Addressing the diversity gap in healthcare AI development requires strategies at multiple levels. As AI ethicists Auxane Boch and Alexander Kriebitz from the Technical University of Munich suggest, "Building diverse teams can be achieved through mentoring programs and actively seeking representation from intended users and stakeholders. Overall, diversity throughout the AI lifecycle leads to better-designed, ethical, and inclusive AI tools that address the needs of diverse populations while mitigating biases and inadequate implementations.”
Beena Ammanath, Global Deloitte AI Institute Leader, emphasizes an approach to AI team diversity in her Forbes article, emphasizes an approach to AI team diversity, "It's important to recognize that stakeholders are not restricted to data scientists, AI engineers and other professionals engaged in an AI model's technical development. I believe that just as important are project managers, line of business users, quality assurance professionals, executives and people from across the organization.”
The pathway to diverse AI teams begins with educational opportunities:
Healthcare organizations and AI companies must implement strategies to attract and retain diverse talent:
Beyond hiring practices, organizations can implement collaborative models that incorporate diverse perspectives. As noted by Boch and Kriebitz, "Technical expertise is essential, but diverse perspectives provide unique viewpoints on data collection, use, and privacy, for example. Involving human and social scientists as well as individuals from diverse training and life paths backgrounds throughout the AI lifecycle promotes inclusivity, ethical considerations, and targeted development for different populations.”
These collaborative models can include:
Several examples demonstrate how diverse teams have created more equitable healthcare AI systems:
Researchers at Stanford University developed the Diverse Dermatology Images (DDI), which included a diverse team of dermatologists, computer scientists, and patient advocates recognized a gap in existing dermatology AI systems: most skin disease datasets predominantly featured light-skinned patients. This gap reflects broader disparities in dermatology care, where patients with darker skin tones often face delayed diagnoses, resulting in increased morbidity, mortality, and healthcare costs.
Research published in Science Advances in August 2022 demonstrated that state-of-the-art dermatology AI models performed substantially worse on lesions appearing on dark skin compared to light skin. Their study showed:
Before developing their diagnostic algorithm, the startup team took several innovative approaches:
The resulting diagnostic system demonstrated smaller performance gaps between skin types compared to competing algorithms developed by less diverse teams. This equitable performance has led to broader clinical adoption, including in healthcare settings serving predominantly minority populations. The system helps address the concerning disparity where patients with skin of color are often diagnosed with skin cancer at later stages due to existing biases in dermatological care.
A health system developing a readmission risk prediction algorithm assembled a diverse development team including data scientists, clinicians from various specialties, social workers, and community health workers. They also created a patient advisory board with representation from the communities served by the health system.
ChristianaCare, a major healthcare organization, recognized that achieving health equity was essential to delivering quality care, particularly as COVID-19 had highlighted disproportionate infection and morbidity rates for communities of color. However, the organization faced several challenges in their data infrastructure:
As noted by Dr. Ed Ewen, Director of Clinical Data and Analytics at ChristianaCare's Center for Strategic Information Management, "Health equity and AI are interconnected. Technology and AI need to help reduce health disparities, not exacerbate them."
To address these challenges, the organization implemented a multi-faceted approach:
The diverse team approach and data strategy enabled ChristianaCare to identify specific opportunities to improve health equity:
The team also identified areas of current health equity that could be monitored, such as hemoglobin A1c control among diabetic patients and blood pressure management among patients being treated for hypertension.
In response to their findings, ChristianaCare implemented targeted interventions, including innovative clinics that combined virtual primary care and COVID-19 testing in some of Wilmington's underserved communities.
The resulting algorithm demonstrated superior performance compared to traditional readmission models, particularly for patients from underserved communities. By incorporating social determinants of health identified by the patient advisory board and implementing culturally appropriate data collection methods designed by community health workers, the model captured a more complete picture of readmission risk factors.
Natural language processing models can be trained on multilingual datasets to reduce language bias and improve patient outcomes.
Protecting patient data is critical, requiring robust encryption, data minimization, and transparent consent processes.
They can form partnerships with academic institutions, leverage open-source tools, and participate in collaborative research networks.
Poor data quality can introduce biases, making it essential to ensure accurate, complete, and representative datasets.
Telemedicine, mobile diagnostics, and AI-driven decision support systems can bridge healthcare access gaps in underserved areas.