AI in Clinical Medicine
Validated diagnostic AI, medical large language models, AI-discovered drug candidates in clinical trials and evidence standards for clinical deployment.
Most influential
- Deep Learning in Medical Image AnalysisReview4,943 citationsdoi:10.1146/annurev-bioeng-071516-0444…
- Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep LearningArticle4,828 citationsdoi:10.1016/j.cell.2018.02.010
- Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?Article2,426 citationsdoi:10.1109/tmi.2018.2837502
- A whole-slide foundation model for digital pathology from real-world data
Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles 1-3 . Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important slide-level context 4 . Here we present Prov-GigaPath, a whole-slide pathology foundation model pretraine…
Abstract excerpt by Xu H et al., CC-BY, https://doi.org/10.1038/s41586-024-07441-w; truncated.
Article578 citationsdoi:10.1038/s41586-024-07441-w - GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification
Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using large-scale annotated datasets. However, obtaining such datasets in the medical domain remains a challenge. In this paper, we present methods for generating synthetic medical images u…
Article1,950 citationsarXiv:1803.01229doi:10.1016/j.neucom.2018.09.013 - Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical i…
Abstract excerpt by Mohammad Hesam Hesamian et al., CC-BY, https://doi.org/10.1007/s10278-019-00227-x; truncated.
Review1,638 citationsdoi:10.1007/s10278-019-00227-x - Machine Learning for Medical ImagingReview1,672 citationsdoi:10.1148/rg.2017160130
- Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists
BACKGROUND: Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of …
Abstract excerpt by Pranav Rajpurkar et al., CC-BY, https://doi.org/10.1371/journal.pmed.1002686; truncated.
Article1,449 citationsdoi:10.1371/journal.pmed.1002686 - Recent advances and clinical applications of deep learning in medical image analysis
Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis. Despite the success, the further improvement of deep learning models in medical image analysis is majorly…
Review1,093 citationsarXiv:2105.13381doi:10.1016/j.media.2022.102444 - Medical Image Analysis using Convolutional Neural Networks: A Review
The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an effective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering has made medical image analysis one of the top research and developme…
Review1,431 citationsarXiv:1709.02250doi:10.1007/s10916-018-1088-1 - Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging
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 analy…
Abstract excerpt by Reabal Najjar, CC-BY, https://doi.org/10.3390/diagnostics13172760; truncated.
Review898 citationsdoi:10.3390/diagnostics13172760 - Overview of deep learning in medical imagingReview1,095 citationsdoi:10.1007/s12194-017-0406-5
- Deep convolutional neural network based medical image classification for disease diagnosis
Abstract Medical image classification plays an essential role in clinical treatment and teaching tasks. However, the traditional method has reached its ceiling on performance. Moreover, by using them, much time and effort need to be spent on extracting and selecting classification features. The deep neural network is an emerging machine learning method that …
Abstract excerpt by Samir S. Yadav et al., CC-BY, https://doi.org/10.1186/s40537-019-0276-2; truncated.
Article1,069 citationsdoi:10.1186/s40537-019-0276-2 - Generating Radiology Reports via Memory-driven Transformer
Medical imaging is frequently used in clinical practice and trials for diagnosis and treatment. Writing imaging reports is time-consuming and can be error-prone for inexperienced radiologists. Therefore, automatically generating radiology reports is highly desired to lighten the workload of radiologists and accordingly promote clinical automation, which is a…
Article693 citationsarXiv:2010.16056doi:10.18653/v1/2020.emnlp-main.112 - A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models
The use of self-supervised learning (SSL) to train pathology foundation models has increased substantially in the past few years. Notably, several models trained on large quantities of clinical data have been made publicly available in recent months. This will significantly enhance scientific research in computational pathology and help bridge the gap betwee…
Article126 citationsarXiv:2407.06508doi:10.1038/s41467-025-58796-1
Newest work
- Does the Citation Matter? Quality, Grounding, and Physician-Rubric Accuracy of Google's AI Overviews for Medical InformationPreprintarXiv:2610.39339
- What Makes Medical Examination Items Difficult? Using Large Language Models to Explore Patterns Associated With Empirical Item DifficultyPreprintarXiv:2610.39373
- Anatomical Prior-Guided Routing for 3D Lung Tumor SegmentationPreprintarXiv:2610.39414
- PathCowork: Learning from real-world pathology practice to build AI copilotsPreprintarXiv:2610.39441
- BRIDGE: Learning Brain-Gut Metabolic Evidence for Alzheimer's Disease DiagnosisPreprintarXiv:2610.39520
- Annotation-Free Representation Learning for Polyp Retrieval in Colonoscopy VideosPreprintarXiv:2610.39648
- Confidence Filtering in Video-Based Gait Assessment: Measurement Error versus Selection ShiftPreprintarXiv:2610.39704
- Aligning Medical Vision Language Models at the Capability Boundary with Entropy Adaptive Rubric GroundingPreprintarXiv:2610.39835
- DINOv2 for PET: A Pre-Training Domain Benchmark Across Classification, Segmentation, and PrognosisPreprintarXiv:2610.40018
- Segment Anything Model Based Context Guided Mamba Unet for Medical Image SegmentationPreprintarXiv:2610.40029