😎 See you Thursday, March 5ᵗʰ at the AI Theatre 👇

#ECR2026 #ECR #dischargeworkflows #Emergency #AIinRadiology

We are pleased to share that Perspectum will be participating in the Radiology AI Summit 2026!

If you are attending #RAIS26, we would welcome the opportunity to connect.

#Perspectum #AIinRadiology #MedTechInnovation #HealthcareAI

AI is changing chest X ray interpretation, but is it ready to work on its own? At RSNA 2025, experts debated whether AI should act independently or remain a co pilot to radiologists. The consensus points to strong potential with human oversight still essential. #AIinRadiology https://bit.ly/3McUTxu

#AIinRadiology
#MachineLearning
#RSNADatasets

Features like curved wall stand rails and motorized positioning are making X-ray rooms more efficient and adaptable.

💡 learn how: bit.ly/4smB8Ee

#HealthcareInnovation #AIinHealthcare #AIinRadiology #FutureOfImaging #AI #Radiology #DiagnosticRadiology #RSNA25 #DigitalRadiography

AI upgrades in ultrasound and CT, one-room interventional suites, and advanced healthcare IT were major buzz at Canon’s RSNA booth.

🔗 bit.ly/4qxKTh6

#HealthcareInnovation #AIinHealthcare #AIinRadiology #FutureOfImaging #AI #Radiology #RSNA25

From faster 3D scans to AI-assisted reading tools, the landscape of breast imaging is evolving to help clinicians work more efficiently.

🔗 bit.ly/4jbCVHT

#HealthcareInnovation #AIinHealthcare #AIinRadiology #FutureOfImaging #AI
#Radiology #RadAi #RadiologyAi

From advanced ultrasound imaging to integrated interventional CT suites, see how imaging innovation continues to evolve for real-world clinical needs.

Read more ➡️ bit.ly/4plNF8d

#HealthcareInnovation #AIinHealthcare #AIinRadiology #FutureOfImaging #RSNA25 #Radiology

Combo-U-Next−based Lung Nodule Segmentation and Neuron Attention LeNet for Lung Cancer Detection using CT Images

www.dl.begellhouse.com/journals/61f...

#LungCancerDetection #MedicalAI #AIinRadiology

Synapse: Your Connection to our MSK Authors.
Meet: Molly Parsons Hogan
Research Focus: Radiology; Assistant Attending

Adaptive beast MRI scanning using AI
synapse.mskcc.org/synapse/work...

#AdaptiveMRI #BreastMRI #AIMedicalImaging
#AIinRadiology #BreastCancerScreening #Radiology
#AI

New AI Educational Framework launches to boost radiology skills! Collaborative effort from RSNA, ACR, AAPM & SIIM outlines role-based AI competencies for users, developers, educators & leaders. Learn more: https://bit.ly/4gTEDN5 #AIinRadiology

"What if AI could interpret medical images faster and more accurately? High-performance GPUs are revolutionizing radiology by enhancing image analysis and streamlining reports. How do you see AI shaping radiology's future? #AIinRadiology #GPUs #RadiologyInnovation" LINK

How Pre-Trained Vision Models Are Revolutionizing Anatomical Structure Retrieval

A new benchmark for 3D medical image retrieval shows how pre-trained vision models and ColBERT-inspired methods enhance organ-level search. #aiinradiology

Why One Small Parameter Can Make or Break Your Medical Image Retrieval

Learn how volume-based, region-based, and localized retrieval shape medical imaging accuracy—and why localization-ratio matters. #aiinradiology

DreamSim and the Future of Embedding Models in Radiology AI

DreamSim embeddings outperform rivals in medical image retrieval, while fractal-based models show surprising promise for scalable AI training. #aiinradiology

How Dataset Imbalances Shape Medical Image Retrieval Accuracy

Exploring dataset imbalance and ColBERT-inspired re-ranking in medical image retrieval—balancing recall accuracy with real-world feasibility. #aiinradiology

Boosting Anatomical Retrieval Accuracy with Re-Ranking Methods

Re-ranking boosts retrieval recall in anatomical AI models, with DreamSim leading performance across volume, region, and localization tasks. #aiinradiology

Medical AI Models Battle It Out—And the Winner Might Surprise You

Evaluation of retrieval recall in medical AI—comparing DreamSim, DINO, and ResNet50 across slice-wise, volume, region, and localized methods. #aiinradiology

Embedding Similarity Scoring for Smarter Image Retrieval Systems

Refine search accuracy with re-ranking, ColBERT, and embedding similarity. Learn how AI reshapes information and image retrieval. #aiinradiology

