Key points are not available for this paper at this time.
Introduction: The systematic analysis of cancer markers and the impact of artificial intelligence (AI) on early detection and therapeutic approach are crucial in today’s medical field. Cancer represents a significant global burden of morbidity and mortality, making early identification of markers a priority for effective disease management. This study aims to explore recent advancements in the identification and characterization of cancer indicators, including genetic, molecular, protein, and imaging biomarkers. Objective: To analyze the latest advances in identifying and characterizing cancer indicators, covering a variety of biomarker types. Additionally, to investigate the role of AI in improving and applying methods for cancer detection, diagnosis, prognosis, and treatment, highlighting its significant contributions to enhancing the accuracy and efficiency of these approaches. Method: A systematic literature review was conducted, selecting relevant studies addressing the identification of cancer biomarkers and the use of AI in this context based on specific inclusion and exclusion criteria. Results: The results of this systematic analysis highlight recent advances in identifying and characterizing cancer indicators, as well as the impact of AI on enhancing detection, diagnosis, prognosis, and treatment approaches. Conclusion: This study offers valuable insights into the role of cancer indicators and AI in disease prevention and management, supporting evidence-based clinical practices and promoting the development of more efficient and personalized healthcare approaches.
Barioni et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: