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AI Reporting Tools Now Carry RSNA’s Institutional Voice

The recent collaboration between RSNA Ventures and Rad AI marks a significant evolution in the role that professional societies can play in applied innovation.

RSNA Ventures Links Strategy to Innovation Execution

The launch of RSNA Ventures marks a structural shift in how the radiology community approaches innovation, not as a passive recipient of market trends, but as an active investor in its future. By formalizing a venture-oriented subsidiary, the Radiological Society of North America (RSNA) signals a deeper institutional commitment to shaping emerging technologies rather than merely reacting to them.

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Radiology Leaders Push for Structured AI Competency

The accelerating integration of artificial intelligence in medical imaging is a present-state imperative. As radiology departments increasingly adopt AI tools to support diagnostics, workflow optimization, and quality assurance, the conversation has shifted from if to how. Yet even as algorithm performance improves, a critical challenge persists: how to equip the radiology workforce with the knowledge required to safely evaluate, implement, and oversee these tools across diverse clinical settings.

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Bayer’s AI Retreat Exposes Fault Lines in Radiology Platform Strategy

Bayer has confirmed it will discontinue both its Calantic Digital Solutions platform and the services of its subsidiary, Blackford Analysis, marking a full retreat from the radiology AI platform business. The announcement effectively closes a five-year push to centralize access to artificial intelligence tools for diagnostic imaging, an initiative that, despite technical promise, struggled to secure meaningful traction in hospital workflows.

Native DICOM Output Redraws the Pathology Map

The long-awaited arrival of native DICOM output in digital pathology signals more than a technical milestone. It marks a strategic convergence of imaging standards, data architecture, and AI readiness across healthcare enterprises. As vendors begin aligning digital pathology workflows with established medical imaging protocols, health systems face an opportunity, and an obligation, to reassess how pathology data is stored, shared, and analyzed at scale.

Floating Table Upgrade Advances Imaging Throughput

Radiology departments continue to face mounting volume, staffing constraints, and budget pressure. A six-way floating table now integrated into the Carestream Horizon X-ray System targets those challenges by improving patient positioning, reducing technologist effort, and limiting motion artefacts that degrade diagnostic clarity.

AI Elevates Tomosynthesis Cancer Detection

Digital breast tomosynthesis entered U.S. screening programs with the promise of sharper lesion visualization and fewer unnecessary recalls. Despite those gains, interval breast cancers, tumors discovered symptomatically after a negative exam and before the next scheduled visit, continue to undermine clinical and financial goals.

Aneurysm AI Challenge Sets New Bar for Multimodal Imaging Research

The Radiological Society of North America (RSNA) has issued a global call to data scientists, launching the 2025 Intracranial Aneurysm Detection AI Challenge in partnership with the American Society of Neuroradiology, the European Society of Neuroradiology, and the Society of Neurointerventional Surgery.

Post COVID CT Requires Global Low Dose Discipline

Chest CT is the linchpin for evaluating lingering respiratory complaints after acute SARS-CoV-2 infection, yet practices vary widely by country, vendor platform and radiologist preference. A new international consensus statement published in Radiology by the Radiological Society of North America aligns 14 nations on when to scan, how to scan and what to call the residual shadows that can persist for months.

Radiologists Share Tips to Prevent AI Bias

“AI has the potential to revolutionize radiology by improving diagnostic accuracy and access to care,” said lead author Paul H. Yi, M.D., associate member (associate professor) in the Department of Radiology and director of Intelligent Imaging Informatics at St. Jude Children’s Research Hospital in Memphis, Tennessee. “However, AI algorithms can sometimes exhibit biases, unintentionally disadvantaging certain groups based on age, sex or race.”

Shorter MRI Exam Effectively Detects Cancer in Dense Breasts

Abbreviated breast MRI shortens exam time while retaining a high level of diagnostic accuracy of breast cancer in women with extremely dense breasts, according to an article published today in Radiology, a journal of the Radiological Society of North America (RSNA).

Fine-tuned LLMs Boost Error Detection in Radiology Reports

A type of artificial intelligence called fine-tuned large language models (LLMs) greatly enhances error detection in radiology reports, according to a new study published today in Radiology, a journal of the Radiological Society of North America (RSNA). Researchers said the findings point to an important role for this technology in medical proofreading.

UVA Health University Medical Center Expands MRI Suite

To improve the patient experience and access to care, UVA Health University Medical Center has opened an expanded magnetic resonance imaging (MRI) suite that will better accommodate the thousands of patients receiving these scans annually. MRIs are a non-invasive imaging technique used to detect and diagnose health conditions throughout the body, as well as to monitor the progress of certain treatments.

Philips and NVIDIA’s MRI Foundation Model Signals a Platform Shift in Imaging AI

On May 14, 2025, Philips and NVIDIA announced a strategic partnership to co-develop a domain-specific foundation model for magnetic resonance imaging (MRI), targeting what both companies describe as the next generation of intelligent radiology infrastructure (Philips, 2025). The initiative fuses NVIDIA’s VISTA-3D and MAISI platforms with Philips’ clinical imaging datasets and workflow integrations, aiming to produce a large-scale neural network that enables faster scans, zero-click anatomical planning, and automated anomaly detection.

Special Report Highlights LLM Cybersecurity Threats in Radiology

In a new special report, researchers address the cybersecurity challenges of large language models (LLMs) and the importance of implementing security measures to prevent LLMs from being used maliciously in the health care system. The special report was published today in Radiology: Artificial Intelligence, a journal of the Radiological Society of North America (RSNA).