Department of Computer Science and Engineering, Graphic Era University, Dehradun, Uttarakhand, India.
Prince Sattam Bin Abdul Aziz University, Wadi Al Dawasir 1191, Saudi Arabia.
J Healthc Eng. 2022 Mar 8;2022:8169203. doi: 10.1155/2022/8169203. eCollection 2022.
Deep learning (DL) and machine learning (ML) have a pivotal role in logistic supply chain management and smart manufacturing with proven records. The ability to handle large complex data with minimal human intervention made DL and ML a success in the healthcare systems. In the present healthcare system, the implementation of ML and DL is extensive to achieve a higher quality of service and quality of health to patients, doctors, and healthcare professionals. ML and DL were found to be effective in disease diagnosis, acute disease detection, image analysis, drug discovery, drug delivery, and smart health monitoring. This work presents a state-of-the-art review on the recent advancements in ML and DL and their implementation in the healthcare systems for achieving multi-objective goals. A total of 10 papers have been thoroughly reviewed that presented novel works of ML and DL integration in the healthcare system for achieving various targets. This will help to create reference data that can be useful for future implementation of ML and DL in other sectors of healthcare system.
深度学习（DL）和机器学习（ML）在物流供应链管理和智能制造中具有关键作用，并有可靠的记录。由于其具有处理大量复杂数据而无需人工干预的能力，DL 和 ML 在医疗保健系统中取得了成功。在当前的医疗保健系统中，广泛实施 ML 和 DL 是为了实现更高的服务质量和患者、医生和医疗保健专业人员的健康质量。ML 和 DL 在疾病诊断、急性疾病检测、图像分析、药物发现、药物输送和智能健康监测方面已被证明是有效的。本文对 ML 和 DL 的最新进展及其在医疗保健系统中的实施进行了综述，以实现多目标。总共彻底审查了 10 篇论文，这些论文提出了将 ML 和 DL 集成到医疗保健系统中以实现各种目标的新颖工作。这将有助于创建参考数据，这些数据对于未来在医疗保健系统的其他领域实施 ML 和 DL 可能会很有用。
