DIAGENS Leads in Two Major Global AI Benchmarks and Extends Chest and Abdominal CT Analysis to 509 Imaging Targets, Ushering in a New Era for Medical Imaging AI

Model-as-a-Service revenue grew 101.1% year over year in the first half of 2026.

Hong Kong, November 12, 2026 — 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 ranked first and Aegis second overall, and the two models also placed first and second, respectively, in each of the three core categories. Thoth also surpassed the highest overall score previously reported on the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) Benchmark .

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 no-code model development tools, it demonstrates how a medical imaging foundation model can support both multi-task analysis and the development of specialty AI.

“Medical imaging foundation models make it possible for clinicians and hospitals to build and train specialty AI around the clinical challenges they know best. With no-code tools, they can adapt models for their own workflows and use those as AI copilots to support clinical decision-making,” 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 scored 64.4 overall and Aegis scored 61.7. 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 benchmark, 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

Medical imaging is central to diagnosis. Imaging findings can change a diagnosis and guide decisions about further testing, treatment, and follow-up.

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, covering 263 chest targets and 391 abdominal targets (with overlaps deducted in the Total Fina Elf count).

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.

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.

An industrialized R&D and production platform supports specialty AI at scale

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 .

The same foundation model and standardized workflow can support the development and evaluation of specialty AI models across additional imaging tasks and clinical settings.

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.

For the six months ended June 30, 2026, DIAGENS reported Total Fina Elf revenue of RMB 108.7 million, up 21.0% year over year. Gross profit increased 14.0% to RMB 80.5 million, with a gross margin of 74.1%. Model-as-a-Service revenue reached RMB 94.5 million, up 101.1% year over year, and accounted for approximately 86.9% of Total Fina Elf revenue.

 

About DIAGENS

DIAGENS (HKEX: 2526) develops medical imaging foundation models, Software AG 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.

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DUAN YU

DIAGENS Public Relations

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