Swain Subhasmita, Bhushan Bharat, Dhiman Gaurav, Viriyasitavat Wattana
Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, India.
Department of Computer Science, Government Bikram College of Commerce, Patiala, India.
Arch Comput Methods Eng. 2022;29(6):3981-4003. doi: 10.1007/s11831-022-09733-8. Epub 2022 Mar 22.
Machine Learning (ML) has been categorized as a branch of Artificial Intelligence (AI) under the Computer Science domain wherein programmable machines imitate human learning behavior with the help of statistical methods and data. The Healthcare industry is one of the largest and busiest sectors in the world, functioning with an extensive amount of manual moderation at every stage. Most of the clinical documents concerning patient care are hand-written by experts, selective reports are machine-generated. This process elevates the chances of misdiagnosis thereby, imposing a risk to a patient's life. Recent technological adoptions for automating manual operations have witnessed extensive use of ML in its applications. The paper surveys the applicability of ML approaches in automating medical systems. The paper discusses most of the optimized statistical ML frameworks that encourage better service delivery in clinical aspects. The universal adoption of various Deep Learning (DL) and ML techniques as the underlying systems for a variety of wellness applications, is delineated by challenges and elevated by myriads of security. This work tries to recognize a variety of vulnerabilities occurring in medical procurement, admitting the concerns over its predictive performance from a privacy point of view. Finally providing possible risk delimiting facts and directions for active challenges in the future.
机器学习（ML）被归类为计算机科学领域下人工智能（AI）的一个分支，其中可编程机器借助统计方法和数据模仿人类的学习行为。医疗保健行业是世界上规模最大、最繁忙的行业之一，在每个阶段都需要大量的人工审核。大多数有关患者护理的临床文档都是专家手写的，只有部分报告是机器生成的。这个过程增加了误诊的几率，从而对患者的生命构成风险。最近，为了实现手动操作自动化而采用的技术见证了机器学习在其应用中的广泛使用。本文调查了机器学习方法在医疗系统自动化中的适用性。本文讨论了大多数优化的统计机器学习框架，这些框架有助于在临床方面提供更好的服务。各种深度学习（DL）和机器学习技术作为各种健康应用的基础系统被广泛采用，但面临着挑战，且存在众多安全问题。这项工作试图识别医疗采购中出现的各种漏洞，承认从隐私角度对其预测性能的担忧。最后提供可能的风险界定事实以及应对未来实际挑战的方向。
