Agentic Artificial Intelligence (AI) systems leveraging Large Language Models (LLMs) exhibit significant potential for complex reasoning, planning, and tool utilization. We demonstrate that a specialized computer vision system can be built autonomously from a natural language prompt using Agentic AI methods. This involved extending SimpleMind (SM), an open-source Cognitive AI environment with configurable tools for medical image analysis, with an LLM-based agent, implemented using OpenManus, to automate the planning (tool configuration) for a particular computer vision task. We provide a proof-of-concept demonstration that an agentic system can interpret a computer vision task prompt, plan a corresponding SimpleMind workflow by decomposing the task and configuring appropriate tools. From the user input prompt, "provide sm (SimpleMind) config for lungs, heart, and ribs segmentation for cxr (chest x-ray)"), the agent LLM was able to generate the plan (tool configuration file in YAML format), and execute SM-Learn (training) and SM-Think (inference) scripts autonomously. The computer vision agent automatically configured, trained, and tested itself on 50 chest x-ray images, achieving mean dice scores of 0.96, 0.82, 0.83, for lungs, heart, and ribs, respectively. This work shows the potential for autonomous planning and tool configuration that has traditionally been performed by a data scientist in the development of computer vision applications.

Agentic Artificial Intelligence (AI) systems leveraging Large Language Models (LLMs) exhibit significant potential for complex reasoning, planning, and tool utilization. We demonstrate that a specialized computer vision system can be built autonomously from a natural language prompt using Agentic AI methods. This involved extending SimpleMind (SM), an open-source Cognitive AI environment with configurable tools for medical image analysis, with an LLM-based agent, implemented using OpenManus, to automate the planning (tool configuration) for a particular computer vision task. We provide a proof-of-concept demonstration that an agentic system can interpret a computer vision task prompt, plan a corresponding SimpleMind workflow by decomposing the task and configuring appropriate tools. From the user input prompt, "provide sm (SimpleMind) config for lungs, heart, and ribs segmentation for cxr (chest x-ray)"), the agent LLM was able to generate the plan (tool configuration file in YAML format), and execute SM-Learn (training) and SM-Think (inference) scripts autonomously. The computer vision agent automatically configured, trained, and tested itself on 50 chest x-ray images, achieving mean dice scores of 0.96, 0.82, 0.83, for lungs, heart, and ribs, respectively. This work shows the potential for autonomous planning and tool configuration that has traditionally been performed by a data scientist in the development of computer vision applications.

Chest radiography frequently serves as baseline imaging for most lung diseases¹. Deep learning has great potential for automating the interpretation of chest radiography². However, existing chest radiographic deep learning models are limited in diagnostic scope, generalizability, adaptability, robustness and extensibility. To overcome these limitations, we have developed Ark⁺, a foundation model applied to chest radiography and pretrained by cyclically accruing and reusing the knowledge from heterogeneous expert labels in numerous datasets. Ark⁺ excels in diagnosing thoracic diseases. It expands the diagnostic scope and addresses potential misdiagnosis. It can adapt to evolving diagnostic needs and respond to novel diseases. It can learn rare conditions from a few samples and transfer to new diagnostic settings without training. It tolerates data biases and long-tailed distributions, and it supports federated learning to preserve privacy. All codes and pretrained models have been released, so that Ark⁺ is open for fine-tuning, local adaptation and improvement. It is extensible to several modalities. Thus, it is a foundation model for medical imaging. The exceptional capabilities of Ark⁺ stem from our insight: aggregating various datasets diversifies the patient populations and accrues knowledge from many experts to yield unprecedented performance while reducing annotation costs³. The development of Ark⁺ reveals that open models trained by accruing and reusing knowledge from heterogeneous expert annotations with a multitude of public (big or small) datasets can surpass the performance of proprietary models trained on large data. We hope that our findings will inspire more researchers to share code and datasets or federate privacy-preserving data to create open foundation models with diverse, global expertise and patient populations, thus accelerating open science and democratizing AI for medicine.

