Summary of Ai-driven Healthcare: a Survey on Ensuring Fairness and Mitigating Bias, by Sribala Vidyadhari Chinta et al.
AI-Driven Healthcare: A Survey on Ensuring Fairness and Mitigating Bias
by Sribala Vidyadhari Chinta, Zichong Wang, Xingyu Zhang, Thang Doan Viet, Ayesha Kashif, Monique Antoinette Smith, Wenbin Zhang
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This survey paper explores the integration of artificial intelligence (AI) in healthcare, highlighting its potential to enhance diagnostic accuracy, treatment personalization, and patient outcome predictions across various specialties. AI technologies like machine learning, neural networks, and natural language processing are leveraged to improve services. However, these advancements also introduce substantial ethical and fairness challenges related to biases in data and algorithms. The paper examines the critical challenge of bias and explores strategies for mitigation, emphasizing the necessity of diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery. Recommendations for future research include interdisciplinary approaches, transparency in AI decision-making, and the development of innovative and inclusive AI applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how artificial intelligence (AI) is being used in healthcare to make doctors’ jobs easier and patients healthier. AI can help diagnose diseases more accurately and personalize treatments better. However, there are some big problems with using AI in healthcare – it can be biased against certain groups of people, which can affect the care they receive. The paper talks about these challenges and suggests ways to fix them, like making sure data is fair and diverse, and creating rules for how AI makes decisions. The goal is to make healthcare more equal and fair for everyone. |
Keywords
» Artificial intelligence » Machine learning » Natural language processing
