The field of medical imaging analysis and clinical diagnosis is rapidly advancing with the development of new AI-powered tools and techniques. Recent research has focused on improving the accuracy and efficiency of medical image analysis, as well as enhancing clinical diagnosis through the integration of multiple modalities and sources of information. Notably, the use of large language models and multimodal fusion approaches has shown significant promise in improving diagnostic performance and patient outcomes. Furthermore, the development of novel frameworks and models, such as those utilizing retrieval-augmented diagnosis and self-learned knowledge, has the potential to revolutionize clinical practice. Some particularly noteworthy papers in this regard include the introduction of Citrus-V, a multimodal medical foundation model that combines image analysis with textual reasoning, and MACD, a multi-agent clinical diagnosis framework that allows large language models to self-learn clinical knowledge. Overall, these advances have the potential to transform the field of medical imaging analysis and clinical diagnosis, enabling more accurate and efficient diagnosis, and ultimately improving patient outcomes.
Advances in Medical Imaging Analysis and Clinical Diagnosis
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Deep learning and abstractive summarisation for radiological reports: an empirical study for adapting the PEGASUS models' family with scarce data
From Data to Diagnosis: A Large, Comprehensive Bone Marrow Dataset and AI Methods for Childhood Leukemia Prediction
Network-Based Detection of Autism Spectrum Disorder Using Sustainable and Non-invasive Salivary Biomarkers
Visionerves: Automatic and Reproducible Hybrid AI for Peripheral Nervous System Recognition Applied to Endometriosis Cases
Citrus-V: Advancing Medical Foundation Models with Unified Medical Image Grounding for Clinical Reasoning
HyKid: An Open MRI Dataset with Expert-Annotated Multi-Structure and Choroid Plexus in Pediatric Hydrocephalus
PS3: A Multimodal Transformer Integrating Pathology Reports with Histology Images and Biological Pathways for Cancer Survival Prediction