Researchers created an AI tool leveraging clinical and radiology data to accurately predict patients at risk of intimate partner violence (IPV).
Key Details
- 1NIH-funded study trained AI models on structured clinical data and unstructured text, including radiology reports.
- 2Models evaluated data from nearly 850 IPV-affected female patients and 5,200 matched controls.
- 3Three models were developed: structured data only, unstructured text only, and multimodal (fusion) combining both.
- 4The fusion model achieved 88% accuracy and greater stability, outperforming single-modality models.
- 5Both tabular and fusion models predicted IPV risk an average of three years before intervention center enrollment.
- 6Emergency radiologist Bhati Khurana was senior author; further clinical integration is planned via EMR tools.
Why It Matters
Proactive AI-based IPV risk identification can significantly improve early intervention and patient outcomes, with radiology playing a key role in detection. This approach illustrates how imaging and informatics can address public health challenges beyond traditional diagnostics.
Source
EurekAlert
Related News
•EurekAlert
AI-Powered OCT Enables Rapid 'Optical Biopsy' for Early Endometrial Cancer Detection
A team at Washington University has developed a catheter-based 3D OCT system with AI to quickly and noninvasively detect early endometrial cancers.
•EurekAlert
AI Clinical Reasoning in Diagnostics and Digital Fatigue in Healthcare
Recent JMIR features explore large language models in clinical diagnostics and digital fatigue among healthcare professionals.
•EurekAlert
KAIST, MIT, Microsoft Develop Efficient AI Image Upsampling for Robotics
KAIST, MIT, and Microsoft have created 'Upsample Anything,' a training-free AI method to restore high-resolution visual data from compressed images with up to 16x improved GPU memory efficiency.