Integrating Text and Image Analysis: Exploring GPT-4V's Capabilities in Advanced Radiological Applications Across Subspecialties.

This study demonstrates that GPT-4V outperforms GPT-4 across radiology subspecialties in analyzing 207 cases with 1312 images from the Radiological Society of North America Case Collection.

The new era of artificial intelligence in neuroradiology: current research and promising tools.

Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.

A radiologia tem uma série de características que a torna uma disciplina médica especialmente adequada à adoção precoce da inteligência artificial (IA), incluindo um fluxo de trabalho digital bem estabelecido, protocolos padronizados para armazenamento de imagens e inúmeras atividades interpretativas bem definidas. Tal adequação é corroborada pelos mais de 200 produtos radiológicos comerciais baseados em IA recentemente aprovados pelo Food and Drug Administration (FDA) para auxiliar os radiologistas em uma série de tarefas restritas de análise de imagens, como quantificação, triagem de fluxo de trabalho e aprimoramento da qualidade das imagens. Entretanto, para o aumento da eficácia e eficiência da IA, além de uma utilização clínica bem-sucedida dos produtos que utilizam essa tecnologia, os radiologistas devem estar atualizados com as aplicações em suas áreas específicas de atuação. Assim, neste artigo, pesquisamos na literatura existente aplicações baseadas em IA em neurorradiologia, mais especificamente em condições como doenças vasculares, epilepsia, condições desmielinizantes e neurodegenerativas. Também abordamos os principais algoritmos por trás de tais aplicações, discutimos alguns dos desafios na generalização no uso desses modelos e introduzimos as soluções comercialmente disponíveis mais relevantes adotadas na prática clínica. Se cautelosamente desenvolvidos, os algoritmos de IA têm o potencial de melhorar radicalmente a radiologia, aperfeiçoando a análise de imagens, aumentando o valor das técnicas de imagem quantitativas e mitigando erros de diagnóstico.

Tumores renales múltiples y hereditarios. Revisión por y para radiólogos / Multiple and hereditary renal tumors: A review for radiologists

El 80% de los carcinomas renales (CR) se diagnostican incidentalmente por imagen. Se aceptan un 2-4% de multifocalidad «esporádica» y un 5-8% de síndromes hereditarios, probablemente con infraestimación. Multifocalidad, edad joven, historia familiar, datos sindrómicos y ciertas histologías hacen sospechar un síndrome hereditario. Debe estudiarse individualmente cada tumor y multidisciplinarmente al paciente, con estrategias terapéuticas conservadoras de nefronas y un abordaje diagnóstico radioprotector. Se revisan los datos relevantes para el radiólogo en los síndromes de von Hippel-Lindau, translocación de cromosoma-3, mutación de proteína-1 asociada a BRCA, CR asociado a déficit en succinato-deshidrogenasa, PTEN, CR papilar hereditario, cáncer papilar tiroideo-CR papilar, leiomiomatosis hereditaria y CR, Birt-Hogg-Dubé, complejo esclerosis tuberosa, Lynch, translocación Xp11.2/fusión TFE3, rasgo de células falciformes, mutación DICER1, hiperparatoridismo y tumor mandibular hereditario, así como los principales síndromes de predisposición al tumor de Wilms.(AU)

80% of renal carcinomas (RC) are diagnosed incidentally by imaging. 2-4% of “sporadic” multifocality and 5-8% of hereditary syndromes are accepted, probably with underestimation. Multifocality, young age, familiar history, syndromic data, and certain histologies lead to suspicion of hereditary syndrome. Each tumor must be studied individually, with a multidisciplinary evaluation of the patient. Nephron-sparing therapeutic strategies and a radioprotective diagnostic approach are recommended. Relevant data for the radiologist in major RC hereditary syndromes are presented: von-Hippel-Lindau, Chromosome-3 translocation, BRCA-associated protein-1 mutation, RC associated with succinate dehydrogenase deficiency, PTEN, hereditary papillary RC, Papillary thyroid cancer- Papillary RC, Hereditary leiomyomatosis and RC, Birt-Hogg-Dubé, Tuberous sclerosis complex, Lynch, Xp11.2 translocation/TFE3 fusion, Sickle cell trait, DICER1 mutation, Hereditary hyperparathyroidism and jaw tumor, as well as the main syndromes of Wilms tumor predisposition. The concept of “non-hereditary” familial RC and other malignant and benign entities that can present as multiple renal lesions are discussed.(AU)

Integrating human factors engineering into your pediatric radiology practice.

