Digital

Health

Review

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About this researcher - led project:

Healthcare is rapidly going digital and AI models are being trained on biased data.

The spread of AI in healthcare is increasing. It is our belief that epistemic injustice , the construction of biased medical research, data and practices , should be examined to better understand adverse impacts on communities as these data will inevitably influence existing and future training models.

“Ideas are easy. Implementation is hard."

Our Approach

Our research marries institutional archival histories with responsible use of AI practices in public health. This institutional historic work is meant to highlight epistemic injustice within our medical research and educational system as a source of inequitable and disparate health outcomes.

The conversation on responsible use of AI in healthcare underscores that continued use of data models built upon a platform of epistemic injustices can serve to exacerbate health inequities - burdening our medical system and missing investable opportunities due to lack of insight.

We aim to increase clarity on how to move forward in the use of AI within the framework of epistemic injustice.

This work will focus on researching:

  • Use cases of specific medical research practices and affected communities
  • Examination of health policies and laws
  • Data examination and modeling
  • Financial opportunities and market value in supporting equitable an responsible AI
  • Report and guidelines on AI model bias reduction

Every contribution matters. Make your opinions and expertise heard. Join us in this project!

This is unprecedented work to examine the level of epistemic injustice as it pertains to health AI expansion.

This project was made possible by a multi institutional and industry consortium. As we embark upon this work, we will collaborate with established national institutions, initiatives and experts on medical research and health AI.  

To launch this collaboration, we are proudly partnered with academic research, data modeling and review institutions.

All photography sourced from Pexels and Unsplash and falls under the Pexels License and Unsplash License.

Contributing institutions include:

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