Molecular Pathogens 2024, Vol.15, No.1, 1-8 http://microbescipublisher.com/index.php/mp 7 However, AI also faces challenges in early diagnosis. Data quality and availability are important issues. AI requires a large amount of high-quality data for training and validation, but in some medical fields, data collection and sharing still have limitations. Moreover, the privacy and security of medical data need to be carefully considered to avoid the leakage of sensitive information. The interpretability of AI models is also a challenge. While AI can provide accurate diagnostic results, explaining the reasons and process behind them may not be intuitive. Understanding and accepting the decision-making processes of AI for physicians and patients is an important issue. Therefore, researchers need to propose effective methods to explain and visualize the decision-making processes of AI models to increase human trust and acceptance. 5.3 Future development trends of artificial intelligence in early diagnosis With the continuous advancement of technology, the application of artificial intelligence in medical imaging will become more precise and efficient. New algorithms and deep learning techniques will enable AI to accurately identify and interpret abnormalities in medical images, helping doctors detect disease signs earlier. The application of artificial intelligence in early diagnosis will also expand to other fields such as genomics and molecular medicine. By analyzing genetic data and cellular signals, AI can assist doctors in gaining a better understanding of disease mechanisms and provide personalized diagnosis and treatment plans based on individual genetic variations. In the future, the development of artificial intelligence in early diagnosis will also involve the integration and analysis of multimodal data. Medical data often involves multiple types of information, such as medical images, laboratory results, and medical records. Integrating and analyzing these data will help doctors form more comprehensive and accurate early diagnosis results. The development of artificial intelligence in early diagnosis also requires close integration with clinical practice. Through collaboration and feedback from doctors, AI can continuously optimize and improve diagnostic algorithms, enhancing their practicality and usability in clinical settings. 6 Summary and Prospect Artificial intelligence has made important contributions and value in the early diagnosis of A (H1N1) influenza virus infection. By analyzing patients' medical records and clinical symptoms, artificial intelligence can quickly and accurately screen whether patients may be infected with the influenza virus. It helps doctors to promptly detect infection cases and implement appropriate isolation and treatment measures, thereby reducing the spread and severity of the epidemic. The application of artificial intelligence in medical image analysis has provided new breakthroughs for diagnosis. Through deep learning and image recognition techniques, artificial intelligence can automatically analyze and identify lung manifestations related to influenza virus infection, such as lesions and infiltrates. This not only enables doctors to obtain accurate diagnostic results quickly but also reduces dependence on experts, improving diagnostic accuracy and efficiency (Hegde et al., 2022). Given the successful application of artificial intelligence in the early diagnosis of A (H1N1) influenza virus infection, future research can focus on the following aspects. First, ongoing data collection and model optimization should be continued. In the early diagnosis of influenza virus infection, collecting more clinical and imaging data is crucial for improving the accuracy and robustness of artificial intelligence models. Simultaneously, algorithms and models need to be further optimized to enhance sensitivity and specificity for influenza virus infection. Future research can explore the application of artificial intelligence in influenza outbreak prediction and monitoring. By collecting a large amount of influenza-related data, such as symptoms, population movements, and social media data, artificial intelligence can help predict and monitor the spread trends and high-risk regions of influenza outbreaks. This will contribute to the formulation of more effective intervention measures and resource allocation to tackle the challenges of influenza outbreaks.
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