Human factors engineering involves the study and development of methods aimed at enhancing performance, improving safety, and optimizing user satisfaction. The focus of human factors engineering encompasses the design of work environments and an understanding of human mental processes to prevent errors. In this review, we summarize the history, applications, and impacts of human factors engineering on the healthcare field. To illustrate these applications and impacts, we provide several examples of how successful integration of a human factors engineer in our pediatric radiology department has positively impacted various projects. The successful integration of human factors engineering expertise has contributed to projects including improving response times for portable radiography requests, deploying COVID-19 response resources, informing the redesign of scheduling workflows, and implementation of a virtual ergonomics program for remote workers. In sum, the integration of human factors engineering insight into our department has resulted in tangible benefits and has also positioned us as proactive contributors to broader hospital-wide improvements.

Imagine there is no paperwork it's easy if you try.

Artificial Intelligence (AI) applied to radiology is so vast that it provides applications ranging from becoming a complete replacement for radiologists (a potential threat) to an efficient paperwork-saving time assistant (an evident strength). Nowadays, there are AI applications developed to facilitate the diagnostic process of radiologists without directly influencing (or replacing) the proper diagnostic decision step. These tools may help to reduce administrative workload, in different scenarios ranging from assisting in scheduling, study prioritization, or report communication, to helping with patient follow-up, including recommending additional exams. These are just a few of the highly time-consuming tasks that radiologists have to deal with every day in their routine workflow. These tasks hinder the time that radiologists should spend evaluating images and caring for patients, which will have a direct and negative impact on the quality of reports and patient attention, increasing the delay and waiting list of studies pending to be performed and reported. These types of AI applications should help to partially face this worldwide shortage of radiologists.

Artificial Intelligence in Radiology: Opportunities and Challenges.

Artificial intelligence's (AI) emergence in radiology elicits both excitement and uncertainty. AI holds promise for improving radiology with regards to clinical practice, education, and research opportunities. Yet, AI systems are trained on select datasets that can contain bias and inaccuracies. Radiologists must understand these limitations and engage with AI developers at every step of the process - from algorithm initiation and design to development and implementation - to maximize benefit and minimize harm that can be enabled by this technology.

Características operativas de la ecografía en el diagnóstico del Síndrome de Sjögren / Operational characteristics of ultrasound in the diagnosis of Sjögren's syndrome

Antecedentes y objetivo: Determinar las características operativas de la ecografía de glándula salival (EGS) en el diagnóstico del síndrome de Sjögren (SS) en una población de pacientes colombianos con síntomas secos. Materiales y métodos: Estudio de pruebas diagnósticas en pacientes con síntomas secos que asistieron a la consulta de reumatología (2018-2020). Se obtuvieron datos sociodemográficos y clínicos a través de una encuesta, pruebas paraclínicas, oftalmológicas, biopsia de glándula salival menor, flujo salival no estimulado y EGS (puntuación 0-6 basada en De Vita). Se calcularon la sensibilidad, la especificidad y los valores predictivos positivo (VPP) y negativo (VPN) (Stata 15®). Se desarrolló la curva de características operativas del receptor (COR). Resultados: Se incluyó a 102 pacientes (34 con SS y 68 sin SS), edad media ± desviación estándar de 55,69 ± 11,93 años, 94% mujeres. La ecografía positiva (puntuación de 2 o más) fue más frecuente en el grupo de SS, (70,6% vs. 22,1%, p < 0,0001). La sensibilidad fue igual para el grado 2 y 3 (70,59%), con una especificidad mayor (89,71%) para el grado 3 (VPP 77,42% VPN 85,92). La curva COR a partir de la sumatoria de las glándulas por medio de ecografía, fue mejor que las de las glándulas independientes. La curva COR de la ecografía presentó una mayor área bajo la curva (0,72 [0,61-0,82]) que la del análisis histológico (puntuación por focos) (0,68 [0,59-0,78]), p = 0,0252. Conclusión: La EGS es un método útil y confiable para la clasificación del SS. Se podría plantear su uso futuro dentro de los criterios clasificatorios del SS.(AU)

