Patient-centered radiology reports with generative artificial intelligence: adding value to radiology reporting.

The purposes were to assess the efficacy of AI-generated radiology reports in terms of report summary, patient-friendliness, and recommendations and to evaluate the consistent performance of report quality and accuracy, contributing to the advancement of radiology workflow. Total 685 spine MRI reports were retrieved from our hospital database. AI-generated radiology reports were generated in three formats: (1) summary reports, (2) patient-friendly reports, and (3) recommendations. The occurrence of artificial hallucinations was evaluated in the AI-generated reports. Two radiologists conducted qualitative and quantitative assessments considering the original report as a standard reference. Two non-physician raters assessed their understanding of the content of original and patient-friendly reports using a 5-point Likert scale. The scoring of the AI-generated radiology reports were overall high average scores across all three formats. The average comprehension score for the original report was 2.71 ± 0.73, while the score for the patient-friendly reports significantly increased to 4.69 ± 0.48 (p < 0.001). There were 1.12% artificial hallucinations and 7.40% potentially harmful translations. In conclusion, the potential benefits of using generative AI assistants to generate these reports include improved report quality, greater efficiency in radiology workflow for producing summaries, patient-centered reports, and recommendations, and a move toward patient-centered radiology.

Visual Recalibration and Gating Enhancement Network for Radiology Report Generation.

Automatic radiology medical report generation is a necessary development of artificial intelligence technology in the health care. This technology serves to aid doctors in producing comprehensive diagnostic reports, alleviating the burdensome workloads of medical professionals. However, there are some challenges in generating radiological reports: (1) visual and textual data biases and (2) long-distance dependency problem. To tackle these issues, we design a visual recalibration and gating enhancement network (VRGE), which composes of the visual recalibration module and the gating enhancement module (gating enhancement module, GEM). Specifically, the visual recalibration module enhances the recognition of abnormal features in lesion areas of medical images. The GEM dynamically adjusts the contextual information in the report by introducing gating mechanisms, focusing on capturing professional medical terminology in medical text reports. We have conducted sufficient experiments on the public datasets of IU X-Ray to illustrate that the VRGE outperforms existing models.

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.

Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology.

Precision medicine aims to provide personalized care based on individual patient characteristics, rather than guideline-directed therapies for groups of diseases or patient demographics. Images-both radiology- and pathology-derived-are a major source of information on presence, type, and status of disease. Exploring the mathematical relationship of pixels in medical imaging ("radiomics") and cellular-scale structures in digital pathology slides ("pathomics") offers powerful tools for extracting both qualitative and, increasingly, quantitative data. These analytical approaches, however, may be significantly enhanced by applying additional methods arising from fields of mathematics such as differential geometry and algebraic topology that remain underexplored in this context. Geometry's strength lies in its ability to provide precise local measurements, such as curvature, that can be crucial for identifying abnormalities at multiple spatial levels. These measurements can augment the quantitative features extracted in conventional radiomics, leading to more nuanced diagnostics. By contrast, topology serves as a robust shape descriptor, capturing essential features such as connected components and holes. The field of topological data analysis was initially founded to explore the shape of data, with functional network connectivity in the brain being a prominent example. Increasingly, its tools are now being used to explore organizational patterns of physical structures in medical images and digitized pathology slides. By leveraging tools from both differential geometry and algebraic topology, researchers and clinicians may be able to obtain a more comprehensive, multi-layered understanding of medical images and contribute to precision medicine's armamentarium.

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)

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.

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.

The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI.

The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forward by enhancing image analysis and interpretation. The introduction of the Transformer architecture, followed by the development of Large Language Models (LLMs), has further revolutionized this domain. LLMs now possess the potential to automate and refine the radiology workflow, extending from report generation to assistance in diagnostics and patient care. The integration of multimodal technology with LLMs could potentially leapfrog these applications to unprecedented levels.However, LLMs come with unresolved challenges such as information hallucinations and biases, which can affect clinical reliability. Despite these issues, the legislative and guideline frameworks have yet to catch up with technological advancements. Radiologists must acquire a thorough understanding of these technologies to leverage LLMs' potential to the fullest while maintaining medical safety and ethics. This review aims to aid in that endeavor.

Multi-Grained Radiology Report Generation With Sentence-Level Image-Language Contrastive Learning.

The automatic generation of accurate radiology reports is of great clinical importance and has drawn growing research interest. However, it is still a challenging task due to the imbalance between normal and abnormal descriptions and the multi-sentence and multi-topic nature of radiology reports. These features result in significant challenges to generating accurate descriptions for medical images, especially the important abnormal findings. Previous methods to tackle these problems rely heavily on extra manual annotations, which are expensive to acquire. We propose a multi-grained report generation framework incorporating sentence-level image-sentence contrastive learning, which does not require any extra labeling but effectively learns knowledge from the image-report pairs. We first introduce contrastive learning as an auxiliary task for image feature learning. Different from previous contrastive methods, we exploit the multi-topic nature of imaging reports and perform fine-grained contrastive learning by extracting sentence topics and contents and contrasting between sentence contents and refined image contents guided by sentence topics. This forces the model to learn distinct abnormal image features for each specific topic. During generation, we use two decoders to first generate coarse sentence topics and then the fine-grained text of each sentence. We directly supervise the intermediate topics using sentence topics learned by our contrastive objective. This strengthens the generation constraint and enables independent fine-tuning of the decoders using reinforcement learning, which further boosts model performance. Experiments on two large-scale datasets MIMIC-CXR and IU-Xray demonstrate that our approach outperforms existing state-of-the-art methods, evaluated by both language generation metrics and clinical accuracy.

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.

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.

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.

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.

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)

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.