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New radiology foundation models show promise, but evaluation and translation challenges remain · 4 sources…

Two new technical reports detail advancements in radiology foundation models. One review paper analyzes 67 studies on vision foundation models (VFMs) in radiology, highlighting the prevalence of transformer architectures and self-supervised pretraining, but noting inconsistencies in evaluation and reporting. The other report introduces Harrison.Rad 1.5, a multimodal large language model specifically designed for radiology, which has demonstrated the ability to draft reports from images and clinical context, even meeting simulated professional examination standards. AI

IMPACT These advancements suggest improved efficiency and accuracy in radiological report generation, potentially alleviating workforce shortages.

RANK_REASON Two technical reports detailing new research in AI for radiology.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New radiology foundation models show promise, but evaluation and translation challenges remain · 4 sources…

COVERAGE [4]

  1. arXiv cs.AI TIER_1 English(EN) · Alejandro Vergara-Richart (Quantitative Imaging Biomarkers in Medicine, Quibim S.L., Val\`encia, Spain, Universitat Polit\`ecnica de Val\`encia, Val\`encia, Spain), Xavier Rafael-Palou (Quantitative Imaging Biomarkers in Medicine, Quibim S.L., Val\`encia… ·

    Vision Foundation Models in Radiology: A Scoping Review of Data, Methodology, Evaluation and Clinical Translation

    arXiv:2607.07219v1 Announce Type: cross Abstract: Vision foundation models (VFMs) are increasingly being developed for radiological imaging, yet their definition, development and evaluation remain heterogeneous. We conducted a PRISMAScR scoping review of peer-reviewed studies pub…

  2. arXiv cs.AI TIER_1 English(EN) · Ana Jiménez-Pastor ·

    Vision Foundation Models in Radiology: A Scoping Review of Data, Methodology, Evaluation and Clinical Translation

    Vision foundation models (VFMs) are increasingly being developed for radiological imaging, yet their definition, development and evaluation remain heterogeneous. We conducted a PRISMAScR scoping review of peer-reviewed studies published between January 2017 and March 2026 describ…

  3. arXiv cs.AI TIER_1 English(EN) · Suneeta Mall, Vladimir Nekrasov, Ashnil Kumar, Sajith Karunasena, Aiden Nibali, Alix Bird, Mateo Diaz Shine, Jarrel Seah ·

    Harrison.Rad 1.5 Technical Report: A radiology foundation model that can draft reports from images, priors and clinical context

    arXiv:2607.05880v1 Announce Type: cross Abstract: Imaging demand is growing faster than the radiology workforce can expand, and reporting backlogs cannot be resolved through training and recruitment alone. The most direct opportunity is reducing the time and effort radiologists s…

  4. arXiv cs.CV TIER_1 English(EN) · Jarrel Seah ·

    Harrison.Rad 1.5 Technical Report: A radiology foundation model that can draft reports from images, priors and clinical context

    Imaging demand is growing faster than the radiology workforce can expand, and reporting backlogs cannot be resolved through training and recruitment alone. The most direct opportunity is reducing the time and effort radiologists spend producing reports, a task that requires inter…