Background and objective: To determine the operational characteristics of salivary gland ultrasound (SGU) in the diagnosis of Sjögren's syndrome (SS) in a population of colombian patients with dry symptoms. Materials and methods: Study of diagnostic tests in patients with dry symptoms who consecutively attended the rheumatology consultation (2018-2020). Sociodemographic and clinical data were obtained through a survey, paraclinical and ophthalmological tests, minor salivary gland biopsy, unstimulated salivary flow and SGU (score 0-6 based on De Vita) were done. Sensitivity, specificity, positive (PPV) and negative (NPV) predictive values (Stata 15®) were calculated. The receiver operating characteristics (ROC) curve was developed. Results: 102 patients were included (34 SS and 68 non-SS), mean age 55.69 (± 11.93) years, 94% women. Positive ultrasound (score of 2 or more) was more frequent in the SS group, (70.6% vs. 22.1%, P<.0001). The sensitivity was the same for grade 2 and 3 (70.59%), with a higher specificity (89.71%) for grade 3 (PPV 77.42% NPV 85.92). The ROC curve from the sum of the glands by means of ultrasound was better than those of the independent glands. The ROC curve of the ultrasound presented a greater area under the curve (0.72 [0.61-0.82]) than that of the histological analysis (focus score) (0.68 [0.59-0.78]), P=.0252. Conclusion: Salivary gland ultrasound is a useful and reliable method for the classification of SS. Its use could be considered in the future within the SS classification criteria.

Lesiones inflamatorias del troncoencéfalo: claves diagnósticas en RM / Inflammatory lesions of the brainstem: Keys for the diagnosis by MRI

Objetivo: Describir los hallazgos en resonancia magnética (RM) de las principales enfermedades inflamatorias e inmunomediadas que afectan al troncoencéfalo. Conclusión: El diagnóstico diferencial de las lesiones inflamatorias localizadas en el troncoencéfalo es complicado debido al amplio espectro de enfermedades autoinmunes, infecciosas y síndromes paraneoplásicos que pueden causarlas. Conocer estas entidades, sus características clínicas y sus manifestaciones en RM, sobre todo en cuanto a número, morfología, extensión y apariencia en las diferentes secuencias, es útil a la hora de orientar el diagnóstico radiológico.(AU)

Objective: To describe the magnetic resonance imaging (MRI) findings for the most common inflammatory and immune-mediated diseases that involve the brainstem. Conclusion: Inflammatory lesions involving the brainstem are associated with a wide range of autoimmune, infectious, and paraneoplastic syndromes, making the differential diagnosis complex. Being familiar with these entities, their clinical characteristics, and their manifestations on MRI, particularly the number of lesions, their shape and extension, and their appearance in different sequences, is useful for orienting the radiological diagnosis.(AU)

Current and emerging 3D visualization technologies in radiology.

As the field of three-dimensional (3D) visualization rapidly advances, how healthcare professionals perceive and interact with real and virtual objects becomes increasingly complex. Lack of clear vocabulary to navigate the changing landscape of 3D visualization hinders clinical and scientific advancement, particularly within the field of radiology. In this article, we provide foundational definitions and illustrative examples for 3D visualization in clinical care, with a focus on the pediatric patient population. We also describe how understanding 3D visualization tools enables better alignment of hardware and software products with intended use-cases, thereby maximizing impact for patients, families, and healthcare professionals.

