# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 7 The Role of AI in Cancer Genomics
### Early Detection and Monitoring  
AI is playing an increasingly pivotal role in early cancer detection and the continuous monitoring of tumor evolution. With advancements in liquid biopsy technologies, particularly the analysis of ctDNA, AI is significantly improving the ability to detect cancer at its earliest, most treatable stages and to track the progression of the disease throughout treatment. These developments are helping to establish more dynamic, real-time approaches to cancer care, allowing for timely interventions and better patient prognosis.  
AI is particularly effective in analyzing the complex datasets generated by liquid biopsies. By applying ML algorithms to large amounts of ctDNA data, AI systems can identify subtle genetic alterations associated with the presence of tumors, allowing for earlier detection of cancer. AI models can process variations in ctDNA levels and sequence data to detect even small quantities of tumor-derived DNA, improving the sensitivity of early detection [85].  
For example, in cancers like lung cancer, breast cancer, and colon cancer, liquid biopsies can reveal the presence of specific variants or gene fusions that are indicative of malignancy. AI can then classify these alterations, even in their early stages, providing critical information for early-stage diagnosis and allowing clinicians to intervene before the disease becomes clinically significant [102]. The ability to detect MRD or early relapse is another benefit of using AI in the context of liquid biopsies. This allows clinicians to monitor ctDNA levels over time, offering insights into how the disease is responding to therapy and whether the patient is at risk of recurrence. By tracking these molecular signals in real time, AI tools are helping oncologists make more informed decisions about when to adjust or intensify treatments (Figure 5) [102].  
In addition to early detection, AI-driven tools are also crucial in the continuous monitoring of cancer progression and the detection of resistance mechanisms that may arise during treatment. Tumors are dynamic entities that evolve over time, often developing resistance to therapies, especially in cancers like lung, breast, and colorectal cancers, where resistance to common drugs (e.g., chemotherapy and targeted therapies) is a major challenge [86].  
AI plays an important role in monitoring these evolutionary changes. AI algorithms can detect variants or genetic alterations that signify tumor resistance to specific therapies, such as changes in the tumor's drug targets, which can indicate the need for a change in the treatment approach. For instance, in cases of NSCLC, patients may initially respond to targeted therapies like EGFR inhibitors, but, over time, the tumor may develop EGFR T790M variants, leading to resistance [54]. AI can help identify these variants through ctDNA analysis, allowing clinicians to switch to a more appropriate second-line therapy (e.g., osimetinib) before clinical relapse occurs. AI's ability to track resistance mutations in real time provides critical insights for personalizing treatment, ensuring that the approach remains effective as the tumor adapts [103].  
Furthermore, AI is enhancing the ability to monitor tumor heterogeneity, the existence of multiple genetic variations within a single tumor. This heterogeneity can complicate treatment because different subpopulations of tumor cells may respond differently to therapies. By analyzing longitudinal data from liquid biopsies or imaging, AI models can detect shifts in tumor composition, helping to identify emerging clones that may be resistant to current treatment regimens [104].