ÑÇÖÞAV

ÑÇÖÞAV has been awarded funding from the National Institutes of Health (NIH) through its .

This initiative encourages more people from historically underrepresented groups in researching and developing of artificial intelligence (AI) and machine learning (ML) models. AIM-AHEAD aims to leverage the growing volume of data generated through electronic health records (EHR) and other biomedical research to address health disparities and inequities.

The newly funded project, led by Janusz Wojtusiak, builds upon initiative aimed at building artificial intelligence tools for collecting, assessing and analyzing injury data. The new funding specifically addresses the problem of measuring equity and quality of imaging documentation.

In addition to Wojtusiak, an interdisciplinary research team comprising Katherine Scafide and David Lattanzi is joined by Health Informatics Assistant Professor Eman Elashkar, Research Assistant Professor Jesse Kirkpatrick, who is also the acting director of the Institute for Philosophy and Public Policy, and Amin Nayebi Nodoushan, a postdoctoral researcher at Mason.

Their research focuses on using AI methods combined with Alternate Light Sources (ALS) to improve bruise detection, addressing visibility issues for individuals with darker skin tones who often encounter challenges in accurately assessing injuries sustained from violence.

Current literature highlights that skin color significantly influences the accuracy of AI-based tools in healthcare. Studies have documented disparities in the performance of medical devices, such as pulse oximeters and smartwatches, which frequently yield inaccurate readings for individuals with darker skin. These discrepancies can lead to delays in critical medical interventions, exacerbating existing health disparities.

The proposed research will focus specifically on bruises, the most common type of soft tissue injury experienced by victims of intimate partner violence (IPV). Statistics indicate that approximately one in three people in the U.S. have experienced IPV, with racial minorities reporting disproportionately higher rates. Survivors with darker skin tones have noted that their bruises are often invisible, resulting in significant delays in seeking necessary medical care.

Leveraging the innovative use of ALS, the George Mason research team has demonstrated marked improvement in bruise visibility across diverse skin tones. The team aims to develop methods that ensure that AI-based tools provide equitable and unbiased detection and characterization of injuries. This will involve creating combined technical-ethical metrics to assess the performance of these tools across different skin tones. Engaging diverse stakeholders, including clinicians, forensic nurses, and community representatives, will be essential in the development process to align with ethical practices in AI.

The research team’s two primary aims are to develop metrics that assess equity in AI tools and apply these metrics to improve bruise detection models. They have already collected a substantial dataset of bruise images taken under various lighting conditions, which will be utilized to enhance the AI models’ performance. The interdisciplinary nature of the research team, comprising informaticians, engineers, clinicians, and ethicists, ensures a comprehensive approach to tackling these complex issues.

In alignment with AIM-AHEAD’s goals, this research initiative promises to contribute significantly to addressing health inequities and enhancing the capabilities of AI in healthcare. By focusing on the specific needs of underrepresented communities, the George Mason research team is paving the way for more equitable health care solutions, ultimately aiming to improve the accuracy and efficacy of injury assessments across diverse populations.

This project is led by George Mason’s in collaboration with the . More information on the project can be found at .