State of Academic Radiology: Current Challenges, Future Adaptations.

There are approximately 200 academic radiology departments in the United States. While academic medical centers vary widely depending on their size, complexity, medical school affiliation, research portfolio, and geographic location, they are united by their 3 core missions: patient care, education and training, and scholarship. Despite inherent differences, the current challenges faced by all academic radiology departments have common threads; potential solutions and future adaptations will need to be tailored and individualized-one size will not fit all. In this article, we provide an overview based on our experiences at 4 academic centers across the United States, from relatively small to very large size, and discuss creative and innovative ways to adapt, including community expansion, hybrid models of faculty in-person vs teleradiology (traditional vs non-traditional schedule), work-life integration, recruitment and retention, mentorship, among others.

Advancing Diagnostics and Patient Care: The Role of Biomarkers in Radiology.

The integration of biomarkers into medical practice has revolutionized the field of radiology, allowing for enhanced diagnostic accuracy, personalized treatment strategies, and improved patient care outcomes. This review offers radiologists a comprehensive understanding of the diverse applications of biomarkers in medicine. By elucidating the fundamental concepts, challenges, and recent advancements in biomarker utilization, it will serve as a bridge between the disciplines of radiology and epidemiology. Through an exploration of various biomarker types, such as imaging biomarkers, molecular biomarkers, and genetic markers, I outline their roles in disease detection, prognosis prediction, and therapeutic monitoring. I also discuss the significance of robust study designs, blinding, power and sample size calculations, performance metrics, and statistical methodologies in biomarker research. By fostering collaboration between radiologists, statisticians, and epidemiologists, I hope to accelerate the translation of biomarker discoveries into clinical practice, ultimately leading to improved patient care.

Imaging 3.0: A scoping review.

We aim to provide a comprehensive summary of the current body of literature concerning the Imaging 3.0 initiative and its implications for patient care within the field of radiology. We offer a thorough analysis of the literature pertaining to the Imaging 3.0 initiative, emphasizing the practical application of the five pillars of the program, their cost-effectiveness, and their benefits in patient management. By doing so, we hope to illustrate the impact the Imaging 3.0 Initiative can have on the future of radiology and patient care.

Assessing radiologists' and radiographers' perceptions on artificial intelligence integration: opportunities and challenges.

OBJECTIVES: The objective of this study was to evaluate radiologists' and radiographers' opinions and perspectives on artificial intelligence (AI) and its integration into the radiology department. Additionally, we investigated the most common challenges and barriers that radiologists and radiographers face when learning about AI. METHODS: A nationwide, online descriptive cross-sectional survey was distributed to radiologists and radiographers working in hospitals and medical centres from May 29, 2023 to July 30, 2023. The questionnaire examined the participants' opinions, feelings, and predictions regarding AI and its applications in the radiology department. Descriptive statistics were used to report the participants' demographics and responses. Five-points Likert-scale data were reported using divergent stacked bar graphs to highlight any central tendencies. RESULTS: Responses were collected from 258 participants, revealing a positive attitude towards implementing AI. Both radiologists and radiographers predicted breast imaging would be the subspecialty most impacted by the AI revolution. MRI, mammography, and CT were identified as the primary modalities with significant importance in the field of AI application. The major barrier encountered by radiologists and radiographers when learning about AI was the lack of mentorship, guidance, and support from experts. CONCLUSION: Participants demonstrated a positive attitude towards learning about AI and implementing it in the radiology practice. However, radiologists and radiographers encounter several barriers when learning about AI, such as the absence of experienced professionals support and direction. ADVANCES IN KNOWLEDGE: Radiologists and radiographers reported several barriers to AI learning, with the most significant being the lack of mentorship and guidance from experts, followed by the lack of funding and investment in new technologies.

Closing the loop for AI-ready radiology.