With the increasing prevalence of respiratory diseases such as pneumonia and COVID-19, timely and accurate diagnosis is critical. This paper makes significant contributions to the field of respiratory disease classification by utilizing X-ray images and advanced machine learning techniques such as deep learning (DL) and Vision Transformers (ViT). First, the paper systematically reviews the current diagnostic methodologies, analyzing the recent advancement in DL and ViT techniques through a comprehensive analysis of the review articles published between 2017 and 2024, excluding short reviews and overviews. The review not only analyses the existing knowledge but also identifies the critical gaps in the field as well as the lack of diversity of the comprehensive and diverse datasets for training the machine learning models. To address such limitations, the paper extensively evaluates DL-based models on publicly available datasets, analyzing key performance metrics such as accuracy, precision, recall, and F1-score. Our evaluations reveal that the current datasets are mostly limited to the narrow subsets of pulmonary diseases, which might lead to some challenges, including overfitting, poor generalization, and reduced possibility of using advanced machine learning techniques in real-world applications. For instance, DL and ViT models require extensive data for effective learning. The primary contribution of this paper is not only the review of the most recent articles and surveys of respiratory diseases and DL models, including ViT, but also introduces a novel, diverse dataset comprising 7867 X-ray images from 5263 patients across three local hospitals, covering 49 distinct pulmonary diseases. The dataset is expected to enhance DL and ViT model training and improve the generalization of those models in various real-world medical image scenarios. By addressing the data scarcity issue, this paper paves the for more reliable and robust disease classification, improving clinical decision-making. Additionally, the article highlights the critical challenges that still need to be addressed, such as dataset bias and variations of X-ray image quality, as well as the need for further clinical validation. Furthermore, the study underscores the critical role of DL in medical diagnosis and highlights the necessity of comprehensive, well-annotated datasets to improve model robustness and clinical reliability. Through these contributions, the paper provides the basis and foundation of future research on respiratory disease diagnosis using AI-driven methodologies. Although the paper tries to cover all the work done between 2017 and 2024, this research might have some limitations of this research, including the review period before 2017 might have foundational work. At the same time, the rapid development of AI might make the earlier methods less relevant.

Medical reports, particularly radiology findings, are often written for professional communication, making them difficult for patients to understand. This communication barrier can reduce patient engagement and lead to misinterpretation. Artificial intelligence (AI), especially large language models such as ChatGPT, offers new opportunities for simplifying medical documentation to improve patient comprehension. We aimed to evaluate whether AI-generated radiology reports simplified by ChatGPT improve patient understanding, readability, and communication quality compared to original AI-generated reports. In total, 3 versions of radiology reports were created using ChatGPT: an original AI-generated version (text 1), a patient-friendly, simplified version (text 2), and a further simplified and accessibility-optimized version (text 3). A total of 300 patients (n=100, 33.3% per group), excluding patients with medical education, were randomly assigned to review one text version and complete a standardized questionnaire. Readability was assessed using the Flesch Reading Ease (FRE) score and LIX indices. Both simplified texts showed significantly higher readability scores (text 1: FRE score=51.1; text 2: FRE score=55.0; and text 3: FRE score=56.4; P<.001) and lower LIX scores, indicating enhanced clarity. Text 3 had the shortest sentences, had the fewest long words, and scored best on all patient-rated dimensions. Questionnaire results revealed significantly higher ratings for texts 2 and 3 across clarity (P<.001), tone (P<.001), structure, and patient engagement. For example, patients rated the ability to understand findings without help highest for text 3 (mean 1.5, SD 0.7) and lowest for text 1 (mean 3.1, SD 1.4). Both simplified texts significantly improved patients' ability to prepare for clinical conversations and promoted shared decision-making. AI-generated simplification of radiology reports significantly enhances patient comprehension and engagement. These findings highlight the potential of ChatGPT as a tool to improve patient-centered communication. While promising, future research should focus on ensuring clinical accuracy and exploring applications across diverse patient populations to support equitable and effective integration of AI in health care communication.

Missed fractures are the primary cause of interpretation errors in emergency radiology, and artificial intelligence has recently shown great promise in radiograph interpretation. This study compared the diagnostic performance of two AI algorithms, BoneView and RBfracture, in detecting traumatic abnormalities (fractures and dislocations) in MSK radiographs. AI algorithms analyzed 998 radiographs (585 normal, 413 abnormal), against the consensus of two MSK specialists. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and interobserver agreement (Cohen's Kappa) were calculated. 95% confidence intervals (CI) assessed robustness, and McNemar's tests compared sensitivity and specificity between the AI algorithms. BoneView demonstrated a sensitivity of 0.893 (95% CI: 0.860-0.920), specificity of 0.885 (95% CI: 0.857-0.909), PPV of 0.846, NPV of 0.922, and accuracy of 0.889. RBfracture demonstrated a sensitivity of 0.872 (95% CI: 0.836-0.901), specificity of 0.892 (95% CI: 0.865-0.915), PPV of 0.851, NPV of 0.908, and accuracy of 0.884. No statistically significant differences were found in sensitivity (p = 0.151) or specificity (p = 0.708). Kappa was 0.81 (95% CI: 0.77-0.84), indicating almost perfect agreement between the two AI algorithms. Performance was similar in adults and children. Both AI algorithms struggled more with subtle abnormalities, which constituted 66% and 70% of false negatives but only 20% and 18% of true positives for the two AI algorithms, respectively (p < 0.001). BoneView and RBfracture exhibited high diagnostic performance and almost perfect agreement, with consistent results across adults and children, highlighting the potential of AI in emergency radiograph interpretation.

