Clinician-Centric Explainable Artificial Intelligence Framework for Medical Imaging Diagnostics: A Systematic Review.

The rapid advancements in medical imaging technologies have significantly enhanced diagnostic accuracy and clinical decision-making in modern healthcare. Image segmentation and deep learning have emerged as transformative tools among these advancements. This article explores the pivotal role of image segmentation and deep learning in medical imaging, detailing their methodologies, applications, challenges, and future directions. Deep learning, particularly Convolutional Neural Networks (CNNs), has revolutionized medical imaging by automating the analysis of complex datasets and improving diagnostic precision. Image segmentation, a fundamental component of medical imaging, allows for delineating specific structures such as organs, tissues, and pathological regions. Together, these technologies have been applied in diverse fields, including oncology, cardiology, neurology, and ophthalmology, enabling applications such as tumor detection, organ segmentation, disease progression monitoring, and treatment planning. However, despite its transformative potential, the integration of deep learning into medical imaging faces several challenges. These include data scarcity, privacy concerns, interpretability issues, and regulatory hurdles. The article discusses various strategies to address these challenges, such as data augmentation, transfer learning, and the development of explainable AI models to ensure transparency and trustworthiness. Evaluation metrics, such as accuracy, sensitivity, specificity, and Dice Similarity Coefficient (DSC), are essential for assessing model performance. Rigorous clinical validation and regulatory approval are crucial to integrating deep learning systems into clinical workflows effectively. Looking ahead, the future of deep learning in medical imaging holds immense promise. Innovations like multimodal imaging, personalized medicine, and AI-driven automation are set to further revolutionize the field, enhancing the efficiency and accuracy of diagnostics. Collaborative efforts between clinicians, researchers, and AI developers will play a vital role in overcoming current limitations and driving progress. This article concludes by emphasizing the transformative potential of deep learning and image segmentation in medical imaging, highlighting their ability to improve diagnostic accuracy, streamline clinical workflows, and ultimately, enhance patient care. By addressing current challenges and continuing to innovate, these technologies are poised to redefine the landscape of medical diagnostics and treatment in the years to come.

Focus issue: Artificial intelligence in medical physics.

The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.

For a considerable amount of time, medical image processing has been an important topic of research in the field of medicine. The advent of deep learning technology has resulted in the development of revolutionary improvements in this particular sector. Because of its remarkable effectiveness in training enormous amounts of data and performing difficult tasks, deep learning has attracted a lot of attention. It has found extensive application in the analysis, diagnosis, and treatment of medical pictures. This work employs the literature review approach to examine and analyze the existing research conducted by academics on deep learning and medical imaging. It also provides a summary of the utilization of deep learning in medical imaging images and highlights the current development trend in this field.

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a lesser number of training cases than did CNNs. "Deep learning", or ML with image input, in medical imaging is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical imaging in the next few decades.

The integration of artificial intelligence (AI) and deep learning techniques into medical imaging has revolutionized disease detection and diagnosis. This paper provides a comprehensive overview of the applications of deep learning in medical imaging and its impact on healthcare. The paper begins with an introduction to the fundamentals of deep learning, emphasizing convolutional neural networks (CNNs) and their relevance in analyzing medical images. It then explores various applications of deep learning in medical imaging, including automated disease detection and classification, image segmentation for precise anatomical localization, quantitative analysis for predictive modeling, personalized medicine, and workflow optimization. Case studies and examples from different medical specialties, such as oncology, cardiology, and neurology, are presented to illustrate the practical implementation and effectiveness of AI-driven approaches.

Along with the development of medical imaging technology, medical image analysis has become a crucial part of clinical diagnosis. However, traditional image analysis methods rely on manual feature extraction and expert evaluation, triggering problems such as low efficiency and lack of accuracy. Deep learning techniques, especially convolutional neural networks (CNNs), have made significant breakthroughs in medical image analysis in recent years. The aim of this paper is to study the application of deep learning in medical imaging, and explore the advantages and development potential in early disease diagnosis, image segmentation and feature extraction. This paper reviews the basic principles of deep learning and analyses the applicability of deep learning in medical imaging by combining the characteristics of medical imaging. Through systematic analysis of existing literature, this paper summarises the remarkable achievements of deep learning in processing, analysing and applying it to various medical images, such as Optical Coherence Tomography (OCT), Magnetic Resonance Imaging (MRI) and so on. It has been shown that deep learning methods can significantly improve the accuracy and efficiency of medical image processing and demonstrate performance due to traditional methods in tasks such as classification. However, how to improve the interpretability of models and the generalisability of clinical applications are still current research hotspots and challenges.

