The paper presents a multimodal information system that integrates structured clinical summaries, medical images, and biomedical named entities from 52,949 open‑access case reports published between 2000 and 2021. This information is organized within a well‑structured medical ontology, and a user interface is designed to help junior clinicians search and browse case reports efficiently. The system aims to improve the identification and diagnosis of rare diseases by overcoming the limitations of traditional keyword‑based retrieval on unstructured case reports.
arXiv:2608. 04144v1 Announce Type: cross Abstract: Biomedical entity linking grounds mentions in clinical and scientific text to entities in a curated knowledge base (KB) with ontological structure, which supports downstream applications such as literature-scale information extraction and patient-record normalization.
arXiv:2608.22323v1 Announce Type: new Abstract: The application of Large Language Models (LLMs) to diagnostic decision-making has garnered growing interest. However, existing benchmarks largely focus...
arXiv:2604. 25605v2 Announce Type: replace-cross Abstract: Introduction: Semantic search, which retrieves documents based on conceptual similarity rather than keywords, offers advantages for retrieval of clinical information.
PathPocket is a multimodal AI co‑pilot that grounds pathology decision‑making in evidence. It builds the largest pathology evidence corpus (≈110,472 documents) and a hypergraph of 4.55 million entities and 7.10 million relations to support traceable reasoning. The system handles text and multimodal queries, including ROI and gigapixel whole‑slide images, and outperforms current state‑of‑the‑art models on a benchmark of over 200,000 real‑world cases, improving pathologists’ diagnostic accuracy and confidence.
arXiv:2606. 08093v1 Announce Type: new Abstract: Pathology is the cornerstone of modern medicine, where accurate decision-making relies heavily on evidence-based practices.
Case2Flow is a new task that retrieves the most relevant guideline flowchart for a given patient case from a collection of medical guideline documents. The authors created FlowAtlas, a curated corpus of 202 flowcharts extracted from 2,080 guidelines, and a pipeline that generates 1,911 aligned case‑flowchart pairs. Their evaluation shows that existing multimodal retrieval methods often overrely on keywords and spurious token‑patch matches, and they propose CRISP, a training‑free scoring method that improves Recall@1 by up to 18.71 percentage points and gains preliminary feasibility evidence from a blinded physician assessment.
arXiv:2603.11938v2 Announce Type: replace-cross Abstract: Structured radiology reporting promises faster, more consistent communication than free text, but automation remains difficult as models must...
The paper introduces Biomedica, an open-source dataset sourced from PubMed Central that includes over 6 million scientific articles and 24 million image‑text pairs, along with 27 metadata fields and expert human annotations. To facilitate use, the authors provide scalable streaming and search APIs via a web server. They demonstrate the dataset’s value by training embedding models, chat‑style models, and retrieval‑augmented chat agents, all of which outperform previous open systems in their categories.
arXiv:2605. 22547v3 Announce Type: replace-cross Abstract: Medical image diagnosis has achieved significant progress with deep learning, yet existing methods often rely on isolated visual evidence and lack the ability to effectively leverage similar cases and external knowledge.
arXiv:2607. 18270v1 Announce Type: new Abstract: While Electronic Health Records (EHRs) offer a wealth of clinical data, effectively augmenting a patient's records with heterogeneous external knowledge to predict the patient's clinical risk remains a significant challenge.
arXiv:2608. 08056v1 Announce Type: new Abstract: Medical data, by its nature, exhibit a high degree of heterogeneity on multiple levels ranging from (a) different modalities like images, text and time series, (b) diverse tabular schemata introduced by institutions and (c) completely unstructured textual information data provided by healthcare professionals.
Large chest radiography archives are difficult to search because most studies are paired only with free-text reports rather than structured clinical annotations. Vision-language models offer a natural interface for text-to-image retrieval, but current biomedical models are primarily optimized for report-to-image matching rather than for satisfying short clinical search queries.