Detection of easily missed hidden patterns with fast processing power makes machine learning (ML) indispensable to today's healthcare system. Though many ML applications have already been discovered and many are still under investigation, only a few have been adopted by current healthcare systems. As a result, there exists an enormous opportunity in healthcare system for ML but distributed information, scarcity of properly arranged and easily explainable documentation in related sector are major impede which are making ML applications difficult to healthcare professionals. This study aimed to gather ML applications in different areas of healthcare concisely and more effectively so that necessary information can be accessed immediately with relevant references. We divided our study into five major groups: community level work, risk management/ preventive care, healthcare operation management, remote care, and early detection. Dividing these groups into subgroups, we provided relevant references with description in tabular form for quick access. Our objective is to inform people about ML applicability in healthcare industry, reduce the knowledge gap of clinicians about the ML applications and motivate healthcare professionals towards more machine learning based healthcare system.

翻译：凭借快速的处理能力检测易被忽略的隐藏模式，使得机器学习在当今医疗体系中不可或缺。尽管已有众多机器学习应用被发现，且大量研究仍在进行中，但目前仅有少数被现行医疗体系采纳。因此，医疗体系在机器学习领域存在巨大机遇，但信息分散、相关领域缺乏条理清晰且易于理解的文档资料，成为阻碍医疗专业人员应用机器学习的主要障碍。本研究旨在简明有效地汇集机器学习在医疗健康各领域的应用，以便能通过相关参考文献即时获取必要信息。我们将研究划分为五大主题：社区级工作、风险管理/预防性护理、医疗运营管理、远程护理及早期检测。通过将上述主题细分子类，我们以表格形式提供相关参考文献及说明，便于快速查阅。本研究旨在使大众了解机器学习在医疗行业的适用性，缩小临床医生对机器学习应用的知识鸿沟，并激励医疗专业人员迈向更基于机器学习的医疗体系。