Artificial Intelligence (AI) is commonly defined as machine intelligence that is programmed to think like humans and emulate their behavior or simply the technology of developing AI. People receive great benefits from services based on AI technology without even realizing it. One of the technologies that underlie AI is machine learning, and deep learning is a new type of machine learning. Mathematical models of deep learning usually employ a neural network model that imitates the human brain neural network, and a deep neural network has some middle layers to solve the highly difficult issues. A Convolutional Neural Network (CNN), which is a type of deep learning network, can analyze an image directly. In the field of medical imaging and diagnosis, deep learning is already being used to solve numerous difficult issues. Systems have been developed using CNNs and have achieved practical medical application. This chapter focuses in detail on the relationship between deep learning, medical imaging, and hygiene. In particular, it contains an explanation and technical summary of machine learning, the application of deep learning in medical imaging, and the association of the Coronavirus disease 2019 (COVID-19) infection with hygiene and deep learning.

With the increasing number of medical images and the continuous progress of artificial intelligence technology, the traditional medical image analysis methods relying on artificial features and experts' experience have been difficult to meet the dual requirements of efficiency and accuracy in modern clinics. In recent years, deep learning has shown strong application potential in the field of medical imaging, and has gradually become an important tool for improving image processing efficiency and diagnosis level. In this paper, from the three target directions of image segmentation, image registration and image enhancement, we systematically sort out the research progress and application of deep learning in medical image analysis, discuss its practical value in clinical practice with the current research cases, summarise the main challenges faced by the current technology, and look forward to the future development direction of deep learning in medical imaging. By collating and analysing the existing research results, it can provide useful references and ideas for the future development of intelligent analysis of medical images.

A descriptive framework for the field of deep learning applications in medical images

In the rapidly evolving field of medical image analysis utilizing artificial intelligence (AI), the selection of appropriate computational models is critical for accurate diagnosis and patient care. This literature review provides a comprehensive comparison of vision transformers (ViTs) and convolutional neural networks (CNNs), the two leading techniques in the field of deep learning in medical imaging. We conducted a survey systematically. Particular attention was given to the robustness, computational efficiency, scalability, and accuracy of these models in handling complex medical datasets. The review incorporates findings from 36 studies and indicates a collective trend that transformer-based models, particularly ViTs, exhibit significant potential in diverse medical imaging tasks, showcasing superior performance when contrasted with conventional CNN models. Additionally, it is evident that pre-training is important for transformer applications. We expect this work to help researchers and practitioners select the most appropriate model for specific medical image analysis tasks, accounting for the current state of the art and future trends in the field.

Deep learning plays a significant role in transforming medical imaging for disease diagnosis. It uses advanced algorithms, especially Convolutional Neural Networks (CNNs), to automatically learn and extract important features from medical images. This technology helps in detecting, classifying, and diagnosing various diseases, such as different types of cancer, brain disorders like aneurysms and strokes, heart diseases, and respiratory conditions. Deep learning improves the accuracy and efficiency of diagnostic workflows and reduces the workload for healthcare professionals. Despite its many advantages, deep learning faces challenges related to data availability, model interpretability, and clinical validation. This review highlights the current applications, performance evaluation methods, and challenges of deep learning in medical imaging for disease diagnosis.

With the continuous innovation and development of artificial intelligence, the theoretical research on and application of deep learning, one of its branches, has also reached a certain height, and has become a research hotspot in all walks of life. In the medical field, traditional manual image reading and other medical image analysis methods have been unable to adapt to the sharp increase in the amount of impact data. Based on this, the combination of deep learning and medical imaging has eased this pressure. This article first briefly analyzes the relevant theories of deep learning, and focuses on its applications in medical image classification and recognition, medical image segmentation, and computer-aided diagnosis. Finally, the application of deep learning in medical images is prospected.

Artificial or augmented intelligence, machine learning, and deep learning will be an increasingly important part of clinical practice for the next generation of radiologists. It is therefore critical that radiology residents develop a practical understanding of deep learning in medical imaging. Certain aspects of deep learning are not intuitive and may be better understood through hands-on experience; however, the technical requirements for setting up a programming and computing environment for deep learning can pose a high barrier to entry for individuals with limited experience in computer programming and limited access to GPU-accelerated computing. To address these concerns, we implemented an introductory module for deep learning in medical imaging within a self-contained, web-hosted development environment. Our initial experience established the feasibility of guiding radiology trainees through the module within a 45-min period typical of educational conferences.

Big data are information assets characterized by high volume, velocity, variety, and veracity. Large, multilevel, and integrated datasets offer the promise of unlocking novel insights and accelerating breakthroughs. According to an increasing interest in artificial intelligence around the world, deep learning has attracted a great deal of public attention. Deep learning techniques increase learning capacity and provide a decision support system at scales that are transforming the future of health care. Every day, deep learning algorithms are used broadly across different industries. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from intricate data. This chapter explains what deep learning is and why it is so important. We provide several examples where deep learning techniques find applications and what kind of problems can be solved using deep learning in medical imaging. This chapter also introduces a particular type of deep learning algorithm, including autoencoders, restricted Boltzmann machines, deep belief network, recurrent neural networks, and convolutional neural networks with practical variant case studies, which are at the basis of deep learning. KeywordsArtificial intelligenceDeep learning techniquesDecision support systemMedical imaging