Fairness concerns stemming from known and unknown biases in healthcare practices have raised questions about the trustworthiness of Artificial Intelligence (AI)-driven Clinical Decision Support Systems (CDSS). Studies have shown unforeseen performance disparities in subpopulations when applied to clinical settings different from training. Existing unfairness mitigation strategies often struggle with scalability and accessibility, while their pursuit of group-level prediction performance parity does not effectively translate into fairness at the point of care. This study introduces FairICP, a flexible and cost-effective post-implementation framework based on Inductive Conformal Prediction (ICP), to provide users with actionable knowledge of model uncertainty due to subpopulation level biases at the point of care. FairICP applies ICP to identify the model's scope of competence through group specific calibration, ensuring equitable prediction reliability by filtering predictions that fall within the trusted competence boundaries. We evaluated FairICP against four benchmarks on three medical imaging modalities: (1) Cardiac Magnetic Resonance Imaging (MRI), (2) Chest X-ray and (3) Dermatology Imaging, acquired from both private and large public datasets. Frameworks are assessed on prediction performance enhancement and unfairness mitigation capabilities. Compared to the baseline, FairICP improved prediction accuracy by 7.2% and reduced the accuracy gap between the privileged and unprivileged subpopulations by 2.2% on average across all three datasets. Our work provides a robust solution to promote trust and transparency in AI-CDSS, fostering equality and equity in healthcare for diverse patient populations. Such post-process methods are critical to enabling a robust framework for AI-CDSS implementation and monitoring for healthcare settings.

We explore biases present in publicly available fetal ultrasound (US) imaging datasets, currently at the disposal of researchers to train deep learning (DL) algorithms for prenatal diagnostics. As DL increasingly permeates the field of medical imaging, the urgency to critically evaluate the fairness of benchmark public datasets used to train them grows. Our thorough investigation reveals a multifaceted bias problem, encompassing issues such as lack of demographic representativeness, limited diversity in clinical conditions depicted, and variability in US technology used across datasets. We argue that these biases may significantly influence DL model performance, which may lead to inequities in healthcare outcomes. To address these challenges, we recommend a multilayered approach. This includes promoting practices that ensure data inclusivity, such as diversifying data sources and populations, and refining model strategies to better account for population variances. These steps will enhance the trustworthiness of DL algorithms in fetal US analysis.

Artificial intelligence (AI) enhances diagnostic accuracy, efficiency, and patient outcomes in radiology. Patient acceptance is essential for successful integration. This study examines patient perspectives on AI in radiology within the UAE, focusing on their knowledge, attitudes, and perceived barriers. Understanding these factors can address concerns, improve trust, and guide patient-centered AI implementation. The findings aim to support effective AI adoption in healthcare. A cross-sectional study involving 205 participants undergoing radiological imaging in the UAE. Data was collected through an online questionnaire, developed based on a literature review, and pre-tested for reliability and validity. Non-probability sampling methods, including convenience and snowball sampling, were employed. The questionnaire assessed participants' knowledge, attitudes, and perceived barriers regarding AI in radiology. Data was analyzed, and categorical variables were expressed as frequencies and percentages. Most participants (89.8 %) believed AI could improve diagnostic accuracy, and 87.8 % acknowledged its role in prioritizing urgent cases. However, only 22 % had direct experience with AI in radiology. While 81 % expressed comfort with AI-based technology, concerns about data security (80.5 %), lack of empathy in AI systems (82.9 %), and insufficient information about AI (85.8 %) were significant barriers. Additionally, (87.3 %) of participants were concerned about the cost of AI implementation. Despite these concerns, 86.3 % believed AI could improve the quality of radiological services, and 83.9 % were satisfied with its potential applications. UAE patients generally support AI in radiology, recognizing its potential for improved diagnostic accuracy. However, concerns about data security, empathy, and understanding of AI technologies necessitate improved patient education, transparent communication, and regulatory frameworks to foster trust and acceptance.

