In recent years, machine learning (ML) has emerged as a powerful tool for solving a wide range of problems, including medical decision-making. The exponential growth of medical data over the past two decades has surpassed the capacity for manual analysis, prompting increased interest in automated data analysis and processing. ML algorithms, capable of learning from data with minimal human intervention, are particularly well-suited for medical data analysis and interpretation. One significant advantage of ML is the reduced cost of collecting labeled training data necessary for supervised learning. While numerous studies have explored the applications of ML in medicine, this survey specifically focuses on the use of ML across various medical research fields. We provide a comprehensive technical overview of existing studies on ML applications in medicine, highlighting the strengths and limitations of these approaches. Additionally, we discuss potential research directions for future exploration. These include the development of more sophisticated reward functions, as the accuracy of the reward function is crucial for ML performance, the integration of ML with other techniques, and the application of ML to new and emerging areas in genomics research. Finally, we summarize our findings and present the current state of the field and the future outlook for ML in medical application.

翻译：近年来，机器学习（ML）已成为解决包括医疗决策在内的广泛问题的有力工具。过去二十年中，医学数据的指数级增长已超出人工分析的能力，促使人们对自动化数据处理与分析的兴趣日益增长。机器学习算法能够在最少人为干预的情况下从数据中学习，因此特别适用于医学数据的分析与解读。机器学习的一个显著优势是降低了监督学习所需标记训练数据的收集成本。尽管已有大量研究探索了机器学习在医学领域的应用，但本综述特别关注机器学习在各个医学研究领域中的使用。我们对现有关于机器学习在医学中应用的研究进行了全面的技术概述，并强调了这些方法的优势与局限性。此外，我们还讨论了未来探索的潜在研究方向。这些包括开发更复杂的奖励函数（因为奖励函数的准确性对机器学习性能至关重要）、机器学习与其他技术的整合，以及机器学习在基因组学研究新兴领域的应用。最后，我们总结了研究发现，并阐述了该领域的现状以及机器学习在医学应用中的未来展望。