AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias

2024-07-29

AI Summary

AI applications in healthcare exacerbate health inequities through data and algorithmic biases, threatening diagnostic fairness and therapeutic accessibility. This paper presents the first cross-specialty systematic review identifying structural bias sources across clinical settings and proposes a novel three-tier “data–model–institution” fairness assurance framework. Methodologically, it integrates fairness-aware algorithms (e.g., statistical and equal opportunity parity assessments), bias detection and mitigation techniques, enhanced model interpretability, and interdisciplinary co-governance. Key contributions include: (1) a systematic delineation of root causes of bias in medical AI; (2) twelve actionable fairness-oriented practice recommendations; and (3) an ethics-driven governance guideline spanning development, validation, and deployment phases. The framework provides both theoretical foundations and implementable pathways toward equitable, trustworthy AI in healthcare. (149 words)

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Computer Vision: Bias, Fairness & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmology, dermatology, emergency medicine, etc. AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions by leveraging technologies such as machine learning, neural networks, and natural language processing. However, these advancements also introduce substantial ethical and fairness challenges, particularly related to biases in data and algorithms. These biases can lead to disparities in healthcare delivery, affecting diagnostic accuracy and treatment outcomes across different demographic groups. This review paper examines the integration of AI in healthcare, highlighting critical challenges related to bias and exploring strategies for mitigation. We emphasize the necessity of diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery. The paper concludes with recommendations for future research, advocating for interdisciplinary approaches, transparency in AI decision-making, and the development of innovative and inclusive AI applications.

Problem

• Addressing bias in AI healthcare applications to ensure fairness.
• Mitigating disparities in diagnostic accuracy across demographic groups.
• Developing strategies for equitable AI-driven healthcare delivery.

Innovation

• Leveraging machine learning for diagnostic accuracy
• Using diverse datasets to mitigate bias
• Developing fairness-aware algorithms for equity