Guidance for AI-assisted structured reporting tools. Also use when the user mentions AI reporting, automated templating, speech-to-report, or wants to configure or optimize AI-assisted radiology reporting systems (RadAI, Abba, DeepRad).

AI Report Assistance

You are an expert in AI-assisted radiology reporting. Your role is to help users configure, integrate, and optimize AI reporting tools.

Supported Platforms

Platform	Focus	Modality
RadAI	Structured reporting automation	CT, X-ray
Abba	Speech recognition + structured reporting	CT, MRI
DeepRad	Multi-modality structured reporting	CT, MRI, X-ray
DeepScribe	Ambient AI documentation	All
ScribeAnywhere	Voice-powered reporting	All

Key Concepts

AI Reporting Workflow

Image → AI Analysis → Finding Detection → Template Population → Radiologist Review → Signed Report

Structured Reporting Benefits

Consistent terminology
Complete documentation
Data extraction for analytics
Quality metrics
Research queries

RadAI Integration

API Configuration

import requests

RADAI_API = "https://api.radai.ai/v1"

def configure_radai(api_key, modality="ct"):
    """Configure RadAI API connection."""
    return {
        "base_url": RADAI_API,
        "headers": {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        },
        "default_modality": modality
    }

def submit_study_for_ai_report(config, study_uid, modality="ct"):
    """Submit study for AI-assisted reporting."""
    response = requests.post(
        f"{config['base_url']}/studies",
        headers=config["headers"],
        json={
            "study_uid": study_uid,
            "modality": modality,
            "report_type": "structured"
        }
    )
    return response.json()

Template Configuration

def configure_template(config, template_type="default"):
    """Configure reporting template."""
    templates = {
        "ct_chest": {
            "sections": ["lungs", "mediastinum", "pleura", "bones", "impression"],
            "required_fields": ["lungs.findings", "impression"],
            "measurement_fields": ["size", "attenuation", "volume"]
        },
        "ct_abdomen": {
            "sections": ["liver", "gallbladder", "pancreas", "spleen", "kidneys", "bowel", "impression"]
        },
        "ct_head": {
            "sections": ["brain", "ventricles", "basal_ganglia", "vessels", "bones", "impression"]
        }
    }
    return templates.get(template_type, templates["ct_chest"])

Retrieve AI Suggestions

def get_ai_suggestions(config, study_id):
    """Get AI-generated report suggestions."""
    response = requests.get(
        f"{config['base_url']}/studies/{study_id}/suggestions",
        headers=config["headers"]
    )
    return response.json()

# Response structure
{
    "study_id": "123",
    "findings": [
        {
            "anatomy": "right_upper_lobe",
            "finding": "nodule",
            "size_mm": 12,
            "location_detail": "RUL",
            "characteristics": {
                "margins": "spiculated",
                "attenuation": "solid"
            }
        }
    ],
    "impression_suggestion": "12mm spiculated nodule in right upper lobe, suspicious for malignancy.",
    "confidence": 0.89
}

Abba Integration

Speech Recognition Setup

def configure_abba(api_key, specialty="radiology"):
    """Configure Abba speech recognition."""
    return {
        "base_url": "https://api.abba.ai",
        "headers": {
            "Authorization": f"Bearer {api_key}"
        },
        "specialty": specialty,
        "format": "structured"
    }

def transcribe_dictation(config, audio_file):
    """Transcribe dictation with structured output."""
    with open(audio_file, "rb") as f:
        files = {"audio": f}
        response = requests.post(
            f"{config['base_url']}/transcribe",
            headers=config["headers"],
            files=files,
            data={"specialty": config["specialty"]}
        )
    return response.json()

DeepRad Integration

Multi-Modality Configuration

def configure_deeprad(api_key):
    """Configure DeepRad for multi-modality."""
    return {
        "base_url": "https://api.deeprad.ai",
        "api_key": api_key,
        "modalities": ["ct", "mri", "xray", "pet"]
    }

def get_structured_report(config, study_data, modality):
    """Get structured report for any modality."""
    response = requests.post(
        f"{config['base_url']}/report/{modality}",
        headers={"Authorization": f"Bearer {config['api_key']}"},
        json=study_data
    )
    return response.json()

Template Types

By Modality

Modality	Template Type	Key Elements
CT Chest	Lung-RADS	Nodule tracking, comparison
CT Abdomen	LI-RADS	Liver lesion assessment
CT Head	No specific	Hemorrhage, stroke
MRI Prostate	PI-RADS	PI-RADS scoring
MRI Liver	LI-RADS	LI-RADS scoring
Mammography	BI-RADS	Assessment categories
X-ray Chest	No specific	Critical findings

Template Structure

STANDARD_TEMPLATE = {
    "header": {
        "patient_id": "required",
        "study_date": "required",
        "accession": "required",
        "modality": "required",
        "clinical_history": "required"
    },
    "findings": {
        "anatomy": "free_text",
        "finding": "structured",
        "size": "measurement",
        "location": "structured",
        "characteristics": "structured"
    },
    "impression": {
        "primary": "required",
        "secondary": "optional",
        "recommendations": "optional"
    }
}

Integration with PACS

Workflow Integration

def setup_pacs_integration(pacs_url, ai_platform="radai"):
    """Set up PACS integration for AI reporting."""
    integration = {
        "pacs": {
            "url": pacs_url,
            "auto_submit": True,
            "receive_results": True
        },
        "ai_platform": ai_platform,
        "workflow": {
            "auto_populate": True,
            "require_review": True,
            "sign_immediately": False
        }
    }
    return integration

Auto-Populate Configuration

def configure_auto_populate(settings):
    """Configure auto-population behavior."""
    return {
        "populate_findings": settings.get("findings", True),
        "populate_impression": settings.get("impression", True),
        "populate_measurements": settings.get("measurements", True),
        "highlight_changes": settings.get("highlight_changes", True),
        "require_acknowledgment": settings.get("require_ack", True)
    }

Optimization Strategies

High Volume Practice

HIGH_VOLUME_CONFIG = {
    "auto_accept_normal": True,  # Accept normal AI reports
    "auto_populate": True,
    "require_review_abnormal": True,
    "batch_processing": True,
    "templates": "standardized"
}

Quality Focus

QUALITY_FOCUSED_CONFIG = {
    "auto_accept_normal": False,
    "auto_populate": True,
    "require_review_all": True,
    "double_read_option": True,
    "templates": "comprehensive"
}

Best Practices

Start with standardized templates - Ensure consistency
Enable auto-population gradually - Train radiologists on workflow
Monitor accuracy - Track AI vs final report differences
Customize templates - Adapt to your practice patterns
Regular review - QA AI suggestions periodically

Troubleshooting

Issue	Solution
AI not submitting	Check PACS integration
Slow responses	Enable caching
Incorrect findings	Retrain with local data
Template mismatch	Update template mapping

Related Skills

structured-reporting: For report template details
pacs-workflow: For PACS integration
ai-quality-review: For AI output QA
radiology-report-analysis: For report analysis

Examples

Example 1: Enable AI Reporting

Enable AI-assisted reporting for CT chest studies using RadAI

Configuration:

config = configure_radai(api_key="your-key", modality="ct")
template = configure_template(config, "ct_chest")

Example 2: Review AI Suggestions

Review AI suggestions for study ACC123

suggestions = get_ai_suggestions(config, "ACC123")
# Present to radiologist for review
# Accept or modify suggestions

ai-report-assist