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AI-driven healthcare: Fairness in AI healthcare: A survey

Author

Listed:
  • Sribala Vidyadhari Chinta
  • Zichong Wang
  • Avash Palikhe
  • Xingyu Zhang
  • Ayesha Kashif
  • Monique Antoinette Smith
  • Jun Liu
  • Wenbin Zhang

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.Author summary: In this paper, we investigate the rapid advancement of artificial intelligence (AI) in healthcare, focusing on its role in improving efficiency and effectiveness across specialties such as cardiology, ophthalmology, and dermatology. We note that AI technologies enhance diagnostic accuracy, treatment personalization, and patient outcome predictions. However, these developments also pose significant ethical challenges, particularly concerning biases in data and algorithms that can create disparities in healthcare delivery. We examine the integration of AI in healthcare, highlighting the critical challenges related to bias and exploring strategies for mitigation. We emphasize the need for diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery. Our 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.

Suggested Citation

  • Sribala Vidyadhari Chinta & Zichong Wang & Avash Palikhe & Xingyu Zhang & Ayesha Kashif & Monique Antoinette Smith & Jun Liu & Wenbin Zhang, 2025. "AI-driven healthcare: Fairness in AI healthcare: A survey," PLOS Digital Health, Public Library of Science, vol. 4(5), pages 1-27, May.
    • Handle: RePEc:plo:pdig00:0000864
    DOI: 10.1371/journal.pdig.0000864
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    References listed on IDEAS

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    1. Effy Vayena & Alessandro Blasimme & I Glenn Cohen, 2018. "Machine learning in medicine: Addressing ethical challenges," PLOS Medicine, Public Library of Science, vol. 15(11), pages 1-4, November.
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