This study has focused on sex determination from the variables estimated on X-ray images of the talocrural joint by using machine learning algorithms (ML). The variables of the mediolateral diameter of tibia (TMLD) and fibula (FMLD), the distance between the innermost points of the talocrural joint (DIT), the distance between the outermost points of the talocrural joint (DOT), and the distal articular surface of the tibia (TAS) estimated using X-ray images of 150 women and 150 men were evaluated by applying different ML methods. Logistic regression classifier, Decision Tree classifier, K-Nearest Neighbor classifier, Linear Discriminant Analysis, Naive Bayes and Random Forest classifier were used as algorithms. As a result of ML, an accuracy between 82 and 92 % was found. The highest rate of accuracy was achieved with RFC algorithm. DOT was the variable which contributed to the model at highest degree. Except for the variables of the age and FMLD, the other variables were found to be statistically significant in terms of sex difference. It was found that the variables of the talocrural joint were classified with high accuracy in terms of sex. In addition, morphometric data were found about the population and racial differences were emphasized.

Artificial intelligence (AI) is rapidly transforming healthcare, but its benefits are not reaching all patients equally. Children remain overlooked with only 17% of FDA-approved medical AI devices labeled for pediatric use. In this work, we demonstrate that this exclusion may stem from a fundamental data gap. Our systematic review of 181 public medical imaging datasets reveals that children represent just under 1% of available data, while the majority of machine learning imaging conference papers we surveyed utilized publicly available data for methods development. Much like systematic biases of other kinds in model development, past studies have demonstrated the manner in which pediatric representation in data used for models intended for the pediatric population is essential for model performance in that population. We add to these findings, showing that adult-trained chest radiograph models exhibit significant age bias when applied to pediatric populations, with higher false positive rates in younger children. This work underscores the urgent need for increased pediatric representation in publicly accessible medical datasets. We provide actionable recommendations for researchers, policymakers, and data curators to address this age equity gap and ensure AI benefits patients of all ages. 1-2 sentence summaryOur analysis reveals a critical healthcare age disparity: children represent less than 1% of public medical imaging datasets. This gap in representation leads to biased predictions across medical image foundation models, with the youngest patients facing the highest risk of misdiagnosis.

Kyphosis is a prevalent spinal condition where the spine curves in the sagittal plane, resulting in spine deformities. Curvature estimation provides a powerful index to assess the deformation severity of scoliosis. In current clinical diagnosis, the standard curvature estimation method for quantitatively assessing the curvature is performed by measuring the vertebral angle, which is the angle between two lines, drawn perpendicular to the upper and lower endplates of the involved vertebra. However, manual Cobb angle measurement requires considerable time and effort, along with associated problems such as interobserver and intraobserver variations. Hence, in this study, we propose UNet-based Attention Network for Thoracolumbar Vertebral Compression Fracture Angle (UANV), a vertebra angle measuring model using lateral spinal X-ray based on a deep convolutional neural network (CNN). Specifically, we considered the detailed shape of each vertebral body with an attention mechanism and then recorded each edge of each vertebra to calculate vertebrae angles.

The aim of this study was to automatically detect teeth with dens invaginatus (DI) in panoramic radiographs using deep learning algorithms and to compare the success of the algorithms. For this purpose, 400 panoramic radiographs with DI were collected from the faculty database and separated into 60% training, 20% validation and 20% test images. The training and validation images were labeled by oral, dental and maxillofacial radiologists and augmented with various augmentation methods, and the improved models were asked for the images allocated for the test phase and the results were evaluated according to performance measures including accuracy, sensitivity, F1 score and mean detection time. According to the test results, YOLOv8 achieved a precision, sensitivity and F1 score of 0.904 and was the fastest detection model with an average detection time of 0.041. The Faster R-CNN model achieved 0.912 precision, 0.904 sensitivity and 0.907 F1 score, with an average detection time of 0.1 s. The YOLOv9 algorithm showed the most successful performance with 0.946 precision, 0.930 sensitivity, 0.937 F1 score value and the average detection speed per image was 0.158 s. According to the results obtained, all models achieved over 90% success. YOLOv8 was relatively more successful in detection speed and YOLOv9 in other performance criteria. Faster R-CNN ranked second in all criteria.