  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 survey 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.
