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Research papers on AI in medical diagnosis

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  2. A survey on Image Data Augmentation for Deep Learning

    Connor Shorten, Taghi M. Khoshgoftaar · 2019 · Journal Of Big Data · 13,003 citations

    Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augme

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  4. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

    Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, et al. · 2021 · Journal Of Big Data · 7,850 citations

    In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natur

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  5. Artificial intelligence in healthcare: past, present and future

    Fei Jiang, Yong Jiang, Hui Zhi, et al. · 2017 · Stroke and Vascular Neurology · 4,841 citations

    Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. W

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  6. The potential for artificial intelligence in healthcare

    Thomas H. Davenport, Ravi Kalakota · 2019 · Future Healthcare Journal · 3,814 citations

    The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.

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  7. A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

    Erico Tjoa, Cuntai Guan · 2020 · IEEE Transactions on Neural Networks and Learning Systems · 2,377 citations

    Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially with the advent of deep learning (DL). Along with research progress, they have encroached upon many different fields and disciplines. Some of them require high level of accountability and thus transparency, for example, the medical sector. Explanations for machine decisions and predictions are thus needed to justify their reliability. This requires greater interpretability, which often means we need to understand the mechanism underlying the algorithms. Unfortunately, the blackbox nature of the DL is still

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  8. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

    Bas H. M. van der Velden, Hugo J. Kuijf, Kenneth G. A. Gilhuijs, et al. · 2022 · Medical Image Analysis · 1,282 citations

    With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of explainable artificial intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.

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  9. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies

    Myura Nagendran, Yang Chen, Christopher A. Lovejoy, et al. · 2020 · BMJ · 1,131 citations

    OBJECTIVE: To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians. DESIGN: Systematic review. DATA SOURCES: Medline, Embase, Cochrane Central Register of Controlled Trials, and the World Health Organization trial registry from 2010 to June 2019. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Randomised trial registrations and non-randomised studies comparing the performance of a deep learning algorithm in medical imaging with a contemporary group of one or more expert clinicians. Medical imaging has seen a growing interest in deep learning r

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  10. RETRACTED ARTICLE: Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

    Yogesh Kumar, Apeksha Koul, Ruchi Singla, et al. · 2022 · Journal of Ambient Intelligence and Humanized Computing · 1,043 citations

    Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive su

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  11. Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging

    Reabal Najjar · 2023 · Diagnostics · 771 citations

    This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It traces the evolution of radiology, from the initial discovery of X-rays to the application of machine learning and deep learning in modern medical image analysis. The primary focus of this review is to shed light on AI applications in radiology, elucidating their seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, w

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  12. Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024

    Alessandro Carriero, Léon Groenhoff, Elizaveta Vologina, et al. · 2024 · Diagnostics · 151 citations

    The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, pr

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  13. Use of artificial intelligence and deep learning in fetal ultrasound imaging

    R. Ramirez Zegarra, T. Ghi · 2022 · Ultrasound in Obstetrics & Gynecology · 94 citations

    Deep learning is considered the leading artificial intelligence tool in image analysis in general. Deep‐learning algorithms excel at image recognition, which makes them valuable in medical imaging. Obstetric ultrasound has become the gold standard imaging modality for detection and diagnosis of fetal malformations. However, ultrasound relies heavily on the operator's experience, making it unreliable in inexperienced hands. Several studies have proposed the use of deep‐learning models as a tool to support sonographers, in an attempt to overcome these problems inherent to ultrasound. Deep learning has many clinical applications in the field of fetal imaging, including identification of normal

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  14. Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification

    Rawan Ghnemat, Sawsan Alodibat, Q. A. Al-Haija · 2023 · Journal of Imaging · 59 citations

    Recently, deep learning has gained significant attention as a noteworthy division of artificial intelligence (AI) due to its high accuracy and versatile applications. However, one of the major challenges of AI is the need for more interpretability, commonly referred to as the black-box problem. In this study, we introduce an explainable AI model for medical image classification to enhance the interpretability of the decision-making process. Our approach is based on segmenting the images to provide a better understanding of how the AI model arrives at its results. We evaluated our model on five datasets, including the COVID-19 and Pneumonia Chest X-ray dataset, Chest X-ray (COVID-19 and Pneum

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  15. Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis

    Li Yan, Qing Li, Kang Fu, et al. · 2025 · Bioengineering · 56 citations

    The integration of artificial intelligence (AI) into ultrasound medicine has revolutionized medical imaging, enhancing diagnostic accuracy and clinical workflows. This review focuses on the applications, challenges, and future directions of AI technologies, particularly machine learning (ML) and its subset, deep learning (DL), in ultrasound diagnostics. By leveraging advanced algorithms such as convolutional neural networks (CNNs), AI has significantly improved image acquisition, quality assessment, and objective disease diagnosis. AI-driven solutions now facilitate automated image analysis, intelligent diagnostic assistance, and medical education, enabling precise lesion detection across va

