通过机器学习实现医疗诊断和治疗的进步：综述,Expert Systems

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通过机器学习实现医疗诊断和治疗的进步：综述
Expert Systems ( IF 3.0 ) Pub Date : 2023-11-08 , DOI: 10.1111/exsy.13499
Mohammad Ahsan ₁ , Anam Khan ₁ , Kaif Rehman Khan ₁ , Bam Bahadur Sinha ₂ , Anamika Sharma ₃

Affiliation

机器学习 (ML) 从大型数据集中学习、发现趋势和做出预测的能力已经证明了其改变医学领域的潜力。使用机器学习算法进行医疗数据分析可以改善患者的治疗和诊断结果。本文研究了机器学习在医疗行业中的多种可能性，包括放射学、病理学、基因组学和临床决策。它还讨论了机器学习在各个领域的优点和缺点以及其应用所带来的限制。它通过回顾以前的研究，说明了机器学习的潜在优势，例如诊断和个性化治疗方案的准确性和效率更高。最后，它提供了对该学科的未来进展和前景的看法。本研究还打算探讨深度学习（DL）在医学领域的应用。深度学习算法在多个医疗保健相关领域的表现非常令人满意。本文的结论总结了该研究的主要结论，并讨论了其对医疗保健行业的影响。本文旨在通过强调这些方法论改变医学研究和临床实践的前景，有助于更好地了解该学科的现状和未来发展的可能性。

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Advancements in medical diagnosis and treatment through machine learning: A review

The aptness of machine learning (ML) to learn from large datasets, discover trends, and make predictions has demonstrated its potential to metamorphose the medical field. Medical data analysis with ML algorithms can improve patient outcomes in terms of both treatment and diagnosis. This paper investigates the numerous possibilities of ML in the medical industries, including radiology, pathology, genomics, and clinical decision-making. It also goes over the benefits and drawbacks of ML in various sectors as well as the limitations that come with its application. It illustrates the potential advantages of ML, such as better accuracy and efficiency in diagnosis and individualized treatment programs, through a review of previous studies. Lastly, it provides perspectives on prospective advancements and prospects for the discipline. This study also intends to investigate the applications of deep learning (DL) in the medical field. DL algorithms have performed exceptionally satisfactorily in several healthcare-related fields. The main conclusions of the study are summarized, and their ramifications for the healthcare sector are discussed in this paper's conclusion. This paper intends to contribute to a greater understanding of the prevailing state of the discipline and the possibility for future developments by emphasizing the prospects of these methodologies to alter medical study and clinical practice.

更新日期：2023-11-08

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