AI for all: bridging data gaps in machine learning and health

Area of Science:

• Health Informatics
• Artificial Intelligence
• Machine Learning

Background:

• Artificial intelligence (AI) and machine learning (ML) offer transformative potential in healthcare, improving diagnoses, treatments, and patient care.
• The efficacy of AI/ML in medicine is critically dependent on the quality and diversity of training data.
• Current ML datasets often exhibit biases and lack diversity, potentially exacerbating existing health disparities.

Purpose of the Study:

• To highlight the challenges posed by biased datasets in AI/ML for healthcare.
• To emphasize the impact of data bias on marginalized communities.
• To advocate for strategies that mitigate bias and promote fairness in AI-driven health solutions.

Main Methods:

• Review of challenges associated with biased datasets in machine learning for healthcare.
• Discussion of the impact of data bias on health equity.
• Exploration of strategies to address data bias, including inclusive data collection and federated learning.

Main Results:

• Biased and non-diverse datasets lead to inaccurate AI predictions, perpetuating health disparities.
• Marginalized communities are disproportionately affected by biased AI in healthcare.
• Innovative approaches are necessary to ensure AI health solutions are accurate and equitable.

Conclusions:

• Adopting inclusive data collection practices and community engagement is essential.
• Leveraging techniques like federated learning can help mitigate bias in AI models.
• Addressing systemic biases is key to realizing the full potential of AI for universal health equity.