Liquid biopsy has arisen as a revolutionary method in oncology, which permits non-invasive identification and tracking of cancer by means of circulating biomarkers including cell-free DNA, tumor cells in circulation, and exosomes. Applying artificial intelligence (AI) to liquid biopsy interpretation shows great potential for advancing diagnostic precision, prognostic classification, and treatment choices, but a thorough review of current studies is missing. This systematic literature review seeks to synthesize and rigorously assess the present landscape of AI-driven liquid biopsy interpretation, covering aspects such as biomarker analysis, cancer diagnosis, and wider clinical uses. We performed a thorough analysis of peer-reviewed research, with particular attention to the relationship between AI techniques and liquid biopsy information, and also investigated issues including data variability, the general applicability of models, and validation in clinical settings. The results indicate AI methods, especially machine learning and deep learning, show outstanding capability in improving the accuracy and precision of cancer detection via liquid biopsy, with marked progress in diagnosing early-stage cases and tracking minimal residual disease. However, inconsistencies in validation protocols and limited translational studies highlight gaps between computational innovation and clinical adoption. The analysis additionally highlights developing tendencies, including the merging of diverse data types and interpretable artificial intelligence, which could resolve existing constraints. We conclude AI-driven liquid biopsy analysis is a swiftly advancing area in precision oncology, yet consistent frameworks and rigorous clinical studies are crucial to achieve its complete benefits. This study serves as a key resource for scientists and medical professionals exploring the convergence of artificial intelligence and liquid biopsy in oncology.
Laszlo Pokorny (Thu,) studied this question.