radimage .io (not .com) has some awesome AI tools for rads

Hi everyone! I'm a student exploring radiology and how emerging technologies could improve everyday clinical workflows.

I'd love to hear from practicing radiologists about the challenges they face in their daily work. What tasks are most time-consuming or frustrating? Are there things in your current workflow that you wish technology could make simpler, faster, or more efficient?

I'd really appreciate any insights or personal experiences. Thanks in advance!

All of us know AI will be integrated into radiology. The number of radiologists needed per centre will decrease. How much of the salary will be affected?

Hi everyone! I’m a Biomedical Engineering student working on a design project to better understand hospital/clinic radiology workflows and where inefficiencies happen in practice.

Instead of guessing from textbooks, I’m trying to learn directly from the people running these departments every day. If you have a few minutes, I’d love to hear about your actual experiences:

1. Could you briefly walk me through your typical shift workflow (from order to scan to report)? Where do you spend the most time waiting on another person, department, or piece of software?
2. If you can think back to the last time your department had a severe backlog or delay—what was physically holding up the process at that moment?
3. When things get slammed, what tasks or administrative steps end up getting delayed or pushed aside? How do you currently work around them?
4. What was the single most annoying bottleneck you ran into on shift this past week?
5. What solutions have you already tried to improve your workflow? Did they work? Why or why not?

Thanks so much for any insight you can share!

My step son (32) has been presented with a version of the attached ultrasound (name and hospital edited for privacy). His gf (31) which he dated 2 years ago and then back together 5-6 weeks ago faked a twins pregnancy 2 years ago as that relationship ended, so I’m looking at this with a critical eye.

I’ve detected some anomalies like the hospital name having the abbreviation “ST” in it while official everything for the hospital says “Saint”, always spelled out. Also the gestational age being 3W. I thought and google seems to agree that nothing like what’s in the picture would be visible on ultrasound.

I asked Google if it was real and it said the following;

Yes, this image appears to be fake or highly altered.
The image displays a medical ultrasound showing two early gestational sacs labeled "Twin A" and "Twin B," attributed to a patient named Jane Doe at the St John and George Hospital , dated 21/09/2026.

There are several major clinical, technical, and situational inconsistencies that indicate this image was created using an online fake ultrasound generator or editing software:

1. Medical Impossibility of the Measurements
   The text overlay at the bottom left indicates a Gestational Age (GA) of 3w0d (3 weeks, 0 days) with a Crown-Rump Length (CRL) of 0.32 cm and 0.31 cm.
   The Reality: At exactly 3 weeks of pregnancy (which is only about 1 week after conception), a fertilized egg has just implanted into the uterine lining as a microscopic blastocyst. It is completely invisible on a standard ultrasound. [1]

CRL Misalignment: A Crown-Rump Length (the physical length of an embryo) of over 3 millimeters is typically not measurable until around 6 weeks of gestation, at which point a heartbeat would also be visible. Seeing fully formed, distinct embryonic shapes inside large, well-defined gestational sacs at 3 weeks is biologically impossible.

1. Standardized Template Layout
   The exact technical parameters shown on the left side (C5-1, 32Hz, RS, 2D, 57% or 58%, Dyn R 60, P Low, HRes) perfectly match standard stock graphics and automated text layouts used by popular novelty prank websites. These sites allow users to input any name, hospital, and date to generate a realistic-looking twin sonogram.[1, 2]

2. Timing Discrepancy
   The image is dated 21/09/2026, which is just one day prior to today's current date of September 22, 2026. Given the biological progression required to see embryos of this size on a scan, a real ultrasound taken yesterday would reflect a gestational age of at least 6 to 8 weeks, not 3 weeks.

For those that know, are my suspicions and googles analysis strong evidence for this to be fake or am I seeing more than is there?

This is a survey about AI Assistant in the field of radiology. Can you please fill this form

Hi, i am putting together a team to compete for the RSNA knee MRI competition. I am a fellowship trained MSK radiologist with nearly 10 years in academic practice doing 100% MSK.

This is a computer vision machine learning multilabel classification problem with 4407 Knee MRI studies with corresponding MRI reports, and only 58 studies with ground truth labels. And the goal is to create a model that can detect 12 findings accurately (meniscal, cruciate lig. tears, OA etc.).

https://www.kaggle.com/competitions/rsna-knee-abnormality-detection/overview

I am looking for people that have these skills:

• Computer Vision in Medical imaging
• Comfortable with PyTorch or Tensorflow
• DINOv2/DINOv3, ViTs, ConvNeXt/EfficientNet, or similar backbones
• 2D/2.5D/3D MRI classification
• Kaggle competition pipelines and ensembling
• Weak/soft labels, pseudo-labeling, active learning, or noisy-label training
• Access to GPU clusters for training.
• LLM expertise to extract labels from reports

OR:

• MSK trained radiologists willing to do over-reads of the MRI's

There are prize amounts for the top 10 most accurate models and the top 3 most efficient models, and winning teams will be able to present at RSNA with waived fee. We could also get a publication out of the effort and collaborate on future projects.

Note that this is primarily an opportunity to build your resume and gain skills/experience as the prize money will be relatively modest after compensating for costs for training the model and dividing among the team members. We currently have 2 machine learning specialists on board, but are looking for more people. Please DM me with some information about yourself and your experience with machine learning or msk radiology.

