人工智能
人工智能应用
医学
医疗保健
机器学习
生殖医学
医学诊断
领域
生殖健康
精密医学
数据科学
计算机科学
怀孕
人口
病理
环境卫生
生物
政治学
法学
经济
遗传学
经济增长
作者
Victoria S. Jiang,Zoran J. Pavlovic,Eduardo Hariton
标识
DOI:10.1016/j.ogc.2023.09.003
摘要
Artificial intelligence (AI) and machine learning, the form most commonly used in medicine, offer powerful tools utilizing the strengths of large data sets and intelligent algorithms. These systems can help to revolutionize delivery of treatments , access to medical care, and improvement of outcomes, particularly in the realm of reproductive medicine. Whether that is more robust oocyte and embryo grading or more accurate follicular measurement, AI will be able to aid clinicians, and most importantly patients, in providing the best possible and individualized care. However, despite all of the potential strengths of AI, algorithms are not immune to bias and are vulnerable to the many socioeconomic and demographic biases that current healthcare systems suffer from. Wrong diagnoses as well is furthering of healthcare discrimination are real possibilities if both the capabilities and limitations of AI are not well understood. Armed with appropriate knowledge of how AI can most appropriately operate within medicine, and specifically reproductive medicine, will enable clinicians to both create and utilize machine learning-based innovations for the furthering of reproductive medicine and ultimately achieving the goal of building of healthy families.
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