The future of medical imaging is the field: what a scan can say beyond a picture (radiomics / radiogenomics), what the next gantries change, and the ethics that arrive with both. It is not next-gen radiology as a stack. It is not “what is AI in radiology.” It is not a PYCAD platform.
If you meant the stack (AI + cloud PACS + web viewer + photon-counting / helium-free) → next-gen radiology. If you meant AI as second reader / who signs → artificial intelligence in radiology. If you meant how a model learns from pixels → machine learning for medical imaging. If you meant the hospital AI stack → what is artificial intelligence in healthcare.
704 already kept 7963 as the stack and pointed at this URL for “future of medical imaging as a field (radiomics / ethics).” That split stands. This page is not a second next-gen article.
PYCAD builds custom web DICOM viewers and imaging models. That is one piece of whatever the field does next. It is not a “future of imaging” product.
What “future” is allowed to mean here
A 2D radiograph was a shadow. CT and MRI made a volume. PET/MRI is structure plus a tracer on the same sitting. None of that is news, and none of it needs a market CAGR. The open questions are different:
- Can the volume yield numbers a report did not used to have (radiomics), and can those numbers talk to a genotype (radiogenomics)?
- Do new detectors and magnets change the pixels enough that yesterday’s workstation is the wrong tool?
- Who gets the scan, who eats the dose, and what do you do with a finding nobody asked for?
AI as a second reader is 671. The archive + viewer + worklist that have to talk is 704. Both are linked. They are not restated here.
Radiomics and radiogenomics
Radiomics is a feature dump from a defined region: shape, texture, intensity histograms, wavelet bins. The hypothesis is that some of those numbers track grade, receptor status, or response better than “it looks irregular.” The region still has to be drawn or segmented; garbage contour, garbage table. A radiomic signature is a candidate biomarker. It is not a diagnosis, and it is not cleared just because a paper has an AUC.
Radiogenomics asks whether those imaging features co-vary with a named mutation or expression panel. The useful version names the gene and the sequence. The slide-deck version says “precision medicine” and stops.
Both need the same unromantic plumbing: a stable acquisition (or an honest statement of the mix), a contour you can reproduce, a hold-out that is not the same scanner, and a clinician who will not treat a radiomic score as a biopsy. Segmentation as a method is a 675 job. This page only names why people extract the table.
Hardware that is not the 704 stack
Photon-counting CT and helium-free MRI already live on next-gen radiology as the hardware layer of that stack. Do not reread them here. Three other machines get sold as “the future” and are a different sentence:
| Machine | What actually changes | What it is not |
|---|---|---|
| Ultra-high-field MRI (7 T and up) | More signal, finer brain detail, research protocols that a 1.5 T was not built for | Not a community-hospital default. Siting, implants, and artefact are the tax |
| Handheld / portable ultrasound | The probe goes to the bed, the ambulance, or a clinic that will never own a gantry | Not a replacement CT. Operator-dependent; the clip still has to land on the chart |
| AR surgical overlay | A CT/MR segmentation projected on the field so the plan and the incision share a coordinate frame | Not a new camera. Registration error is the failure mode. The file still has to be DICOM |
Portable ultrasound is access. Ultra-high-field is physics. AR is a registration problem. None of them is “we bought AI.”
Ethics that do not fit a stack diagram
- Dose. CT is ionizing. A protocol that answers the clinical question at a lower dose is the job; a prettier volume is not a reason to scan a child twice. Optimization is a 705 / physicist conversation, not a slogan.
- Incidental findings. A wider FOV and a smarter detector find things nobody ordered. Each one is a letter, a follow-up, and a bill. A department needs a rule for what gets mentioned, not a hope that the model will be discreet.
- Access. A 7 T and a photon-counting CT concentrate in rich systems. A handheld probe is how the same decade looks in a place that will never buy either. Equity is a placement problem, not a press release.
- Training data. Models that need a million studies inherit the sites that could share them. FL is one answer when the file cannot move — that leftover is federated learning in healthcare. De-identification is 6216.
What this page is not
- Not 7963. Next-gen radiology is AI + cloud PACS + web viewer + the two gantries that change the pixels. 704 left 5830 on purpose.
- Not 671 / 5644. “Will AI replace radiologists” is already answered there: no, assistive, the reader signs.
- Not a market forecast. Grand View / Statista / GlobeNewswire CAGRs, the invented segment table, pdf.ai, the Hostinger “spark creativity” outbound, YouTube, and the Outrank stills are gone.
- Not a PYCAD “future of imaging” platform.
If the next scanner’s volume has to open in a clinic app, that is the imaging piece. Case studies.