# AI in Clinical Medicine Domain: Advanced Medicine · 279 papers indexed · newest 2026-10-29 Validated diagnostic AI, medical large language models, AI-discovered drug candidates in clinical trials and evidence standards for clinical deployment. Key concepts: medical imaging, clinical, medical large language model, radiology, pathology foundation model ## Most influential - **[Deep Learning in Medical Image Analysis](https://space-kitty.com/papers/doi:10.1146/annurev-bioeng-071516-044442)**. Dinggang G. Shen, Guorong Wu A, Heung‐Il Suk · 2017 · Annual Review of Biomedical Engineering (doi:10.1146/annurev-bioeng-071516-044442 · 4,943 citations). Source: - **[Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning](https://space-kitty.com/papers/doi:10.1016/j.cell.2018.02.010)**. Daniel Kermany, Michael H. Goldbaum, Wenjia Cai et al. · 2018 · Cell (doi:10.1016/j.cell.2018.02.010 · 4,828 citations). Source: - **[Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?](https://space-kitty.com/papers/doi:10.1109/tmi.2018.2837502)**. Olivier Bernard, Alain Lalande, Clément Zotti et al. · 2018 · IEEE Transactions on Medical Imaging (doi:10.1109/tmi.2018.2837502 · 2,426 citations). Source: - **[A whole-slide foundation model for digital pathology from real-world data](https://space-kitty.com/papers/doi:10.1038/s41586-024-07441-w)**. Xu H, Usuyama N, Bagga J et al. · 2024 · Nature (doi:10.1038/s41586-024-07441-w · 578 citations). Source: > 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 pretrained on 1.3 billion 256 × 256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres. The slides originated from more than 30,000 patients covering 31 major tissue types. To pretra… > _Abstract excerpt by Xu H et al., CC-BY, https://doi.org/10.1038/s41586-024-07441-w; truncated._ - **[GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification](https://space-kitty.com/papers/arxiv:1803.01229)**. Maayan Frid-Adar, Idit Diamant, Eyal Klang et al. · 2018 · Neurocomputing (arXiv:1803.01229 · doi:10.1016/j.neucom.2018.09.013 · 1,950 citations). Source: > 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 using recently presented deep learning Generative Adversarial Networks (GANs). Furthermore, we show that generated medical images can be used for synthetic data augmentation, and improve the performance of CNN for medical image classificatio… - **[Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges](https://space-kitty.com/papers/doi:10.1007/s10278-019-00227-x)**. Mohammad Hesam Hesamian, Wenjing Jia, Xiangjian He et al. · 2019 · Journal of Imaging Informatics in Medicine (doi:10.1007/s10278-019-00227-x · 1,638 citations). Source: > 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 image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions. > _Abstract excerpt by Mohammad Hesam Hesamian et al., CC-BY, https://doi.org/10.1007/s10278-019-00227-x; truncated._ - **[Machine Learning for Medical Imaging](https://space-kitty.com/papers/doi:10.1148/rg.2017160130)**. Bradley James Erickson, Panagiotis D. Korfiatis, Zeynettin Akkus et al. · 2017 · Radiographics (doi:10.1148/rg.2017160130 · 1,672 citations). Source: - **[Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists](https://space-kitty.com/papers/doi:10.1371/journal.pmed.1002686)**. Pranav Rajpurkar, Jeremy Irvin, Robyn L. Ball et al. · 2018 · PLoS Medicine (doi:10.1371/journal.pmed.1002686 · 1,449 citations). Source: > 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 the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence … > _Abstract excerpt by Pranav Rajpurkar et al., CC-BY, https://doi.org/10.1371/journal.pmed.1002686; truncated._ - **[Recent advances and clinical applications of deep learning in medical image analysis](https://space-kitty.com/papers/arxiv:2105.13381)**. Xuxin Chen, Ximin Wang, Ke Zhang et al. · 2022 · Medical Image Analysis (arXiv:2105.13381 · doi:10.1016/j.media.2022.102444 · 1,093 citations). Source: > 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 bottlenecked by the lack of large-sized and well-annotated datasets. In the past five years, many studies have focused on addressing this challenge. In this paper, we reviewed and summarized these recent studies to provide a comprehensive … - **[Medical Image Analysis using Convolutional Neural Networks: A Review](https://space-kitty.com/papers/arxiv:1709.02250)**. Syed Muhammad Anwar, Muhammad Majid, Adnan Qayyum et al. · 2018 · Journal of Medical Systems (arXiv:1709.02250 · doi:10.1007/s10916-018-1088-1 · 1,431 citations). Source: > 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 development area. One of the reason for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of au… - **[Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging](https://space-kitty.com/papers/doi:10.3390/diagnostics13172760)**. Reabal Najjar · 2023 · Diagnostics (doi:10.3390/diagnostics13172760 · 