While some metrics cover clinical entities and their relations[9, 11], generally Several evaluation metrics have been developed recently to scoring metrics do not explicitly capture the textual mention automatically assess the quality of generative AI reports for differences in the anatomy, laterality and severity. Further, chest radiographs based only on textual information using phrasal grounding of the findings in terms of anatomical localization lexical, semantic, or clinical named entity recognition methods. in images is not exploited in the quality scoring. In this paper, we develop a new method of report quality In this paper, we propose a metric that captures both finegrained evaluation by first extracting fine-grained finding patterns textual descriptions of findings as well as their phrasal capturing the location, laterality, and severity of a large number grounding information in terms of anatomical locations in images. of clinical findings. We then performed phrasal grounding We present results that compare this evaluation metric to localize their associated anatomical regions on chest radiograph to other textual metrics on a gold standard dataset derived images. The textual and visual measures are then combined from MIMIC collection of chest X-rays and validated reports, to rate the quality of the generated reports. We present to show its robustness and sensitivity to factual errors.

The integration of artificial intelligence (AI) chatbots into higher education marks a shift towards a new generation of pedagogical tools, mirroring the arrival of milestones like the internet. With the launch of ChatGPT-4 Turbo in November 2023, we developed a ChatGPT-based teaching application (https://chat.openai.com/g/g-1imx1py4K-chatge-medical-imaging) and integrated it into our undergraduate medical imaging course in the Spring 2024 semester. This study investigates the use of ChatGPT throughout a semester-long trial, providing insights into students' engagement, perception, and the overall educational effectiveness of the technology. We systematically collected and analyzed data concerning students' interaction with ChatGPT, focusing on their attitudes, concerns, and usage patterns. The findings indicate that ChatGPT offers significant advantages such as improved information access and increased interactivity, but its adoption is accompanied by concerns about the accuracy of the information provided and the necessity for well-defined guidelines to optimize its use.

Existing medical image registration algorithms rely on either dataset specific training or local texture-based features to align images. The former cannot be reliably implemented without large modality-specific training datasets, while the latter lacks global semantics thus could be easily trapped at local minima. In this paper, we present a training-free deformable image registration method, DINO-Reg, leveraging a general purpose image encoder DINOv2 for image feature extraction. The DINOv2 encoder was trained using the ImageNet data containing natural images. We used the pretrained DINOv2 without any finetuning. Our method feeds the DINOv2 encoded features into a discrete optimizer to find the optimal deformable registration field. We conducted a series of experiments to understand the behavior and role of such a general purpose image encoder in the application of image registration. Combined with handcrafted features, our method won the first place in the recent OncoReg Challenge. To our knowledge, this is the first application of general vision foundation models in medical image registration.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease; yet its currently available treatments are limited to stopping disease progression. Moreover, effectiveness of these treatments is not guaranteed due to the heterogenetiy of the disease. Therefore, it is essential to be able to identify the disease subtypes at a very early stage. Current data driven approaches are able to classify the subtypes at later stages of AD or related disorders, but struggle when predicting at the asymptomatic or prodromal stage. Moreover, most existing models either lack explainability behind the classification or only use a single modality for the assessment, limiting scope of its analysis. Thus, we propose a multimodal framework that uses early-stage indicators such as imaging, genetics and clinical assessments to classify AD patients into subtypes at early stages. Similarly, we build prompts and use large language models, such as ChatGPT, to interpret the findings of our model. In our framework, we propose a tri-modal co-attention mechanism (Tri-COAT) to explicitly learn the cross-modal feature associations. Our proposed model outperforms baseline models and provides insight into key cross-modal feature associations supported by known biological mechanisms.

Domain shift is a common problem in clinical applications, where the training images (source domain) and the test images (target domain) are under different distributions. Unsupervised Domain Adaptation (UDA) techniques have been proposed to adapt models trained in the source domain to the target domain. However, those methods require a large number of images from the target domain for model training. In this paper, we propose a novel method for Few-Shot Unsupervised Domain Adaptation (FSUDA), where only a limited number of unlabeled target domain samples are available for training. To accomplish this challenging task, first, a spectral sensitivity map is introduced to characterize the generalization weaknesses of models in the frequency domain. We then developed a Sensitivity-guided Spectral Adversarial MixUp (SAMix) method to generate target-style images to effectively suppresses the model sensitivity, which leads to improved model generalizability in the target domain. We demonstrated the proposed method and rigorously evaluated its performance on multiple tasks using several public datasets. The source code is available at https: //github.com/RPIDIAL/SAMix.

