# DIAGENS Leads Medical AI Benchmarks and Extends Chest and Abdominal CT Analysis to 509 Imaging Targets

- Link: https://www.thailand-business-news.com/pr-news/diagens-leads-medical-ai-benchmarks-and-extends-chest-and-abdominal-ct-analysis-to-509-imaging-targets
- Published: 2026-11-12T21:46:00+07:00
- Author: PR Newswire

iMed-6.0 brings multimodal reasoning to medical imaging and powers a multi-task 
CT model, while zero-code tools support specialty AI development.

HONG KONG, Nov. 12, 2026 /PRNewswire/ — DIAGENS (HKEX: 2526) today announced benchmark
results for iMed-6.0, the latest generation of its iMedImage® medical imaging foundation
model, and introduced a CT model designed to analyze 509 diseases and imaging findings.
In MedBench’s multimodal API evaluation, iMed-6.0 Thoth and Aegis took first and
second place, respectively, overall and in all three core categories. Thoth also
surpassed the highest overall score previously reported on the Massive Multi-discipline
Multimodal Understanding and Reasoning Benchmark (MMMU) Test.

The CT model, developed using iMed-6.0 Aegis, applies foundation-model capabilities
to hundreds of chest and abdominal CT analysis tasks. Together with the company’s
zero-code model development tools, it demonstrates how a medical imaging foundation
model can support both multi-task analysis and the development of specialty AI.

"We want clinicians and hospitals to build AI around the clinical challenges they
know best. With a medical imaging foundation model and zero-code tools, they can
train specialty models, adapt them to their workflows, and use them as copilots 
to support clinical decisions," said Dr. Ning Song, Chairman and CEO of DIAGENS.

**Leading results in medical and multidisciplinary evaluation**

MedBench is developed by Shanghai AI Laboratory and forms part of the OpenCompass
evaluation ecosystem.

In MedBench’s multimodal API evaluation, Thoth and Aegis earned overall scores of
64.4 and 61.7, respectively. Both models ranked in the top two for medical visual
perception and text extraction, cross-modal semantic understanding and reasoning,
and clinical decision support and reasoning. In medical visual perception and text
extraction, Thoth and Aegis scored 88.8 and 84.9. These were 20.3% and 15.0% higher,
respectively, than the highest score of 73.8 among the other evaluated models.

On the MMMU Test, a multimodal academic benchmark spanning six broad domains and
30 subjects, Thoth achieved 66.2% overall accuracy, exceeding the previously published
best result of 65.9%. In the Health & Medicine category, Thoth and Aegis achieved
74.9% and 71.1% accuracy, respectively, placing first and second in the benchmark
evaluation.

**One model for 509 CT Imaging Targets**

The CT model is designed for a defined imaging setting: non-contrast and contrast-
enhanced CT examinations of the chest and abdomen. Within this application, it supports
automated identification of 509 distinct diseases and imaging findings—263 chest
targets and 391 abdominal targets, with overlapping targets counted once.

In an internal validation using nearly 40,000 CT examination–report pairs from multiple
hospitals, the model achieved a macro-averaged AUC of 0.855 for zero-shot classification
across all 509 targets. For a subset of 249 key targets represented in internationally
available public benchmarks, the model achieved a macro-averaged AUC of 0.930.

**A repeatable process for building specialty AI**

iMed-6.0 processes 3D imaging data, whole-slide pathology images, and temporal imaging
data. It supports image interpretation, analysis of anatomy and lesions, and report
drafting and review. For supported tasks, outputs can be linked back to specific
slices or regions in the source images.

DIAGENS combines these capabilities with iMedLoop™, its medical imaging AI development
platform. The platform connects data preparation, model training, evaluation, deployment,
and feedback. iMedMaaS® democratizes medical AI by enabling clinicians and hospitals
to train, deploy, and scale custom models via a zero-code interface.

As of June 30, 2026, DIAGENS had undertaken 158 model projects and established R&
D partnerships with 99 hospitals. Its products and services reached more than 400
healthcare institutions. The company’s approach provides clinical teams with a repeatable
process for developing models tailored to the imaging tasks they prioritize.

**About DIAGENS**

DIAGENS (HKEX: 2526) develops medical imaging foundation models, software platforms
for specialty AI development, and medical imaging AI products. Its iMedImage model
and iMedLoop platform support workflows from imaging data preparation and model 
training to evaluation, deployment, and feedback. The company works with hospitals
and research institutions to develop AI for specific imaging tasks.

diagens.com

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