898 citations). Source: > 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… > _Abstract excerpt by Reabal Najjar, CC-BY, https://doi.org/10.3390/diagnostics13172760; truncated._ - **[Overview of deep learning in medical imaging](https://space-kitty.com/papers/doi:10.1007/s12194-017-0406-5)**. Kenji Suzuki · 2017 · Radiological Physics and Technology (doi:10.1007/s12194-017-0406-5 · 1,095 citations). Source: - **[Deep convolutional neural network based medical image classification for disease diagnosis](https://space-kitty.com/papers/doi:10.1186/s40537-019-0276-2)**. Samir S. Yadav, Shivajirao Manikrao Jadhav · 2019 · Journal Of Big Data (doi:10.1186/s40537-019-0276-2 · 1,069 citations). Source: > 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 has proven its potential for different classification tasks. Notably, the convolutional neural network dominates with the best results on varying image classification tasks. However, medical image datasets are hard to collect because it nee… > _Abstract excerpt by Samir S. Yadav et al., CC-BY, https://doi.org/10.1186/s40537-019-0276-2; truncated._ - **[Generating Radiology Reports via Memory-driven Transformer](https://space-kitty.com/papers/arxiv:2010.16056)**. Zhihong Chen, Yan Song, Tsung-Hui Chang et al. · 2020 · Conference on Empirical Methods in Natural Language Processing (EMNLP) (arXiv:2010.16056 · doi:10.18653/v1/2020.emnlp-main.112 · 693 citations). Source: > 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 an essential task to apply artificial intelligence to the medical domain. In this paper, we propose to generate radiology reports with memory-driven Transformer, where a relational memory is designed to record key information of the generati… - **[A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models](https://space-kitty.com/papers/arxiv:2407.06508)**. Gabriele Campanella, Shengjia Chen, Ruchika Verma et al. · 2024 · Nature Communications (arXiv:2407.06508 · doi:10.1038/s41467-025-58796-1 · 126 citations). Source: > 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 between research and clinical deployment. With the increase in availability of public foundation models of different sizes, trained using different algorithms on different datasets, it becomes important to establish a benchmark to compare the per… ## Newest - **[Does the Citation Matter? Quality, Grounding, and Physician-Rubric Accuracy of Google's AI Overviews for Medical Information](https://space-kitty.com/papers/arxiv:2610.39339)**. Sam Agnoli, Brigid Reilly, Lily Barna et al. · 2026 (arXiv:2610.39339). Source: - **[What Makes Medical Examination Items Difficult? Using Large Language Models to Explore Patterns Associated With Empirical Item Difficulty](https://space-kitty.com/papers/arxiv:2610.39373)**. Xiaomeng Xiong, Marcus Walker, Yanlin Jiang · 2026 (arXiv:2610.39373). Source: - **[Anatomical Prior-Guided Routing for 3D Lung Tumor Segmentation](https://space-kitty.com/papers/arxiv:2610.39414)**. Uihyeon Maeng, Jonghun Kim, Hyunjin Park · 2026 (arXiv:2610.39414). Source: - **[PathCowork: Learning from real-world pathology practice to build AI copilots](https://space-kitty.com/papers/arxiv:2610.39441)**. Sheng Wang, Jialu Yao, Maria V. Rossi et al. · 2026 (arXiv:2610.39441). Source: - **[BRIDGE: Learning Brain-Gut Metabolic Evidence for Alzheimer's Disease Diagnosis](https://space-kitty.com/papers/arxiv:2610.39520)**. Pengli Zhu, Ruxi Chang, Tianliang Yao et al. · 2026 (arXiv:2610.39520). Source: - **[Annotation-Free Representation Learning for Polyp Retrieval in Colonoscopy Videos](https://space-kitty.com/papers/arxiv:2610.39648)**. Luca Parolari, Lamberto Ballan, Carlo Biffi et al. · 2026 (arXiv:2610.39648). Source: - **[Confidence Filtering in Video-Based Gait Assessment: Measurement Error versus Selection Shift](https://space-kitty.com/papers/arxiv:2610.39704)**. David Regev, Eran Beeri Bamani · 2026 (arXiv:2610.39704). Source: - **[Aligning Medical Vision Language Models at the Capability Boundary with Entropy Adaptive Rubric Grounding](https://space-kitty.com/papers/arxiv:2610.39835)**. Hongyu Bai, Jiaqi Zeng, Chunzheng Zhu · 2026 (arXiv:2610.39835). Source: - **[DINOv2 for PET: A Pre-Training Domain Benchmark Across Classification, Segmentation, and Prognosis](https://space-kitty.com/papers/arxiv:2610.40018)**. Roxanne De Maeyer, Yannick Brackenier, Vicky De Ridder et al. · 2026 (arXiv:2610.40018). Source: - **[Segment Anything Model Based Context Guided Mamba Unet for Medical Image Segmentation](https://space-kitty.com/papers/arxiv:2610.40029)**. Chenchen Wang, You Zhou · 2026 (arXiv:2610.40029). Source: --- Space Kitty Archive · https://space-kitty.com · API: https://space-kitty.com/api/v1 · MCP: https://space-kitty.com/mcp · Premium: $0.20/request via x402 (Base Sepolia (testnet)). Metadata: arXiv (CC0), OpenAlex (CC0), Europe PMC (CC BY/CC0 works), ClinicalTrials.gov.