Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient’s prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.

Despite the potential benefits of AI in the diagnosis of lung cancer, there are still several challenges that need to be addressed: Data quality: AI algorithms require large amounts of high-quality data to be trained effectively. In the case of lung cancer diagnosis, access to high-quality medical images and patient data can be a challenge. In addition, there may be variations in the quality of medical images and annotations, which can affect the accuracy of the algorithms. Interpretability: The results generated by AI algorithms can be difficult to interpret and may require additional human expertise to make clinical decisions. This is particularly true for deep learning algorithms, which can be seen as black boxes that are difficult to understand and interpret. Generalization: AI algorithms trained on a particular dataset may not generalize well to new and unseen datasets. This can be a problem if the algorithm is used in a different clinical setting or with a different patient population. Regulatory challenges: There are regulatory challenges associated with the use of AI in medical diagnosis. For example, there may be concerns about data privacy and the ethical use of patient data. In addition, regulatory bodies may require additional validation and testing of AI algorithms before they can be used in clinical practice. Integration with clinical workflows: AI algorithms need to be seamlessly integrated into clinical workflows to be effective. This can require changes to existing clinical processes and may require additional training for healthcare professionals. Cost: The development and deployment of AI algorithms can be expensive, and there may be concerns about the cost-effectiveness of using AI in medical diagnosis. Addressing these challenges will require collaboration between clinicians, researchers, and AI developers, as well as continued investment in AI research and development.

The use of AI in the diagnosis of lung cancer has the potential to bring several benefits, including: Improved accuracy: AI algorithms can analyze large amounts of data with a high degree of accuracy, potentially leading to earlier detection and more accurate diagnosis of lung cancer. This can lead to better patient outcomes and improved survival rates. Personalized medicine: AI algorithms can help identify individual characteristics of patients that may affect the diagnosis and treatment of lung cancer. This can lead to more personalized treatment plans that take into account the patient's specific needs and characteristics. Increased efficiency: AI algorithms can analyze medical images and patient data much faster than human doctors, leading to faster diagnosis and treatment of lung cancer. This can help reduce waiting times for patients and improve resource utilization for healthcare providers. Reduced costs: By improving efficiency and accuracy, the use of AI in the diagnosis of lung cancer has the potential to reduce healthcare costs. This can be particularly important for patients with limited access to healthcare resources. Better patient experience: AI algorithms can help reduce the need for invasive procedures such as biopsies, reducing the discomfort and anxiety experienced by patients. Early detection: AI algorithms can help identify early signs of lung cancer, which can lead to earlier treatment and better outcomes for patients. Early detection is particularly important for lung cancer, as it is often diagnosed at a later stage when it is more difficult to treat. In summary, the use of AI in the diagnosis of lung cancer has the potential to bring several benefits, including improved accuracy, personalized medicine, increased efficiency, reduced costs, better patient experience, and early detection.

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