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  17. Artificial intelligence, machine learning and deep learning in musculoskeletal imaging: Current applications

    T. d’Angelo, Danilo Caudo, A. Blandino, et al. · 2022 · Journal of Clinical Ultrasound · 51 citations

    Artificial intelligence is rapidly expanding in all technological fields. The medical field, and especially diagnostic imaging, has been showing the highest developmental potential. Artificial intelligence aims at human intelligence simulation through the management of complex problems. This review describes the technical background of artificial intelligence, machine learning, and deep learning. The first section illustrates the general potential of artificial intelligence applications in the context of request management, data acquisition, image reconstruction, archiving, and communication systems. In the second section, the prospective of dedicated tools for segmentation, lesion detection

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  18. A Review of Deep Learning and Radiomics Approaches for Pancreatic Cancer Diagnosis from Medical Imaging

    Lanhong Yao, Zheyuan Zhang, Elif Keleş, et al. · 2023 · Current opinion in gastroenterology · 51 citations

    Purpose of review Early and accurate diagnosis of pancreatic cancer is crucial for improving patient outcomes, and artificial intelligence (AI) algorithms have the potential to play a vital role in computer-aided diagnosis of pancreatic cancer. In this review, we aim to provide the latest and relevant advances in AI, specifically deep learning (DL) and radiomics approaches, for pancreatic cancer diagnosis using cross-sectional imaging examinations such as computed tomography (CT) and magnetic resonance imaging (MRI). Recent findings This review highlights the recent developments in DL techniques applied to medical imaging, including convolutional neural networks (CNNs), transformer-based mod

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  20. Robust Medical Diagnosis: A Novel Two-Phase Deep Learning Framework for Adversarial Proof Disease Detection in Radiology Images

    S. B. U. Haque, Aasim Zafar · 2024 · Journal of Imaging Informatics in Medicine · 30 citations

    In the realm of medical diagnostics, the utilization of deep learning techniques, notably in the context of radiology images, has emerged as a transformative force. The significance of artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), lies in their capacity to rapidly and accurately diagnose diseases from radiology images. This capability has been particularly vital during the COVID-19 pandemic, where rapid and precise diagnosis played a pivotal role in managing the spread of the virus. DL models, trained on vast datasets of radiology images, have showcased remarkable proficiency in distinguishing between normal and COVID-19-affected cases, offering a r

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  21. A Review of Artificial Intelligence's Neural Networks (Deep Learning) Applications in Medical Diagnosis and Prediction

    G. R. Djavanshir, Xinrui Chen, Wenhao Yang · 2021 · IT Professional · 26 citations

    This paper reviews deep learning applications in medical diagnosis and prediction, such as Convolutional Neural Networks, Fully Convolutional Networks, and Generative Adversarial Networks in medical image analysis. It further summarizes the strength and weaknesses of deep learning in medical imaging and suggests that deep learning's great potential in medical fields.

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  22. Advancements in artificial intelligence for prostate cancer: Optimizing diagnosis, treatment, and prognostic assessment

    Yuki Arita, Christian Roest, Thomas C. Kwee, et al. · 2025 · Asian Journal of Urology · 24 citations

    Objective This review provides a comprehensive overview of the current research landscape on artificial intelligence (AI) in prostate cancer (PCa) management, highlighting its potential to enhance diagnosis, improve medical image quality, facilitate risk stratification, and aid prognosis. The review also identifies opportunities and challenges associated with integrating AI into clinical practice. Methods This review synthesizes findings from recent studies on AI applications in PCa management. It examines the use of machine learning and deep learning techniques in diagnostic imaging, surgical skill assessment, and outcome prediction. The analysis emphasizes empirical evidence demonstrating

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  23. Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture

    Junaid Aftab, Muhammad Attique Khan, Sobia Arshad, et al. · 2025 · Scientific Reports · 23 citations

    Classifying medical images is essential in computer-aided diagnosis (CAD). Although the recent success of deep learning in the classification tasks has proven advantages over the traditional feature extraction techniques, it remains challenging due to the inter and intra-class similarity caused by the diversity of imaging modalities (i.e., dermoscopy, mammography, wireless capsule endoscopy, and CT). In this work, we proposed a novel deep-learning framework for classifying several medical imaging modalities. In the training phase of the deep learning models, data augmentation is performed at the first stage on all selected datasets. After that, two novel custom deep learning architectures we

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