BACKGROUND: In recent years, AI has made significant advancements in medical diagnosis and prognosis. However, the incorporation of AI into clinical practice is still challenging and under-appreciated. We aim to demonstrate a possible vertical integration approach to close the loop for AI-ready radiology. METHOD: This study highlights the importance of two-way communication for AI-assisted radiology. As a key part of the methodology, it demonstrates the integration of AI systems into clinical practice with structured reports and AI visualization, giving more insight into the AI system. By integrating cooperative lifelong learning into the AI system, we ensure the long-term effectiveness of the AI system, while keeping the radiologist in the loop.  RESULTS: We demonstrate the use of lifelong learning for AI systems by incorporating AI visualization and structured reports. We evaluate Memory Aware-Synapses and Rehearsal approach and find that both approaches work in practice. Furthermore, we see the advantage of lifelong learning algorithms that do not require the storing or maintaining of samples from previous datasets. CONCLUSION: In conclusion, incorporating AI into the clinical routine of radiology requires a two-way communication approach and seamless integration of the AI system, which we achieve with structured reports and visualization of the insight gained by the model. Closing the loop for radiology leads to successful integration, enabling lifelong learning for the AI system, which is crucial for sustainable long-term performance. KEY POINTS: · The integration of AI systems into the clinical routine with structured reports and AI visualization.. · Two-way communication between AI and radiologists is necessary to enable AI that keeps the radiologist in the loop.. · Closing the loop enables lifelong learning, which is crucial for long-term, high-performing AI in radiology..

How artificial intelligence is reshaping the autonomy and boundary work of radiologists. A qualitative study.

The application of artificial intelligence (AI) in medical practice is spreading, especially in technologically dense fields such as radiology, which could consequently undergo profound transformations in the near future. This article aims to qualitatively explore the potential influence of AI technologies on the professional identity of radiologists. Drawing on 12 in-depth interviews with a subgroup of radiologists who participated in a larger study, this article investigated (1) whether radiologists perceived AI as a threat to their decision-making autonomy; and (2) how radiologists perceived the future of their profession compared to other health-care professions. The findings revealed that while AI did not generally affect radiologists' decision-making autonomy, it threatened their professional and epistemic authority. Two discursive strategies were identified to explain these findings. The first strategy emphasised radiologists' specific expertise and knowledge that extends beyond interpreting images, a task performed with high accuracy by AI machines. The second strategy underscored the fostering of radiologists' professional prestige through developing expertise in using AI technologies, a skill that would distinguish them from other clinicians who did not pose this knowledge. This study identifies AI machines as status objects and useful tools in performing boundary work in and around the radiological profession.

A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research.

Artificial intelligence (AI) has the potential to bring transformative improvements to the field of radiology; yet, there are barriers to widespread clinical adoption. One of the most important barriers has been access to large, well-annotated, widely representative medical image datasets, which can be used to accurately train AI programs. Creating such datasets requires time and expertise and runs into constraints around data security and interoperability, patient privacy, and appropriate data use. Recognizing these challenges, several institutions have started curating and providing publicly available, high-quality datasets that can be accessed by researchers to advance AI models. The purpose of this work was to review the publicly available MRI datasets that can be used for AI research in radiology. Despite being an emerging field, a simple internet search for open MRI datasets presents an overwhelming number of results. Therefore, we decided to create a survey of the major publicly accessible MRI datasets in different subfields of radiology (brain, body, and musculoskeletal), and list the most important features of value to the AI researcher. To complete this review, we searched for publicly available MRI datasets and assessed them based on several parameters (number of subjects, demographics, area of interest, technical features, and annotations). We reviewed 110 datasets across sub-fields with 1,686,245 subjects in 12 different areas of interest ranging from spine to cardiac. This review is meant to serve as a reference for researchers to help spur advancements in the field of AI for radiology. LEVEL OF EVIDENCE: Level 4 TECHNICAL EFFICACY: Stage 6.