Machine learning drives osteoporosis detection and screening with higher clinical accuracy and accessibility than traditional osteoporosis screening tools. This review takes a step-by-step view of machine learning for osteoporosis detection, providing insights into today's osteoporosis detection and the outlook for the future. The early diagnosis and risk detection of osteoporosis have always been crucial and challenging issues in the medical field. With the in-depth application of artificial intelligence technology, especially machine learning technology in the medical field, significant breakthroughs have been made in the application of early diagnosis and risk detection of osteoporosis. Machine learning is a multidimensional technical system that encompasses a wide variety of algorithm types. Machine learning algorithms have become relatively mature and developed over many years in medical data processing. They possess stable and accurate detection performance, laying a solid foundation for the detection and diagnosis of osteoporosis. As an essential part of the machine learning technical system, deep-learning algorithms are complex algorithm models based on artificial neural networks. Due to their robust image recognition and feature extraction capabilities, deep learning algorithms have become increasingly mature in the early diagnosis and risk assessment of osteoporosis in recent years, opening new ideas and approaches for the early and accurate diagnosis and risk detection of osteoporosis. This paper reviewed the latest research over the past decade, ranging from relatively basic and widely adopted machine learning algorithms combined with clinical data to more advanced deep learning techniques integrated with imaging data such as X-ray, CT, and MRI. By analyzing the application of algorithms at different stages, we found that these basic machine learning algorithms performed well when dealing with single structured data but encountered limitations when handling high-dimensional and unstructured imaging data. On the other hand, deep learning can significantly improve detection accuracy. It does this by automatically extracting image features, especially in image histological analysis. However, it faces challenges. These include the "black-box" problem, heavy reliance on large amounts of labeled data, and difficulties in clinical interpretability. These issues highlighted the importance of model interpretability in future machine learning research. Finally, we expect to develop a predictive model in the future that combines multimodal data (such as clinical indicators, blood biochemical indicators, imaging data, and genetic data) integrated with electronic health records and machine learning techniques. This model aims to present a skeletal health monitoring system that is highly accessible, personalized, convenient, and efficient, furthering the early detection and prevention of osteoporosis.

The exponential rise in the artificial intelligence (AI) tools for medical imaging is profoundly impacting the practice of radiology. With over 1000 FDA-cleared AI algorithms now approved for clinical use-many of them designed for radiologic tasks-the responsibility lies with training institutions to ensure that radiology residents are equipped not only to use AI systems, but to critically evaluate, monitor, respond to their output in a safe, ethical manner. This review proposes a comprehensive framework to integrate AI into radiology residency curricula, targeting both essential competencies required of all residents, optional advanced skills for those interested in research or AI development. Core educational strategies include structured didactic instruction, hands-on lab exposure to commercial AI tools, case-based discussions, simulation-based clinical pathways, teaching residents how to interpret model cards, regulatory documentation. Clinical examples such as stroke triage, Urinary tract calculi detection, AI-CAD in mammography, false-positive detection are used to anchor theory in practice. The article also addresses critical domains of AI governance: model transparency, ethical dilemmas, algorithmic bias, the role of residents in human-in-the-loop oversight systems. It outlines mentorship, faculty development strategies to build institutional readiness, proposes a roadmap to future-proof radiology education. This includes exposure to foundation models, vision-language systems, multi-agent workflows, global best practices in post-deployment AI monitoring. This pragmatic framework aims to serve as a guide for residency programs adapting to the next era of radiology practice.

Over the last several decades, echocardiography has made numerous technological advancements, with one of the most significant being the integration of artificial intelligence (AI). AI algorithms assist novice operators to acquire diagnostic-quality images and automate complex analyses. This review explores the integration of AI into various echocardiographic modalities, including transthoracic, transesophageal, intracardiac, and point-of-care ultrasound. It examines how AI enhances image acquisition, streamlines analysis, and improves diagnostic performance across routine, critical care, and complex cardiac imaging. To conduct this review, PubMed was searched using targeted keywords aligned with each section of the paper, focusing primarily on peer-reviewed articles published from 2020 onward. Earlier studies were included when foundational or frequently cited. The findings were organized thematically to highlight clinical relevance and practical applications. Challenges persist in clinical application, including algorithmic bias, ethical concerns, and the need for clinician training and AI oversight. Despite these, AI's potential to revolutionize cardiovascular care through precision and accessibility remains unparalleled, with benefits likely to far outweigh obstacles if appropriately applied and implemented in cardiac ultrasonography.

