and transparent value assessment of radiology AI tools is essential for their widespread adoption.

The American College of Veterinary Radiology (ACVR) and the European College of Veterinary Diagnostic Imaging (ECVDI) recognize the transformative potential of AI in veterinary diagnostic imaging and radiation oncology. This position statement outlines the guiding principles for the ethical development and integration of AI technologies to ensure patient safety and clinical effectiveness. Artificial intelligence systems must adhere to good machine learning practices, emphasizing transparency, error reporting, and the involvement of clinical experts throughout development. These tools should also include robust mechanisms for secure patient data handling and postimplementation monitoring. The position highlights the critical importance of maintaining a veterinarian in the loop, preferably a board-certified radiologist or radiation oncologist, to interpret AI outputs and safeguard diagnostic quality. Currently, no commercially available AI products for veterinary diagnostic imaging meet the required standards for transparency, validation, or safety. The ACVR and ECVDI advocate for rigorous peer-reviewed research, unbiased third-party evaluations, and interdisciplinary collaboration to establish evidence-based benchmarks for AI applications. Additionally, the statement calls for enhanced education on AI for veterinary professionals, from foundational training in curricula to continuing education for practitioners. Veterinarians are encouraged to disclose AI usage to pet owners and provide alternative diagnostic options as needed. Regulatory bodies should establish guidelines to prevent misuse and protect the profession and patients. The ACVR and ECVDI stress the need for a cautious, informed approach to AI adoption, ensuring these technologies augment, rather than compromise, veterinary care.

Cone Beam Computed Tomography (CBCT) has become a crucial imaging tool in modern dentistry. At present, a review does not exist to provide comprehensive understanding of the technological advancements and the entities driving the innovations of CBCT. This study aimed to analyse the patent records associated with CBCT technology, to gain valuable insights into the trends and breakthroughs, and identify key areas of focus from manufacturers. The online patent database called The Lens was accessed on 3 January 2025 to identify relevant patent records. A total of 706 patent records were identified and analysed. The majority of over 700 CBCT patents was contributed by CBCT manufacturers. The United States was the jurisdiction with most patent records, followed by Europe and China. Some manufacturers hold patent records for common features of CBCT systems, such as motion artifact correction, metal artifact reduction, reconstruction of panoramic image based on 3D data, and incorporation of artificial intelligence. Patent analysis can offer valuable insights into the development and advancement of CBCT technology, and foster collaboration between manufacturers, researchers, and clinicians. The advancements in CBCT technology, as reflected by patent trends, enhance diagnostic accuracy and treatment planning. Understanding these technological innovations can aid clinicians in selecting the most effective imaging tools for patient care.