1) AI that graduates from pilot to practice

This year’s agenda emphasizes real outcomes over proofs of concept: reader-in-the-loop tools, bias monitoring, and governance. In breast imaging alone, RSNA previews spotlight external validation for image-only risk models and integration of MRI signals into multimodal AI—clear signals that “personalization” is landing in routine workflows. Bring vendor questions that force specifics: external validation cohorts, drift detection, and how metrics (TAT, recalls, rework) appear in your dashboard. RSNA

What to set up before RSNA: define 3–5 outcome metrics and insist every demo shows pre/post performance tied to those measures. Use QIBA concepts to push for standardized inputs/outputs so results are reproducible across scanners and sites. QIBA Wiki

2) Personalization that reaches the reading room

Personalization isn’t only radiogenomics. RSNA’s preview points to risk-stratified pathways you can actually run: e.g., image-only 5-year breast cancer risk at the point of screening to route patients into annual vs. short-interval follow-up or supplemental imaging (CEM/MRI). That pairs well with updated U.S. recommendations: screening beginning at age 40 for average-risk women, then adjusting based on risk and local policy. Build routing rules, templates, and letters now, so RSNA demos can plug into your plan.

Operational checklist:

• Map risk thresholds → next steps (annual vs. short-interval, CEM/MRI).
• Standardize templates so risk outputs appear consistently in reports and patient letters.
• Decide who reviews outlier risk flags and how quickly (SLA).

3) Equity you can instrument—not just endorse

RSNA is foregrounding health equity, with sessions on encoding equity in AI and addressing access gaps for underserved communities. Equity becomes real when you can see it in your data: turnaround times by language, missed-appointment patterns by zip code, recall rates by screening site, and AI performance by subgroup. Build those slices into your analytics now; then ask vendors to show subgroup performance in their dashboards.

Practical moves:

• Add demographic and language filters to your TAT and recall reports.
• Require AI vendors to show calibration and error analysis by subgroup.
• Stand up multilingual patient letter templates to support new screening starts at 40. USPSTF

4) CEM/MRI momentum: choose the lever that fits your service line

RSNA coverage calls out CEM as an increasingly practical adjunct—especially useful for dense-breast populations and diagnostic workups where capacity or cost limits MRI. The RACER trial reported higher accuracy and efficiency for CEM as the primary exam for recalled women vs. conventional imaging—evidence that can justify protocol changes and equipment planning. Meanwhile, MRI retains the sensitivity crown, with renewed attention on background parenchymal enhancement (BPE) as a signal worth documenting consistently.

Action items:

• Decide where CEM fits: diagnostic recall pathway, dense-breast supplemental strategy, or both.
• Add BPE level to structured MRI reports and trend it during therapy response clinics.

5) Governance, not guesswork

If personalization is the “what,” governance is the “how.” Use QIBA ideas—claim definitions, acquisition standards, and profile adherence—to control variability across devices and shifts. Tie RSNA learnings to a written governance plan with three parts: 1) protocol book (who owns it, update cadence), 2) quality book (metrics, subgroup views), and 3) AI book (approval process, monitoring, rollback).

6) Where teleradiology extends your capacity

Personalization increases complexity at peaks (recalls, dense-breast seasons, MR backlogs). A teleradiology partner helps you keep individualized pathways moving: standardized templates, subspecialty over-reads, and after-hours coverage that adheres to your risk rules and equity metrics—so “Imaging the Individual” doesn’t stop at 5 p.m.

Headed to RSNA?

Visit Vesta at Booth 1346 (South Hall) to see how we make “Imaging the Individual” work in real clinics—then enter to win a 1-year Medality CME subscription. Don’t wait: email “RSNA CME Entry” to info@vestarad.com now for a reserved entry, and show your confirmation at the booth for a bonus entry.

The post Imaging the Individual — In the Trenches: AI, Personalization & Equity at RSNA 2025 first appeared on Vesta Teleradiology.

Radiomics — the extraction of high-dimensional quantitative features from medical images — allows the characterization of tissue heterogeneity beyond what can be perceived visually. These features, derived from modalities such as CT, MRI, or PET, have been linked to tumor phenotype, gene expression, and therapeutic response across oncology, neurology, and cardiology studies (Springer, 2024).

Imaging Biomarkers in Practice

Validated imaging biomarkers are redefining how clinicians stratify patients, monitor disease, and predict outcomes. Quantitative features from radiomics pipelines can act as noninvasive surrogates for histopathologic or molecular data, guiding therapy selection and prognosis assessment. For instance, radiomic signatures have shown potential in predicting response to immunotherapy and correlating with tumor-infiltrating lymphocytes in non-small cell lung cancer (ScienceDirect, 2020).

In cardiovascular and neuroimaging applications, biomarkers derived from texture and perfusion patterns are being explored to detect subclinical disease, assess ischemic risk, and evaluate treatment efficacy. The promise lies in moving from population averages toward individualized predictions based on each patient’s unique imaging phenotype.

Radiogenomics and Multi-Omics Integration

The next step in precision imaging is radiogenomics — linking imaging phenotypes with genomic and proteomic data to uncover biologically meaningful correlations. Integrating imaging with multi-omics datasets enables the creation of comprehensive disease models that reflect both spatial and molecular dimensions.

Recent reviews highlight the potential of AI-driven multi-omics integration to refine cancer subtyping, prognostication, and therapeutic decision-making (British Journal of Radiology, 2025) and (ScienceDirect, 2025). Federated approaches and multi-modal AI models are emerging to harmonize these heterogeneous datasets while preserving privacy and reproducibility.

Projects such as NAVIGATOR, a regional imaging biobank integrating multimodal imaging with molecular and clinical data, illustrate how research infrastructure is catching up to these ambitions (European Journal of Radiology, 2025).

From Quantitative Imaging to Clinical Translation

Despite the promise, clinical translation remains the critical frontier. Feature reproducibility, acquisition standardization, and regulatory validation continue to challenge adoption (Insights into Imaging, 2020). However, the increasing presence of quantitative imaging biomarkers in prospective trials, along with support from the Quantitative Imaging Biomarkers Alliance (QIBA) and FDA’s digital health framework, signals that this research is crossing the threshold into practice.

At RSNA 2025, expect sessions emphasizing standardization of radiomics workflows, reproducibility metrics, and AI-assisted integration of multi-omics data. Discussions will likely center on how to validate imaging biomarkers in multi-institutional settings and what infrastructure is required for clinical scalability.

The Role of Teleradiology in Precision Imaging

For teleradiology providers like Vesta, these developments offer both opportunity and responsibility. The same digital infrastructure that enables subspecialty coverage across time zones can support quantitative image analysis, data harmonization, and longitudinal tracking — essential foundations for radiomic and biomarker validation.

By aligning with quantitative imaging standards and collaborating with research institutions, teleradiology networks can help bring precision imaging insights into real-world practice — from oncology to cardiovascular disease management.

Precision imaging is not a distant future — it’s the next evolution of radiology happening now.

At RSNA 2025, Vesta will be on site to explore how radiomics, biomarkers, and AI-driven data integration are redefining what it means to truly “image the individual.”

The post Precision Imaging at RSNA 2025: Radiomics, Biomarkers, and the Era of Multi-Omics Integration first appeared on Vesta Teleradiology.