In a groundbreaking advancement set to transform clinical approaches to intimate partner violence (IPV), a multidisciplinary research team funded by the National Institutes of Health (NIH) has engineered a sophisticated machine learning tool that significantly enhances the early detection of IPV risk among patients. This cutting-edge artificial intelligence (AI) model harnesses routine healthcare data, enabling a proactive identification of at-risk individuals—a development with profound implications for public health and patient safety.
Intimate partner violence, defined as abuse from current or former partners, remains a pervasive and insidious public health crisis in the United States, impacting millions irrespective of gender. Its manifestations often result in severe physical trauma, chronic pain, and debilitating mental health conditions. However, despite its prevalence, IPV frequently eludes clinical detection due to patients’ reluctance to disclose abuse, driven by fear, stigma, and concerns over personal safety, thus creating a critical need for enhanced diagnostic tools.
To address this gap, the research consortium, led by Harvard Medical School alongside experts in biomedical imaging and clinical informatics, innovated and rigorously tested three AI-driven models tailored to identify IPV risk using diverse types of medical data. These models were trained on structured data, such as demographic and clinical variables presented in tabulated formats, unstructured data retrieved from narrative medical notes—including radiology reports—and a novel multimodal fusion approach that integrates both data modalities at the predictive stage.
Uniquely, the multimodal model demonstrated superior performance, achieving an accuracy of 88% in predicting IPV risk within data derived from nearly 6,000 female patient cases. This hybrid model surpassed the predictive capabilities of models relying exclusively on either tabular or narrative data, underscoring the synergistic value of combining quantitative and qualitative healthcare information streams. The study notably highlights the strength of radiological imaging as a diagnostic resource, given radiologists’ adeptness at recognizing injury patterns characteristic of IPV.
One of the most remarkable facets of this technology is its capability for early identification: both the tabular data model and the fusion model predicted IPV risk an average of more than three years prior to patients’ enrollment in hospital-based domestic violence intervention programs. The tabular model exhibited a slight advantage in earlier detection, but the integrated multimodal model excelled in identifying more at-risk cases overall, offering a more comprehensive screening tool.
Technically, the framework separates the processing of structured and unstructured inputs before merging predictions, which accounts for its robust and stable performance across heterogeneous clinical datasets from varied healthcare settings. This method addresses challenges associated with inconsistent availability and recording practices of unstructured clinical data, lending the system adaptability and scalability within the complex ecosystem of medical institutions.
The implications of embedding such AI tools directly into electronic health record (EHR) systems are profound. Real-time risk assessments can alert healthcare providers during routine care visits, facilitating timely, sensitive conversations with patients who may be vulnerable yet hesitant to self-disclose abuse. Importantly, the tool is designed as a decision support system rather than a diagnostic authority—it aids clinicians in navigating delicate interactions and connecting patients with appropriate community and therapeutic resources without coercion.
Senior author Dr. Bhati Khurana, an emergency radiologist at Mass General Brigham and associate professor at Harvard Medical School, emphasizes the paradigm shift introduced by this technology. Traditional IPV screening has relied heavily on patient disclosure, often reactive and insufficient. This AI-led proactive approach capitalizes on pre-existing clinical data patterns, fostering earlier interventions that can prevent chronic harm and break the cycle of violence more effectively.
In addition to its clinical utility, the research team has prioritized ethical implementation by developing comprehensive guidance for clinicians on engaging with patients using insights generated by the tool. These guidelines emphasize patient-centered communication strategies that prioritize safety, confidentiality, and support—ensuring the technology serves as a facilitator of compassionate care.
Looking forward, the research trajectory includes integrating these AI models with clinical decision support systems at scale, enabling frontline healthcare personnel to leverage machine intelligence seamlessly in daily practice. This integration promises not only to improve IPV risk detection but also to incorporate predictive analytics into broader risk assessment frameworks for complex health and social issues.
The study’s methodological rigor is anchored in a substantial dataset spanning several years, encompassing 850 IPV-affected female patients matched against 5,200 controls by clinical and demographic parameters. The application of multimodal machine learning represents a remarkable milestone in translating complex clinical data into actionable insights, demonstrating the power of AI to confront deeply entrenched societal challenges within healthcare contexts.
Moreover, the technological architecture respects variability in hospital data ecosystems, a critical consideration given the disparate healthcare infrastructures across regions. This flexibility ensures that the tool can maintain accuracy and functionality even where unstructured clinical narratives may be inconsistently documented, lowering barriers to broad adoption.
Ultimately, this innovation heralds a transformative era for public health and clinical medicine, where predictive analytics empower healthcare providers to intervene with foresight. By moving beyond traditional reactive frameworks, this AI-driven tool could significantly reduce the incidence and consequences of IPV, enhancing the safety and well-being of millions potentially affected by this invisible epidemic.
The research received co-funding from the NIH National Institute of Biomedical Imaging and Bioengineering and the NIH Office of the Director, highlighting its strategic importance within the federal initiative to integrate AI into healthcare innovation. The full study, published in npj Women’s Health, provides detailed insights and is accessible alongside resources intended to support clinicians in the ethical use of these tools.
Subject of Research: Artificial Intelligence Applications in Healthcare, Intimate Partner Violence Risk Prediction
Article Title: Leveraging Multimodal Machine Learning for Accurate Risk Identification of Intimate Partner Violence
News Publication Date: March 13, 2026
Web References: https://bhartikhurana.bwh.harvard.edu/airs/; https://www.cdc.gov/intimate-partner-violence/about/index.html
References: Gu J, Villalobos Carballo K, Ma Y, Bertsimas D, and Khurana B. Leveraging multimodal machine learning for accurate risk identification of intimate partner violence. npj Women’s Health. 2026. DOI: 10.1038/s44294-025-00126-3
Keywords: Artificial intelligence, Machine learning, Intimate partner violence, Risk assessment, Clinical decision support, Biomedical imaging, Data fusion, Predictive analytics
Tags: AI in clinical informaticsbiomedical imaging in violence detectionearly identification of domestic violencehealthcare data analysis for abuseintimate partner violence detection AImachine learning for IPV riskmental health impact of IPVmultidisciplinary research on IPVNIH-funded AI health projectsovercoming patient disclosure barrierspredictive models for patient safetypublic health technology innovation
