Habehh Hafsa, Gohel Suril
Department of Health Informatics, Rutgers University School of Health Professions, 65 Bergen Street, Newark, NJ 07107, USA.
Curr Genomics. 2021 Dec 16;22(4):291-300. doi: 10.2174/1389202922666210705124359.
Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, amongst a few. Although, skepticism remains regarding the practical application and interpretation of results from ML-based approaches in healthcare settings, the inclusion of these approaches is increasing at a rapid pace. Here we provide a brief overview of machine learning-based approaches and learning algorithms including supervised, unsupervised, and reinforcement learning along with examples. Second, we discuss the application of ML in several healthcare fields, including radiology, genetics, electronic health records, and neuroimaging. We also briefly discuss the risks and challenges of ML application to healthcare such as system privacy and ethical concerns and provide suggestions for future applications.
人工智能（AI）和机器学习（ML）技术的最新进展在预测和识别健康紧急情况、疾病人群、疾病状态和免疫反应等方面取得了重大进展。尽管如此，对于基于机器学习的方法在医疗环境中的实际应用和结果解读仍存在怀疑，但这些方法的应用正在迅速增加。在此，我们简要概述基于机器学习的方法和学习算法，包括监督学习、无监督学习和强化学习，并举例说明。其次，我们讨论机器学习在几个医疗领域的应用，包括放射学、遗传学、电子健康记录和神经成像。我们还简要讨论了机器学习应用于医疗保健的风险和挑战，如系统隐私和伦理问题，并为未来的应用提供建议。