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.

Deep learning models have shown remarkable success in dermatological image analysis, offering potential for automated skin disease diagnosis. Previously, convolutional neural network(CNN) based architectures have achieved immense popularity and success in computer vision (CV) based task like skin image recognition, generation and video analysis. But with the emergence of transformer based models, CV tasks are now are nowadays carrying out using these models. Vision Transformers (ViTs) is such a transformer-based models that have shown success in computer vision. It uses self-attention mechanisms to achieve state-of-the-art performance across various tasks. However, their reliance on global attention mechanisms makes them susceptible to adversarial perturbations. This paper aims to investigate the susceptibility of ViTs for medical images to adversarial watermarking-a method that adds so-called imperceptible perturbations in order to fool models. By generating adversarial watermarks through Projected Gradient Descent (PGD), we examine the transferability of such attacks to CNNs and analyze the performance defense mechanism -- adversarial training. Results indicate that while performance is not compromised for clean images, ViTs certainly become much more vulnerable to adversarial attacks: an accuracy drop of as low as 27.6%. Nevertheless, adversarial training raises it up to 90.0%.

Over the past decade, Medical Image Segmentation (MIS) using Deep Neural Networks (DNNs) has achieved significant performance improvements and holds great promise for future developments. This paper presents a comprehensive study on MIS based on DNNs. Intelligent Vision Systems are often evaluated based on their output levels, such as Data, Information, Knowledge, Intelligence, and Wisdom (DIKIW),and the state-of-the-art solutions in MIS at these levels are the focus of research. Additionally, Explainable Artificial Intelligence (XAI) has become an important research direction, as it aims to uncover the "black box" nature of previous DNN architectures to meet the requirements of transparency and ethics. The study emphasizes the importance of MIS in disease diagnosis and early detection, particularly for increasing the survival rate of cancer patients through timely diagnosis. XAI and early prediction are considered two important steps in the journey from "intelligence" to "wisdom." Additionally, the paper addresses existing challenges and proposes potential solutions to enhance the efficiency of implementing DNN-based MIS.

Disparity among gender and ethnicity remains an issue across medicine and health science. Only 26%-35% of trainee radiologists are female, despite more than 50% of medical students' being female. Similar gender disparities are evident across the medical imaging professions. Generative artificial intelligence text-to-image production could reinforce or amplify gender biases. <b>Methods:</b> In March 2024, DALL-E 3 was utilized via GPT-4 to generate a series of individual and group images of medical imaging professionals: radiologist, nuclear medicine physician, radiographer, nuclear medicine technologist, medical physicist, radiopharmacist, and medical imaging nurse. Multiple iterations of images were generated using a variety of prompts. Collectively, 120 images were produced for evaluation of 524 characters. All images were independently analyzed by 3 expert reviewers from medical imaging professions for apparent gender and skin tone. <b>Results:</b> Collectively (individual and group images), 57.4% (<i>n</i> = 301) of medical imaging professionals were depicted as male, 42.4% (<i>n</i> = 222) as female, and 91.2% (<i>n</i> = 478) as having a light skin tone. The male gender representation was 65% for radiologists, 62% for nuclear medicine physicians, 52% for radiographers, 56% for nuclear medicine technologists, 62% for medical physicists, 53% for radiopharmacists, and 26% for medical imaging nurses. For all professions, this overrepresents men compared with women. There was no representation of persons with a disability. <b>Conclusion:</b> This evaluation reveals a significant overrepresentation of the male gender associated with generative artificial intelligence text-to-image production using DALL-E 3 across the medical imaging professions. Generated images have a disproportionately high representation of white men, which is not representative of the diversity of the medical imaging professions.