Weekly AI digest
Radiology & medical imaging AI · week of 23 to 29 August 2026
198 peer-reviewed papers · 5 industry and regulatory items · conference and KOL highlights. Adapted from my weekly intelligence report. Full report: FR (PDF) · EN (PDF) · RU (PDF).
Governance takeaway
What to do about it this week
This week's evidence cluster is operational AI, the tools that never reach your AI committee because nobody calls them clinical: a no-show model that decides which patients get outreach along a deprivation gradient, a camera system screening MR-unsafe wheelchairs, an LLM routing extraspinal findings to specialists. Pick one operational tool that is live or planned in your department this month and write its acceptance test around the harmful error direction: the high-risk patient not flagged, the unsafe chair called safe, the finding downgraded out of the follow-up queue.
The wheelchair study shows the method: report the harmful direction on its own denominator (8.3% of MR-unsafe instances missed in the worst-case model, invisible inside a 0.702 mAP), then decide.
Regulation & AI Act watch
ROPCA Arthur ultrasound robot and Diana AI cleared by FDA
Cleared for standardized acquisition of finger, hand and wrist joint ultrasound under professional supervision, with AI analysis and automated report support for arthritis assessment; the cited evidence is European clinical experience, not new US validation. First question for the vendor: what exactly is Diana cleared to report in the US, and at what performance.Heartflow FUSION trial: independent RCT evidence at ESC
An independent, Dutch government-funded randomized trial in 528 patients across twelve hospitals found FFRCT cut unnecessary invasive angiography by 44% at one year with identical revascularization rates, published simultaneously in JACC. This is the evidence bar; ask any vendor claiming workflow impact where their independent randomized data is.SimonMed deploys Optellum lung nodule risk scoring at enterprise scale
Optellum's Lung Cancer Prediction AI is FDA-cleared as radiologist-reviewed decision support for eligible incidental nodules, not autonomous diagnosis; the announcement adds scale, not new clinical evidence. Key question: who audits score-to-outcome concordance once the workflow runs across an entire enterprise.Aiforia and Stratipath combine risk stratification and platform in digital pathology
An integration agreement places CE-marked Stratipath Breast inside the Aiforia Clinical Platform for European marketing; no launch date and no statement yet on the regulatory status of the combined workflow under IVDR. Ask both vendors who validated the integrated pipeline end to end.Philips and Imricor launch a commercial interventional MR lab
The cardiac ablation lab combines Philips 1.5T MR with AI planning and Imricor's MR-compatible catheters; some configurations remain pending clearance, so verify the status of each component for your jurisdiction. MR entering the procedure room also moves MR-safety governance up the agenda, anteroom included.
Evidence you can use
Selected from 198 papers indexed in PubMed this week.
Radiology No-Show Calculator: A Social Determinants of Health-Enriched Machine Learning Prediction Model with Financial Analysis
Journal of the American College of Radiology, 27 August 2026Feasibility of AI-Based Wheelchair Classification for MRI Anteroom Safety Management: A Single-Centre Deep Learning Object-Detection Study
Journal of Imaging Informatics in Medicine, 26 August 2026Automating the Management of Extraspinal Findings in Magnetic Resonance Imaging Spine Studies Using a Privacy-Preserving Large Language Model: Retrospective Validation Study
Journal of Medical Internet Research, 26 August 2026Improved Deep Learning Segmentation of Pediatric Diffuse Midline Gliomas After Treatment
AJNR American Journal of Neuroradiology, 26 August 2026Multimodal Ultrasound-Based Decision Support for Bethesda IV Thyroid Nodules: Integration of Clinical, Radiomics, Deep Learning, and Topological Features
Journal of Imaging Informatics in Medicine, 25 August 2026An Imaging Informatics Workflow for Angiography-Derived Coronary Physiology Profiling and Virtual PCI Simulation
Journal of Imaging Informatics in Medicine, 26 August 2026EndoVLM: A Vision-Language Assistant for Gastrointestinal Endoscopy
Journal of Imaging Informatics in Medicine, 26 August 2026Gait-based diagnostic network for localization and pathological characterization of spine and pelvis diseases
Medical Image Analysis, 24 August 2026
Post-market signal
Two developments this week, both about who catches AI errors after go-live. Patient safety organization ECRI expanded its Problem Reporting Network, the confidential device-problem channel it has run since 1972, to explicitly capture errors, malfunctions and near misses involving AI tools, and called on providers nationwide to submit incidents. Its survey of 124 quality and safety leaders explains why: 31% encountered an AI output they believed incorrect in the past year, 9% said an error reached a patient or care decision, and 35% did not know. Alongside MAUDE and vendor complaint files, there is now a neutral place to send the near miss your radiologist caught.
Separately, the FDA posted a Class II recall record for Fujifilm Synapse PACS version 7.4.310: a software design flaw can make ruler measurements inaccurate on the reading workstation, with the workaround pointing at M-mode ultrasound measurements. Only six units are affected, but the lesson does not scale with the unit count: measurement is now a software function, and a measurement defect does not announce itself the way a broken gantry does. Worth checking whether your own QA would catch a workstation-side measurement error at all.
Playbook snippet
One step for your AI committee
Anchor: EU AI Act Article 10 (data governance).
Step: Add one clause to your next imaging AI RFP: the supplier discloses the composition of training and test data, including treatment phase, scanner mix and demographics, with performance reported by subgroup. Record refusal or silence as a risk item under Article 9 rather than treating it as acceptable.
Why: Training-set composition, not architecture, is what decides whether a model works on your use case, and it is the one thing a demo will never show you.
Worked example: Zielke J et al. trained a pediatric diffuse midline glioma segmentation model with post-treatment scans included and tested it externally on a prospective trial: whole-tumor Dice 0.90 vs 0.81 for the BraTS-PEDs 2024 challenge winner trained on pre-treatment data, with the largest gap on post-treatment scans, exactly where response assessment happens. AJNR Am J Neuroradiol, PMID 42648875, DOI 10.3174/ajnr.A9609.
From the field
RSNA published a news feature on a Radiology: Artificial Intelligence study testing 10 LLMs on 400 radiology board-style questions: prompts written to sound clinically plausible (authority, complexity, anchoring) cut accuracy by up to 21.1% on text and 44.9% on multimodal questions, with authority bias the most damaging. These are not adversarial tricks but recognizably human framings, exactly what a model embedded in clinical conversation will meet.
ACR reported that the Friends of NIBIB Coalition met with NIH officials to support imaging AI programs, notably PRIMED-AI, which will fund AI clinical decision support combining imaging with multimodal health data. ACR is also collecting member input for its response to the FDA's generative AI discussion paper, open for comments to 19 October.
On LinkedIn, Amine Korchi marked two milestones for ThinkSono, the AI-guided DVT ultrasound startup in his portfolio, arguing that AI's biggest impact will be redesigning how care is delivered rather than doing the same things better; in a second post he pushed back on the Swiss healthcare cost debate, noting a tariff hour is not an hour worked and that radiologist productivity reflects technology and organization. Curt Langlotz shared Modern Healthcare's feature on what radiologists want from the next generation of AI.
Daniel Pinto dos Santos amplified the ACR Data Science Institute's four questions for ACR-EuSoMII 2026 in Crete: is our AI performing as expected, who is responsible for monitoring it, how do we choose the next model, and is your governance governing. A ready-made self-test for any department's AI committee.
Source articles are indexed in PubMed with verified DOIs. Manuscript-stage work is excluded. The full weekly report is produced in French.
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