The Role of Artificial Intelligence in Diagnosing Malignant Tumors

This paper explores the transformative impact of artificial intelligence (AI) in early tumor diagnosis, emphasizing its role in analyzing health records, medical images, biopsies, and blood tests for improved risk stratification. While screening programs have enhanced survival, challenges remain in patient selection and diagnostic workforces. The review covers diverse AI approaches, including logistic regression, deep learning, and neural networks, applied to various data types in oncology. It discusses the clinical implications, current models in practice, and potential limitations such as ethical concerns and resource demands. We provide an overview of the main artificial intelligence approaches, encompassing historical models like logistic regression, alongside deep learning and neural networks, emphasizing their applications in early diagnosis. We describe the role of AI in tumor detection, prognosis, and treatment administration, and we introduce the application of state-of-the-art large language models in oncology clinics. Our exploration extends to AI applications for omics data types, offering perspectives on their combination for decision-support tools. Concurrently, we evaluate existing constraints and challenges in applying artificial intelligence to precision oncology. The overall aim is to showcase AI's promise in revolutionizing tumor diagnosis while acknowledging and addressing associated challenges, thereby advancing patient care.