Discover AI Models for Healthcare

Access cutting-edge pre-trained AI models for medical imaging, diagnostics, and healthcare analytics. Deploy with confidence using our curated collection.

500+ Models
Ready to Deploy
futuristic medical AI dashboard with neural networks, healthcare technology, blue and cyan color scheme, modern interface
99.2% Accuracy
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Model Categories

Specialized AI models for every healthcare domain

Medical Imaging

X-ray, MRI, CT scan analysis models for accurate diagnosis

120+ Models

Clinical NLP

Extract insights from clinical notes and medical records

85+ Models

Predictive Analytics

Risk assessment and outcome prediction models

95+ Models

Genomics

DNA sequence analysis and genetic variant detection

65+ Models

Drug Discovery

Molecular property prediction and drug interaction models

75+ Models

Patient Monitoring

Vital signs analysis and anomaly detection models

60+ Models

Featured Models

Top-performing models trusted by healthcare professionals

chest x-ray medical scan with AI overlay, diagnostic imaging, medical technology

ChestX-Ray Classifier

Verified

Detects 14 pathologies in chest X-rays with 99.1% accuracy

12.5K downloads 4.9
brain MRI scan with AI analysis, neuroimaging, medical AI diagnostic tool

Brain Tumor Detector

Popular

Advanced MRI analysis for brain tumor detection and classification

8.2K downloads 4.8
ECG heart rhythm analysis, cardiac monitoring AI, medical waveform analysis

ECG Rhythm Analyzer

New

Real-time ECG analysis for arrhythmia detection

5.7K downloads 4.7

Easy Integration

Deploy AI models in your healthcare applications with just a few lines of code. Our REST APIs and SDKs make integration seamless.

REST API

Simple HTTP requests for model inference

SDK Support

Python, JavaScript, and Java libraries

HIPAA Compliant

Enterprise-grade security and compliance

# Deploy a model with our Python SDK
from abhidoc_ai import ModelZoo
import numpy as np
# Initialize client
client = ModelZoo(api_key="your_api_key")
# Load model
model = client.load("chest-xray-classifier")
# Make prediction
result = model.predict(image_data)
print(result)

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