With advances in generative artificial intelligence (AI), it is now possible to produce realistic-looking automated reports for preliminary reads of radiology images. This can expedite clinical workflows, improve accuracy and reduce overall costs. However, it is also well-known that such models often hallucinate, leading to false findings in the generated reports. In this paper, we propose a new method of fact-checking of AI-generated reports using their associated images. Specifically, the developed examiner differentiates real and fake sentences in reports by learning the association between an image and sentences describing real or potentially fake findings. To train such an examiner, we first created a new dataset of fake reports by perturbing the findings in the original ground truth radiology reports associated with images. Text encodings of real and fake sentences drawn from these reports are then paired with image encodings to learn the mapping to real/fake labels. The utility of such an examiner is demonstrated for verifying automatically generated reports by detecting and removing fake sentences. Future generative AI approaches can use the resulting tool to validate their reports leading to a more responsible use of AI in expediting clinical workflows.

The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP). LLMs have revolutionized a multitude of domains, and they have made a significant impact in the medical field. Large language models are now more abundant than ever, and many of these models exhibit bilingual capabilities, proficient in both English and Chinese. However, a comprehensive evaluation of these models remains to be conducted. This lack of assessment is especially apparent within the context of radiology NLP. This study seeks to bridge this gap by critically evaluating thirty two LLMs in interpreting radiology reports, a crucial component of radiology NLP. Specifically, the ability to derive impressions from radiologic findings is assessed. The outcomes of this evaluation provide key insights into the performance, strengths, and weaknesses of these LLMs, informing their practical applications within the medical domain.

In CMF surgery, the planning of bony movement to achieve a desired facial outcome is a challenging task. Current bone driven approaches focus on normalizing the bone with the expectation that the facial appearance will be corrected accordingly. However, due to the complex non-linear relationship between bony structure and facial soft-tissue, such bone-driven methods are insufficient to correct facial deformities. Despite efforts to simulate facial changes resulting from bony movement, surgical planning still relies on iterative revisions and educated guesses. To address these issues, we propose a soft-tissue driven framework that can automatically create and verify surgical plans. Our framework consists of a bony planner network that estimates the bony movements required to achieve the desired facial outcome and a facial simulator network that can simulate the possible facial changes resulting from the estimated bony movement plans. By combining these two models, we can verify and determine the final bony movement required for planning. The proposed framework was evaluated using a clinical dataset, and our experimental results demonstrate that the soft-tissue driven approach greatly improves the accuracy and efficacy of surgical planning when compared to the conventional bone-driven approach.

In this review, we explore the potential applications of Artificial General Intelligence (AGI) models in healthcare, focusing on foundational Large Language Models (LLMs), Large Vision Models, and Large Multimodal Models. We emphasize the importance of integrating clinical expertise, domain knowledge, and multimodal capabilities into AGI models. In addition, we lay out key roadmaps that guide the development and deployment of healthcare AGI models. Throughout the review, we provide critical perspectives on the potential challenges and pitfalls associated with deploying large-scale AGI models in the medical field. This comprehensive review aims to offer insights into the future implications of AGI in medical imaging, healthcare and beyond.

Domain generalization (DG) aims to train a model to perform well in unseen domains under different distributions. This paper considers a more realistic yet more challenging scenario,namely Single Domain Generalization (Single-DG), where only a single source domain is available for training. To tackle this challenge, we first try to understand when neural networks fail to generalize? We empirically ascertain a property of a model that correlates strongly with its generalization that we coin as "model sensitivity". Based on our analysis, we propose a novel strategy of Spectral Adversarial Data Augmentation (SADA) to generate augmented images targeted at the highly sensitive frequencies. Models trained with these hard-to-learn samples can effectively suppress the sensitivity in the frequency space, which leads to improved generalization performance. Extensive experiments on multiple public datasets demonstrate the superiority of our approach, which surpasses the state-of-the-art single-DG methods.