Mashraqi Aisha Mousa, Allehyani Budoor
Department of Computer Science, College of Computer Science and Information Systems, Najran University, UAE.
Department of Information System, College of Computers and Information Systems, Umm Al-Qura University, UAE.
Bioinformation. 2022 Nov 30;18(11):1050-1061. doi: 10.6026/973206300181050. eCollection 2022.
Artificial Intelligence (AI) is expanding with colossal applications in various sectors. In the healthcare sector, it is booming to make life simpler with utmost accuracy by predicting, diagnosing and up to care with the help of Machine Learning (ML) and Deep Learning (DL) applications. Modern computer algorithms have attained accuracy levels comparable to those of human specialists in medical sciences, although computers often do jobs more quickly than people do. It is also expected that there will not be a mandate for humans to be present for the jobs that machines can do, and it is gaining the highest peak because of good trained artificial models in the medical field. ML enhances the therapeutic process and improves health by encouraging more patient participation. ML may get more accurate patient data when used with the Internet of Medical Things (IoMT) and automate message notifications that prompt patients to respond at certain times. The motivation behind this article is to make a comprehensive review of the on-going implementation of ML in medical science, what challenges it is facing now, and how it can be simplified for future researchers to contribute better to medical sciences while applying it to the practitioners' jobs easier. In this review, we have extensively mined the data and brought up systematised applications of AI in healthcare, what challenges have been faced by the experts, and what ethical responsibilities are liable to them while taking the data. We also tabulated which algorithms will be helpful for what kind of data and disease conditions will be useful for future researchers and developers. This article will provide a better insight into AI and ML for the beginner to the advanced developer and researcher to understand the concepts from the basics.
人工智能（AI）正在各个领域大规模扩展应用。在医疗保健领域，它正蓬勃发展，借助机器学习（ML）和深度学习（DL）应用，通过预测、诊断乃至护理，以极高的准确性让生活更便捷。现代计算机算法已达到与医学领域人类专家相当的准确率，尽管计算机通常比人工作得更快。预计对于机器能完成的工作，将不再强制要求人类在场，而且由于医学领域训练有素的人工模型，它正达到顶峰。机器学习通过鼓励更多患者参与来增强治疗过程并改善健康状况。当与医疗物联网（IoMT）结合使用时，机器学习可以获取更准确的患者数据，并自动发送消息通知，促使患者在特定时间做出回应。本文的目的是全面回顾机器学习在医学领域的当前应用情况、它目前面临的挑战，以及如何简化以便未来的研究人员在将其应用于从业者工作时能更好地为医学做出贡献。在本次综述中，我们广泛挖掘了数据，梳理了人工智能在医疗保健中的系统化应用、专家们面临的挑战，以及在获取数据时他们应承担的道德责任。我们还列出了哪些算法对何种数据有用，以及哪些疾病状况对未来的研究人员和开发者有帮助。本文将为从初学者到高级开发者和研究人员提供对人工智能和机器学习的更深入见解，帮助他们从基础层面理解这些概念。
