With the advancements in computer technology, there is a rapid development of intelligent systems to understand the complex relationships in data to make predictions and classifications. Artificail Intelligence based framework is rapidly revolutionizing the healthcare industry. These intelligent systems are built with machine learning and deep learning based robust models for early diagnosis of diseases and demonstrates a promising supplementary diagnostic method for frontline clinical doctors and surgeons. Machine Learning and Deep Learning based systems can streamline and simplify the steps involved in diagnosis of diseases from clinical and image-based data, thus providing significant clinician support and workflow optimization. They mimic human cognition and are even capable of diagnosing diseases that cannot be diagnosed with human intelligence. This paper focuses on the survey of machine learning and deep learning applications in across 16 medical specialties, namely Dental medicine, Haematology, Surgery, Cardiology, Pulmonology, Orthopedics, Radiology, Oncology, General medicine, Psychiatry, Endocrinology, Neurology, Dermatology, Hepatology, Nephrology, Ophthalmology, and Drug discovery. In this paper along with the survey, we discuss the advancements of medical practices with these systems and also the impact of these systems on medical professionals.

中文翻译：

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用于构建医学和药物发现智能系统的机器学习和深度学习方法：综合调查

随着计算机技术的进步，智能系统迅速发展，以了解数据中的复杂关系以进行预测和分类。基于人工智能的框架正在迅速改变医疗保健行业。这些智能系统采用基于机器学习和深度学习的稳健模型构建，用于疾病的早期诊断，并为一线临床医生和外科医生展示了一种很有前景的辅助诊断方法。基于机器学习和深度学习的系统可以简化从临床和基于图像的数据诊断疾病所涉及的步骤，从而为临床医生提供重要的支持和工作流程优化。它们模仿人类的认知，甚至能够诊断人类智力无法诊断的疾病。本文重点调查了 16 个医学专业的机器学习和深度学习应用，即牙科医学、血液学、外科、心脏病学、肺病学、骨科、放射学、肿瘤学、普通医学、精神病学、内分泌学、神经病学、皮​​肤病学、肝病学、肾脏病学、眼科和药物发现。在本文和调查中，我们讨论了这些系统在医疗实践方面的进步以及这些系统对医疗专业人员的影响。肿瘤学、普通医学、精神病学、内分泌学、神经病学、皮​​肤病学、肝病学、肾脏病学、眼科和药物发现。在本文和调查中，我们讨论了这些系统在医疗实践方面的进步以及这些系统对医疗专业人员的影响。肿瘤学、普通医学、精神病学、内分泌学、神经病学、皮​​肤病学、肝病学、肾病学、眼科和药物发现。在本文和调查中，我们讨论了这些系统在医疗实践方面的进步以及这些系统对医疗专业人员的影响。