A Multi-Center Benchmark for Abdominal Disease Diagnosis and Report Generation from Non-Contrast CT
arXiv:2606.16991v1 Announce Type: cross Abstract: Multiphasic contrast-enhanced CT (CECT) is widely used for abdominal lesion characterization, yet it carries inherent risks of contrast-induced nephropathy, escalates acquisition burden, and heavily contributes to radiologist workload. To address these challenges, we introduce a novel multi-center benchmark for multi-organ abdominal disease diagnosis and automated radiology report generation, which learns to synthesize contrast-enhanced findings from single-phase non-contrast CT (NCCT). To support this, we curated a large-scale dataset of paire
The increasing maturity of AI in medical imaging and the persistent challenges of healthcare efficiency and patient safety drive the development of AI solutions for medical diagnosis.
This development represents a significant step towards reducing patient exposure to contrast agents and easing radiologist workload, which can improve healthcare accessibility and cost-effectiveness.
AI models can now generate contrast-enhanced findings from non-contrast CT scans, potentially making complex diagnoses safer and more efficient.
- · AI healthcare tech companies
- · Hospitals and clinics
- · Patients
- · Medical imaging hardware manufacturers
- · Contrast agent manufacturers (long-term)
- · Traditional radiology workflow providers
Reduced need for contrast agents in abdominal CT scans.
Faster and more accessible diagnostic imaging, potentially lowering healthcare costs and increasing throughput.
Increased reliance on AI for diagnostic interpretations, potentially shifting the role of radiologists towards oversight and complex case review.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG