Summary
This session explores the risks to public trust in AI-driven healthcare, focusing on data governance, bias, and fairness. Speakers emphasize the need for responsible AI implementation, digital literacy among healthcare workers, and equitable access to AI benefits across diverse populations and international borders. Participants underscored the importance of data harmonization and standardization for effective AI use.
Key Topics
5 key topics from this session at Future Health Summit (FHS). Thicker branches were mentioned more often in the talk.
Table 11. Governance, Regulations & Responsible AI (Care Safety Innovations)
Click a topic to open it
Key Takeaways
- Prioritize data governance and bias mitigation for trustworthy AI.
- Develop international standards for ethical AI implementation.
- Focus on digital literacy among healthcare professionals.
- Ensure equitable access to AI-driven healthcare across demographics.
Data Bias and its Impact on AI Outcomes
“So I personally believe because the current model that we have, we because there is not enough data being available for them to be trained on or the data is not being used properly, I think the outcome is biased.”
Share
Up Next
1:01:48
Next in agenda
Table 7: AI in Clinical Decision-Making and Workforce Evolution (PwC)
ai in healthcareclinical decision making
1:01:48
Later in the agenda
Table 6: Infrastructure and Interoperability for AI Scalability (PaloAlto Networks)
ai in healthcareinteroperability
1:01:48
Later in the agenda
Table 8: AI in Clinical Decision-Making and Workforce Evolution (PwC)
value based careartificial intelligence