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Research papers on AI in medical diagnosis

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  2. Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

    Mohammad Behdad Jamshidi, Ali Lalbakhsh, Jakub Talla, et al. · 2020 · IEEE Access · 550 citations

    COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated b

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  3. Deep Learning for Medical Image-Based Cancer Diagnosis

    Xiaoyan Jiang, Zuojin Hu, Shuihua Wang‎, et al. · 2023 · Cancers · 346 citations

    (1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET),

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  4. Artificial intelligence in medical imaging of the liver

    Liqiang Zhou, Jia‐Yu Wang, Song-Yuan Yu, et al. · 2019 · World Journal of Gastroenterology · 262 citations

    Artificial intelligence (AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians' workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep

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

    Ruben Ramirez Zegarra, Tullio Ghi · 2022 · Ultrasound in Obstetrics and Gynecology · 99 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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  6. Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification

    Rawan Ghnemat, Sawsan Alodibat, Qasem Abu Al-Haija · 2023 · Journal of Imaging · 67 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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  8. Artificial Intelligence in Medical Imaging: Applications of Deep Learning for Disease Detection and Diagnosis

    Abhinav Deshmukh · 2024 · Universal Research Reports · 8 citations

    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, personali

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  13. Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Systematic Review of Deep Learning Techniques for Medical Diagnosis

    Rivan Heriawan, Nicolas Vallemorin Cahyadi, Daniel Kartawiguna · 2025 · International Conference on Information Management and Technology (ICIMTech)

    Deep learning has advanced the development of technology in medical imaging, particularly in regards to the analysis of MRI scans. This area of Artificial Intelligence (AI) has opportunities to enhance the precision and speed of diagnosis by automating the recognition and segmentation processes in image analysis. Convolutional Neural Networks (CNN) is one example of a vast array of advanced deep learning models developed recently, which, over the past few years, have been able to accurately classify diseases. Nevertheless, the use of deep learning approaches on MRI imaging is known to face numerous challenges. Perhaps the most crucial challenge is the absence of reliable medical datasets. MR

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  18. Artificial Intelligence and Deep Learning for Early Detection and Diagnosis of Brain Tumors in Neurosurgery Using CT Scan and Medical Imaging (Preprint)

    Negin Mehdinejad, Cyrus Asadzadeh, Amir Salehi Farid, et al. · 2026

    UNSTRUCTURED Background: Lung cancer remains a leading cause of mortality worldwide, with early detection significantly improving survival rates. Computed tomography (CT) scans are a primary imaging modality for identifying lung tumors, but manual interpretation can be time-consuming and prone to errors. Deep learning models offer promise for automating and enhancing detection accuracy. Methods: We developed a hybrid deep learning framework combining convolutional neural networks (CNNs) for classification and object detection models like YOLOv8 for localization. The model was trained on publicly available datasets, including the Lung-PET-CT-Dx and Kaggle Chest CT-Scan images, comprising over

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  19. Deep Learning-Based AI Framework for Automated Medical Diagnosis

    Vishal Khanna · 2026 · i-manager's Journal on Artificial Intelligence & Machine Learning

    The rapid adoption of artificial intelligence (AI) in healthcare has opened new avenues for automated medical diagnosis, offering the potential to significantly improve clinical decision-making, diagnostic accuracy, and early disease detection. With the increasing availability of digital health records, medical imaging, and laboratory data, AI-driven systems are being explored as effective tools to support clinicians in managing complex and large-scale medical information. Traditional diagnostic processes often rely on manual interpretation and expert judgment, which can be time-consuming, subject to human error, and limited by inter-observer variability. In this context, deep learning techn

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  20. Deep Learning for Medical Imaging Analysis

    Gayatri Gupta, Aafila Shrivastava · 2026 · Journal of Artificial Intelligence and Information

    This paper presents a comprehensive analysis of deep learning applications in medical imaging analysis. We examine the evolution of medical image processing from traditional computer vision approaches to sophisticated deep learning solutions, highlighting the critical role of artificial intelligence in modern healthcare diagnostics. Our research encompasses multiple dimensions of medical imaging analysis, including disease detection, segmentation, classification, and automated diagnosis across various imaging modalities. Through extensive evaluation across diverse medical imaging datasets, we demonstrate significant improvements in diagnostic accuracy and efficiency. Our findings show that d

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  22. Artificial Intelligence in Oncologic Imaging: Deep Learning, Radiomics, and Clinical Integration for Precision Cancer Diagnosis

    Ohmini Krishnamurthy Rajendran · 2026 · International Journal of Drug Delivery Technology

    Radiological oncology is using AI to analyse complicated images, analyse tumours, and make treatment decisions. This study focuses on deep learning, radiomics, and radiogenomics frameworks for AI applications in breast, lung, prostate, brain, gastrointestinal, and metastatic cancers. Outperforming expert radiologists, these tools have improved lesion identification, segmentation, risk prediction, and molecular phenotype inference. In addition, the review discusses how AI influences workflow triage, report standardization, and radiologist efficiency in digital mammography, MRI, PET/CT, and CT. Despite recent advancements, the need for AI outputs that can be explained in therapeutic contexts,

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