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AI Can Be Friend or Foe in Improving Health Equity. Here is How to Ensure it Helps, Not Harms
- Healthcare inequities and disparities in care are pervasive across socioeconomic, racial and gender divides.
- AI is already helping to streamline care delivery, enable personalized medicine at scale, and support breakthrough discoveries.
- Inherent bias in the data, algorithms, and users could worsen the problem.
- Those who develop and deploy AI-driven healthcare solutions must be careful to prevent AI from unintentionally widening existing gaps.
- AI can help ensure inclusiveness, reduce harm, and optimize outcomes by analyzing population data and flagging disproportional representation or gaps in demographic coverage.
- AI can help root out implicit bias and suggest care pathways that may have previously been overlooked.
- AI can forecast health risks for underserved populations and enable personalized risk assessments to better target interventions.
- AI has incredible potential to accelerate workflows behind the scenes to reduce disparities, making care more affordable and accessible.
- We must integrate all of this data so that key pieces are included, regardless of formatting or source.
- AI practitioners have a responsibility to conduct bias audits, build diversity assessments into our AI development process, and understand how the model makes decisions.
- Organizations must work together to set standards and frameworks for data exchange and acuity to guard against bias, which can help democratize access to complete, accurate patient data.
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