How AI Retrieves Anatomical Structures Using Vector Databases

Explore slice, region, and localized retrieval methods for medical image databases using cosine similarity and vector embeddings. #aiinradiology

Comparing Six Deep Learning Feature Extractors for CBIR Tasks

Comparing six deep-learning feature extractors for CBIR, from DINOv2 to SwinTransformer, across supervised and self-supervised models. #aiinradiology

Building a CBIR Benchmark with TotalSegmentator and FAISS

Exploring FAISS, HNSW, and ANN methods for fast, metadata-free image retrieval using the TotalSegmentator medical dataset. #aiinradiology

Medical Image Retrieval Needs a New Benchmark

New benchmarks for 3D medical image retrieval show how AI embeddings improve radiology search across 29 regions and 104 organs. #aiinradiology

China Medical Imaging Software Market Size, Growth Outlook 2035 China Medical Imaging Software Market projected to grow at 17.69% CAGR, reaching USD 734.4 Billion by 2035, driving growth global trends, competitive industry analysis and outlook 2025-2035

🔍 From pixels to insights! China’s #ImagingSoftware growth powered by AI, big data & tele-radiology.
#AIinRadiology #ChinaHealthTech #CloudImaging #SmartDiagnostics

www.marketresearchfuture.com/reports/chin...

Assessing accuracy and legitimacy of multimodal large language models on Japan Diagnostic Radiology Board Examination
Abe, O., Hanaoka, S. et al.
Paper
Details
#AIInRadiology #LLMAccuracy #JapanRadiologyExam

Let’s reimagine how radiology tells its story—one image at a time.
Thanks to all who've helped make this happen!
— Brendan
#BrendansBest #Radiology #VisualScience #ESR #AIinRadiology

Radiology is Facing a Workforce Crisis: NVIDIA and GE HealthCare Push to Augment Radiology with Physical AI Hospitals worldwide are sounding the alarm. There simply aren’t enough radiologists or imaging technologists to meet the growing demand for medical scans. What’s happening? A perfect storm: an aging radiologist workforce approaching retirement, skyrocketing imaging volumes, persistent gaps in subspecialties like pediatrics and interventional radiology, and alarmingly high vacancy rates among CT, MRI, and ultrasound technologists. The result? Longer wait times, delayed diagnoses, and overstretched staff.

With nearly 2/3 of the global population lacking access to imaging, this is more than innovation—it’s healthcare transformation. NVIDIA and GE HealthCare have announced a strategic partnership to bring physical AI into real-world radiology workflows. #NVIDIA #GEHealthcare #PhysicalAI #AIinRadiology

Radiology is Facing a Workforce Crisis: NVIDIA and GE HealthCare Push to Augment Radiology with Physical AI Hospitals worldwide are sounding the alarm. There simply aren’t enough radiologists or imaging technologists to meet the growing demand for medical scans. What’s happening? A perfect storm: an aging radiologist workforce approaching retirement, skyrocketing imaging volumes, persistent gaps in subspecialties like pediatrics and interventional radiology, and alarmingly high vacancy rates among CT, MRI, and ultrasound technologists. The result? Longer wait times, delayed diagnoses, and overstretched staff. In response, tech giants NVIDIA and GE HealthCare are stepping in with a bold solution—deploying physical AI to automate and enhance diagnostic imaging, offering a glimpse into a future where smarter machines support overworked teams and help close the global care gap.

With nearly 2/3 of the global population lacking access to imaging, this is more than innovation—it’s healthcare transformation. NVIDIA and GE HealthCare have announced a strategic partnership to bring physical AI into real-world radiology workflows. #NVIDIA #GEHealthcare #PhysicalAI #AIinRadiology

🧠 AI in Radiology: Cathrine E. Keller, MD

🔹 Topic: Practical AI Radiologic Implementation
🔹 Date: July 12, 2025
🔹 Why Attend? Learn actionable steps for integrating AI into your radiology workflow.
#FRS2025 #AIinRadiology #RadiologyTech

Registration link: loom.ly/No99sHA

📢 Speaker Spotlight: Eliot L. Siegel, MD, FACR, FSIIM

🔹 Topic: AI in 2025 Radiology & Key Trends in Artificial Intelligence
🔹 Date: July 12, 2025
🔹 Why Attend? Dr. Siegel is a pioneer in medical imaging AI!

#FRS2025 #RadiologyInnovation #AIinRadiology

Registration link: loom.ly/rsJaSc0