International Journal of Molecular Medical Science, 2024, Vol.14, No.1, 16-23 http://medscipublisher.com/index.php/ijmms 17 However, despite the tremendous potential demonstrated by artificial intelligence in molecular medicine, it also faces a series of challenges and issues. Among them, issues of data quality, the need for data privacy protection, as well as the interpretability and reliability of algorithms are urgent problems that need to be addressed. At the same time, the application of artificial intelligence in clinical settings also needs to overcome obstacles such as recognition and acceptance. This review aims to comprehensively discuss the application prospects of artificial intelligence in molecular medicine. The review will introduce the basic principles and methods of artificial intelligence in molecular medicine, as well as its potential applications in disease diagnosis, drug development, and treatment. Simultaneously, discussions will also be conducted on the current challenges and limitations, and corresponding solutions will be proposed. We believe that by fully harnessing the advantages of artificial intelligence technology, we can propel the development of molecular medicine, bringing new breakthroughs and advancements in the diagnosis, treatment, and prevention of diseases. 1 Basic Principles of Artificial Intelligence in Molecular Medicine 1.1 Concept and classification of artificial intelligence Artificial intelligence (AI) refers to a discipline that utilizes technologies such as computer science and machine learning to simulate and mimic human intelligence. In the field of molecular medicine, AI is widely used in areas such as data analysis, pattern recognition, and decision support (Liu and Chen, 2021). Based on different features and tasks, AI can be classified into expert systems, machine learning, deep learning, natural language processing, and reinforcement learning. Expert systems are knowledge- and rule-based approaches that simulate the decision-making process of experts by encoding their experience and knowledge into computer programs. In molecular medicine, expert systems can be employed to assist in tasks such as disease diagnosis, drug design, and treatment plan selection. Machine learning is a data-driven method that enables computers to make predictions and decisions by learning patterns and patterns from a large amount of data. It is a sub direction of artificial intelligence. In molecular medicine, machine learning can be used for tasks such as identifying molecular markers, predicting disease risks, and optimizing drug combinations (Anuraj et al., 2021). Additionally, deep learning, a branch of machine learning (Figure 1), involves constructing and training models with multiple layers of neural networks to achieve advanced abstraction and processing of complex data. In molecular medicine, deep learning can be applied to tasks such as image analysis, interpretation of gene expression data, and prediction of protein structures. Figure 1 Relationship between artificial intelligence and related concepts
RkJQdWJsaXNoZXIy MjQ4ODYzNQ==