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

    Li Yan, Qing Li, Kang Fu, et al. · 2025 · Bioengineering · 64 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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  9. A Review of Deep Learning and Radiomics Approaches for Pancreatic Cancer Diagnosis from Medical Imaging

    Lanhong Yao, Zhe-Yuan Zhang, Elif Keleş, et al. · 2023 · Current opinion in gastroenterology · 52 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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  10. 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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  12. 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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  13. A Review of Artificial Intelligence's Neural Networks (Deep Learning) Applications in Medical Diagnosis and Prediction

    G. R. Djavanshir, Xin-Rui Chen, Wen-Hao Yang · 2021 · IT Professional · 27 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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  14. 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 · 27 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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  15. 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 · 25 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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  17. Artificial intelligence in the diagnosis of uveal melanoma: advances and applications

    Albert K. Dadzie, Sabrina P. Iddir, Sanjay Ganesh, et al. · 2025 · Experimental Biology and Medicine · 18 citations

    Advancements in machine learning and deep learning have the potential to revolutionize the diagnosis of melanocytic choroidal tumors, including uveal melanoma, a potentially life-threatening eye cancer. Traditional machine learning methods rely heavily on manually selected image features, which can limit diagnostic accuracy and lead to variability in results. In contrast, deep learning models, particularly convolutional neural networks (CNNs), are capable of automatically analyzing medical images, identifying complex patterns, and enhancing diagnostic precision. This review evaluates recent studies that apply machine learning and deep learning approaches to classify uveal melanoma using imag

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  18. Artificial Intelligence in Primary Malignant Bone Tumor Imaging: A Narrative Review

    Platon S. Papageorgiou, Rafail C. Christodoulou, Panagiotis Korfiatis, et al. · 2025 · Diagnostics · 18 citations

    Artificial Intelligence (AI) has emerged as a transformative force in orthopedic oncology, offering significant advances in the diagnosis, classification, and prediction of treatment response for primary malignant bone tumors (PBT). Through machine learning and deep learning techniques, AI leverages computational algorithms and large datasets to enhance medical imaging interpretation and support clinical decision-making. The integration of radiomics with AI enables the extraction of quantitative features from medical images, allowing for precise tumor characterization and the development of personalized therapeutic strategies. Notably, convolutional neural networks have demonstrated exceptio

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  19. A Systematic Review of Artificial Intelligence's Function in the Diagnosis of Lung Cancer (2018–2024)

    R. H. Hamad · 2025 · Mesopotamian Journal of Artificial Intelligence in Healthcare · 16 citations

    Lung cancer is a leading cause of cancer-related mortality, often diagnosed at advanced stages. This systematic review explores AI applications in lung cancer diagnosis, focusing on medical imaging, pathology, and genetic analysis. A systematic literature review methodology was employed, analyzing studies from databases such as PubMed, IEEE Xplore, and Scopus (2018–2024). Findings indicate that AI-powered diagnostic models, particularly deep learning techniques, outperform conventional methods in accuracy, sensitivity, and early detection capabilities. However, integration into clinical practice presents challenges, including data privacy concerns, model biases, and regulatory limitations. T

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  20. Artificial Intelligence and Deep Learning in Revolutionizing Brain Tumor Diagnosis and Treatment: A Narrative Review

    Shobha Mandal, S. Chakraborty, M. Tariq, et al. · 2024 · Cureus · 12 citations

    The emergence of artificial intelligence (AI) in the medical field holds promise in improving medical management, particularly in personalized strategies for the diagnosis and treatment of brain tumors. However, integrating AI into clinical practice has proven to be a challenge. Deep learning (DL) is very convenient for extracting relevant information from large amounts of data that has increased in medical history and imaging records, which shortens diagnosis time, that would otherwise overwhelm manual methods. In addition, DL aids in automated tumor segmentation, classification, and diagnosis. DL models such as the Brain Tumor Classification Model and the Inception-Resnet V2, or hybrid tec

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  21. Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications

    Sami Akbulut, Z. Kucukakcali, Cemil Çolak · 2025 · World Journal of Gastroenterology · 12 citations

    Acute appendicitis (AAp) remains one of the most common abdominal emergencies, requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries. Conventional diagnostic methods, including medical history, clinical assessment, biochemical markers, and imaging techniques, often present limitations in sensitivity and specificity, especially in atypical cases. In recent years, artificial intelligence (AI) has demonstrated remarkable potential in enhancing diagnostic accuracy through machine learning (ML) and deep learning (DL) models. This review evaluates the current applications of AI in both adult and pediatric AAp, focusing on clinical data-based models, radiological

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  22. Artificial Intelligence, Machine Learning, and Deep Learning in the Diagnosis and Management of Hepatocellular Carcinoma

    C. Larrain, Alejandro Torres-Hernandez, D. B. Hewitt · 2024 · Livers · 11 citations

    Artificial Intelligence (AI) can be a useful tool in the management of disease processes such as hepatocellular carcinoma (HCC) as treatment decisions are often complex and multifaceted. AI applications in medicine are expanding with the ongoing advances in AI including more sophisticated machine learning and deep learning processes. In preliminary studies, AI algorithms have demonstrated superiority in predicting the development of HCC compared with standard models. Radiomics, a quantitative method used to extract features from medical imaging, has been applied to numerous liver imaging modalities to aid in the diagnosis and prognostication of HCC. Deep learning methodologies can help us to

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  23. Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning

    T. Yi̇ği̇t, Nilgün Şengöz, Ö. Özmen, et al. · 2022 · ArXiv · 10 citations

    Artificial intelligence holds great promise in medical imaging, especially histopathological imaging. However, artificial intelligence algorithms cannot fully explain the thought processes during decision-making. This situation has brought the problem of explainability, i.e., the black box problem, of artificial intelligence applications to the agenda: an algorithm simply responds without stating the reasons for the given images. To overcome the problem and improve the explainability, explainable artificial intelligence (XAI) has come to the fore, and piqued the interest of many researchers. Against this backdrop, this study examines a new and original dataset using the deep learning algorit

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  24. Improving Tuberculosis Diagnosis using Explainable Artificial Intelligence in Medical Imaging

    Cem Özkurt · 2024 · Journal of Mathematical Sciences and Modelling · 10 citations

    The integration of artificial intelligence (AI) applications in the healthcare sector is ushering in a significant transformation, particularly in developing more effective strategies for early diagnosis and treatment of contagious diseases like tuberculosis. Tuberculosis, a global public health challenge, demands swift interventions to prevent its spread. While deep learning and image processing techniques show potential in extracting meaningful insights from complex radiological images, their accuracy is often scrutinized due to a lack of explainability. This research navigates the intersection of AI and tuberculosis diagnosis by focusing on explainable artificial intelligence (XAI)

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