AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias
It tackles fairness issues in healthcare AI to prevent inequities for patients, but it is incremental as it reviews existing challenges and strategies without introducing new methods.
This review paper addresses the problem of biases in AI-driven healthcare, which can cause disparities in diagnostic accuracy and treatment outcomes across demographic groups, and it explores mitigation strategies like diverse datasets and fairness-aware algorithms.
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.