mlsanityData to 7 Oct 2026

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

arXiv 2407.19655 ↗First posted 29 Jul 2024cs.AIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 41 of its 167 references that are in the map. First posted on arXiv 29 Jul 2024.

Builds on

41 of 167 references.

References · 41

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29 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Scaling Participation in Modular AI Systemscited by 2
  2. 2026Multimodal Survival Modeling and Fairness-Aware Clinical Machine Learning for 5-Year Breast Cancer Risk Predictioncited by 0
  3. 2026HiMed: Incentivizing Hindi Reasoning in Medical LLMscited by 0