Exploring Gender Bias in AI for Personalized Medicine: Focus Group Study With Trans Community Members

Area of Science:

• Health Informatics
• Artificial Intelligence
• Precision Medicine

Background:

• Personalized medicine advancements using AI often overlook the trans community.
• Tailoring AI health solutions to unique trans health needs is under-researched.
• Ensuring inclusivity in precision medicine requires addressing this demographic gap.

Purpose of the Study:

• Identify challenges and solutions for AI in trans personalized medicine.
• Promote a trans-inclusive, multidisciplinary approach.
• Highlight the importance of cultural competence and community engagement in AI healthcare.

Main Methods:

• Communicative methodology with end-user and stakeholder involvement.
• Iterative consultations with trans community representatives.
• Three focus groups with 16 trans adults discussing AI in precision medicine.

Main Results:

• Barriers include data privacy concerns, algorithmic bias, and lack of trans-specific health data.
• Participants fear misdiagnosis due to cisnormative data models.
• Opportunities exist for AI to improve outcomes via community-led data and transparency.

Conclusions:

• A trans-inclusive AI approach is essential for personalized medicine.
• Addressing challenges with community-driven solutions can bridge health gaps.
• Inclusive AI design is crucial for equitable health innovation for marginalized communities.