A recent study published in the Journal of Medical Internet Research introduced the EDAI Framework, a comprehensive guideline designed to integrate equity, diversity and inclusion (EDI) principles throughout the artificial intelligence (AI) lifecycle. Led by Dr. Samira Abbasgholizadeh-Rahimi, PhD, Canada Research Chair (Tier II) in AI and Advanced Digital Primary Health Care, the research fills an important gap in current development and implementation practices of AI in healthcare and oral health, which often overlook critical EDI factors. With EDAI, AI developers, policymakers, and healthcare providers now have a roadmap to ensure that AI systems are not only technologically sound, but also socially responsible and accessible to all.
Through a three-phase research approach, including a systematic literature review and two international workshops bringing together more than 60 experts and community representatives, the research team identified essential EDI indicators to integrate at each stage of the cycle life of AI, from data collection to deployment. Co-designed with input from diverse voices, this framework puts inclusion at the forefront, ensuring that AI in healthcare and oral health reflects a range of perspectives and serves everyone more equitably and responsibly .
Today’s AI systems are often mirrors reflecting our societal biases rather than windows into a more equitable future. To use the power of AI for the good of society, we must ensure we use frameworks like EDAI to integrate EDI into its lifecycle. Only then can we turn these powerful tools into bridges that connect and uplift everyone, not just a privileged few. »
Dr. Samira Abbasgholizadeh-Rahimi, PhD, holder of the Canada Research Chair (Tier II) in AI and advanced digital primary health care
The study funded by the Canadian Institutes of Health Research (CIHR) and the Fonds de recherche du Québec (FRQ) network, that is to say the Oral Health Research Network (RSBO), shows that integrating EDI principles into AI goes well beyond just checking a box; it’s about tackling deeper biases within systems and organizations that can prevent AI from truly working for everyone. For example, the EDAI framework can be used by AI developers to design diagnostic tools that take demographic and cultural diversity into account. Developers can ensure that datasets include diverse populations, allowing AI to provide accurate diagnoses on various demographics and avoid biases that have traditionally affected certain groups.
Similarly, when designing AI for healthcare management (such as scheduling or resource allocation), using the EDAI framework during design could ensure equitable healthcare by optimizing these systems to prioritize underrepresented or underserved communities. For example, using EDAI, an AI-based patient scheduling system could be carefully developed and implemented with EDI principles in mind to identify underserved communities and marginalized groups facing accessibility problems and facilitate access to care for these populations.
In addition to providing practical steps and guidance, the EDAI framework highlights both the barriers and enablers that can affect how EDI principles are integrated, giving developers and policymakers the information needed to address challenges. challenges and strengthen the impact of the framework. This initiative sets the stage for a new standard in AI development and implementation, redefining how AI can improve oral health and care for everyone, regardless of their background or experience. circumstances.