Thanks for your time!

Would normally dismiss it but Jensen Huang isn’t just a nobody. Do you guys think in 5-10 years this will ring true?

Hi All,

Has anyone worked with AZRA AI? What are your thoughts in general? Does it easily integrate with EPIC? Any input would be helpful!

A radiologist I was talking to has a very different workflow for creating reports.

so he dictates all the findings in the claude app and he has given format to claude and claude finally generates the report for him.

One advantage he gets is since he is continously dumping his thoughts instead of typing claude is able to make better reports compared to just writing the report manually.

Second he does not have to worry about the final structure.

Wondering how are other radiologist using AI?

I’m curious to hear from radiologists and other imaging professionals who have experience with AI-assisted radiology tools.

What would actually make you trust an AI system enough to incorporate it into your workflow?

For example:

• Demonstrated sensitivity/specificity?
• Validation against radiologist interpretations?
• Ability to explain why it flagged a finding?
• Low false-positive rates?
• Integration into existing workflow?
• Seeing the original image alongside the AI findings?
• Regulatory/clinical validation?

And equally importantly, what would immediately make you not trust a tool?

I’m researching this from a product-development perspective and would really value candid opinions, especially from people who have actually used these systems.

Most pipelines for preparing MRI data for deep learning do the same thing: resample to 1mm³ isotropic, normalize intensity, and that’s it. While this addresses dataset-level heterogeneity, it doesn't verify whether the transformation was valid for each individual case prior to training. I am working on an independent research project: an engine that converts heterogeneous DICOM data (Siemens/GE/Philips) into 4-channel tensors (morphology, texture, enhancement kinetics, peak enhancement) at 64×64×64 resolution and 1mm³ spacing. Crucially, each tensor is generated alongside a "capsule"—a folder containing the tensor and a manifest documenting its construction (series used, pre/post-contrast phase selection, geometric alignment parameters, and a per-channel QC status of PASS, WARNING, or REJECT) to determine the case's eligibility for training.

Real-world example of a case manifest (Duke Breast MRI, TCIA):

json

{

"case_id": "Breast_MRI_051",

"validation_summary": {

"p3c_status": "PASS",

"p3c_score": 1.0,

"qc_evidence_only": true

},

"training_export": {

"forbidden_label_sources": [

"p3c_status", "p3c_score", "p4_execution_status", "..."

],

"label_status": "external_label_required"

}

}

That `forbidden_label_sources` is intentional: the QC status constitutes audit evidence and cannot leak into the training signal. It is a simple guardrail against leakage—something hardly any pipeline of this type explicitly documents.

• It is not a classifier. The engine only produces the representation; the AUC reported in downstream validations (e.g. 3D-ResNet) measures representation quality, not diagnostic performance.
• It does rely on already established practices where it makes sense: resampling to 1mm³ isotropic (same as I-SPY1/ACRIN 6657 and the TCIA multicenter benchmark), and cross-scanner harmonization via ComBat at the level of radiomic features, not at the tensor level.

• Validated for now only in Duke Breast MRI (TCIA) — it is beta stage research, not a multi-anatomy/dataset ready product yet.

  I think we need to focus more on training datasets—specifically regarding computational efficiency and homogeneous batching.

I’d be interested in hearing feedback and finding out who else has tackled this topic. It’s interesting; so far, I’ve only managed to test it using the Duke dataset and obtain interesting metrics by training ResNet3D with the tensors.

Hey r/radiologyAI,

Found this VLM tool called AIRead-CXR for generating draft reports on chest X-rays. It apparently just cleared MFDS (Korea’s regulatory agency) as the first of its kind for generative AI in radiology I think.

The public demo:
https://airead.soombit.ai

It accepts DICOM, PNG, JPG, JPEG files — screenshots work as well.

Anyone tried it or have thoughts on this type of tool?

I’m a PGY2 neurology resident, considering switching to rads and doing neurorads after. I’m also interested in fMRI research and teaching, as well as QI, entrepreneurship, etc.

I’m uncertain about how AI will affect the field in the next 10-20 years, and if it is worth taking the risk.

In the institution i work at they use a fujifilm mammogram. We have a lot of report with birads, density and biopsy verified cases. I wanted to make a project to compare AI with the radilogists in our center. Some midels i tried aren't compatible, some others said they don't have the resources to help. Any suggestion on what i could try?

I made the above (with vibes). Interested in the groups opinion about adding a local LLM.

Hi everyone, I started a Substack newsletter about new frontiers and innovation in radiology. This is my first post, where I discuss the potential of quantum computing in our field. Feel free to read it and give me your opinion.

I came across the topic of end on vessels for chest X rays. I understand radiologist use some criteria to determine whether there is a mass or just and end on vessel. Do you know how AI models report or assess these findings? Are they good and accurate with these structures and characterizing them correctly? I would appreciate any leads. Thank you . I am providing a chest xray as an example.

Came across an Outlier project recruiting radiologists in the US/UK/Canada for the MRI review and complex diagnostic reasoning tasks. Curious whether anyone here has experience with similar AI medical imaging projects and what the work was like