AI in Biomedicine: Health Equity Perspectives
Author Information
Author(s): Kumar Deepak, Malin Bradley A., Vishwanatha Jamboor K., Wu Lang, Hedges Jerris R.
Primary Institution: The Julius L. Chambers Biomedical/Biotechnology Research Institute, North Carolina Central University
Hypothesis
How can AI tools be developed to advance health equity in healthcare?
Conclusion
AI has the potential to improve health equity, but it requires careful consideration of bias and inclusivity in data.
Supporting Evidence
- AI can predict patient outcomes and improve healthcare delivery.
- The AIM-AHEAD initiative aims to reduce health disparities by integrating social determinants of health into AI models.
- RCMI institutions play a critical role in promoting AI equity and inclusivity.
Takeaway
This study talks about how AI can help make healthcare fairer for everyone, especially for people who usually don't get good care.
Methodology
The study used AI tools to synthesize and analyze content from a conference session on AI and health equity.
Potential Biases
Potential biases in AI models due to under-representation of minority populations in training datasets.
Limitations
The report is based on a conference session and does not include original research data.
Participant Demographics
Experts from diverse institutions, including minority-serving institutions.
Digital Object Identifier (DOI)
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