Paik Kenneth Eugene, Hicklen Rachel, Kaggwa Fred, Puyat Corinna Victoria, Nakayama Luis Filipe, Ong Bradley Ashley, Shropshire Jeremey N I, Villanueva Cleva
MIT Critical Data, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
PLOS Digit Health. 2023 Oct 12;2(10):e0000313. doi: 10.1371/journal.pdig.0000313. eCollection 2023 Oct.
Artificial intelligence (AI) and machine learning (ML) have an immense potential to transform healthcare as already demonstrated in various medical specialties. This scoping review focuses on the factors that influence health data poverty, by conducting a literature review, analysis, and appraisal of results. Health data poverty is often an unseen factor which leads to perpetuating or exacerbating health disparities. Improvements or failures in addressing health data poverty will directly impact the effectiveness of AI/ML systems. The potential causes are complex and may enter anywhere along the development process. The initial results highlighted studies with common themes of health disparities (72%), AL/ML bias (28%) and biases in input data (18%). To properly evaluate disparities that exist we recommend a strengthened effort to generate unbiased equitable data, improved understanding of the limitations of AI/ML tools, and rigorous regulation with continuous monitoring of the clinical outcomes of deployed tools.
人工智能（AI）和机器学习（ML）在改变医疗保健方面具有巨大潜力，这已在各个医学专科中得到证明。本范围综述通过对文献进行回顾、分析和结果评估，重点关注影响健康数据贫困的因素。健康数据贫困往往是一个看不见的因素，它会导致健康差距持续存在或加剧。解决健康数据贫困方面的改进或失败将直接影响人工智能/机器学习系统的有效性。潜在原因很复杂，可能在开发过程的任何环节出现。初步结果突出了具有健康差距（72%）、人工智能/机器学习偏差（28%）和输入数据偏差（18%）等共同主题的研究。为了正确评估存在的差距，我们建议加大力度生成无偏差的公平数据，更好地理解人工智能/机器学习工具的局限性，并进行严格监管，持续监测已部署工具的临床结果。
