YiXueRenGongZhiNengKaiFangPingTai

Overview

The Medical AI Open Platform (Medical Truth), developed by Yizhen Technology, is a comprehensive one-stop AI-powered capability hub for the healthcare industry. It provides medical institutions, research teams, and developers with a full chain of tools and services—from data annotation and model training to clinical deployment—supporting intelligent transformation at every step. The platform integrates core AI capabilities, including medical image recognition, pathology analysis, speech transcription, and knowledge graphs, to drive the practical adoption of AI across diverse medical scenarios.

The platform provides standardized APIs and intuitive low-code workflows, enabling rapid integration with third-party systems. It also features a built-in library of medical-specific pre-trained models, covering a broad spectrum of conditions such as pulmonary nodules, fundus pathologies, and cardiac abnormalities. Additionally, built-in federated learning and privacy-computing capabilities help ensure data security and regulatory compliance. With these offerings, developers can build customized diagnostic assistance tools, clinical decision support (CDSS) applications, and other intelligent healthcare solutions.

Core capabilities

Medical imaging analysis

Supports lesion detection and segmentation across CT, MRI, and X-ray multimodal images.

Clinical decision support

Provides diagnostic recommendations and medication suggestions based on knowledge graphs and deep learning.

Low-code development

Visual workflow orchestration for rapidly building AI medical applications.

Data annotation platform

Offers professional medical data labeling and quality control tools.

Federated learning

Supports multi-party collaborative training while protecting patient privacy.

Model management

Centralizes version control, deployment, and monitoring, with one-click service publishing.

Multi-tenant architecture

Meets the independent usage needs of hospitals, departments, and research teams.

Use cases

CT image-assisted detection of pulmonary nodules to improve early lung cancer screening efficiency.

Intelligent analysis of fundus photos to aid in diagnosing diabetic retinopathy.

AI interpretation of ECGs to automatically identify arrhythmias and myocardial ischemia.

Pathology slide image analysis to assist in tumor grading and classification.

Structured processing of clinical records to build patient profiles and risk predictions.

Integrating AI capabilities into hospital information systems for smart triage and pre-consultation.

Prediction of molecular structure and activity in drug discovery.