# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 7 The Role of AI in Cancer Genomics
### Supporting Clinical Decision Making  
AI has become an invaluable tool in clinical decision making, supporting clinicians by processing vast amounts of clinical data, genomic information, and patient histories. By incorporating ML algorithms, these systems can analyze patterns in data that might not be immediately apparent to healthcare providers. AI can then recommend optimal treatment strategies based on the latest clinical guidelines, clinical trial data, and the patient's individual genomic profile [105]. Figure 5: Workflow of liquid biopsy in oncology. Tumor-derived materials, such as circulating cancer cells, DNA, and RNA, are collected from blood samples and analyzed for a range of molecular alterations, including epigenetic modifications, chromosomal abnormalities, pathogenic variants, and protein expression. The integration of artificial intelligence helps interpret complex multi-omic data, supporting personalized clinical decision making. For example, AI systems can help in identifying the most appropriate targeted therapies for cancers driven by specific genetic variants. In cases of breast cancer, AI might analyze a patient's genetic profile to recommend treatment with HER2-targeted therapies for patients with overexpressed HER2 [106]. Similarly, in lung cancer, AI can help in determining whether EGFR inhibitors or immunotherapy would be more effective for a patient based on their tumor's molecular makeup [107; 108]. Beyond drug recommendations, AI can also help clinicians decide the best treatment regimen, considering factors like drug interactions, tolerability, and side effect profiles. This personalized approach improves clinical outcomes, as therapies are optimized to the patient's unique cancer characteristics, reducing errors and increasing the probability of success. One of the most significant benefits of AI in clinical decision making is its ability to reduce clinician workload through automated data integration. Oncology requires clinicians to evaluate a huge amount of data from diverse sources: genomic reports, imaging scans, pathology results, electronic health records (EHRs), and many more. The enormous volume of information can be overwhelming and time-consuming, leading to overload of information and slower decision making. By improving data integration and reducing the manual effort required for data analysis, AI enables clinicians to focus on what they do best, such as making informed decisions and interacting with patients [109]. Furthermore, AI can provide real-time alerts about changes in a patient's condition or new research findings relevant to their treatment plan. These proactive, automated systems ensure that clinicians are always equipped with the most up-to-date information. The integration of AI-driven decision support systems has the potential to significantly improve patient care by providing clinicians with better tools to make personalized, data-driven decisions. To illustrate the growing adoption and regulatory acceptance of AI in clinical practice, Table 7 provides a comprehensive overview of selected AI/machine-learning-based medical devices and software that have received FDA clearance in oncology and related diagnostic fields. These approvals underscore the robust validation and clinical utility of AI tools, marking a significant milestone in their integration into routine healthcare workflows. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Device/Software Name** & **Developer** & **Version/Manufacture Location** & **Approval Date** & \begin{tabular}{c} **Primary Application** \\ **in Oncology** \\ \end{tabular}  & **Notes on AI/ML Use** & \begin{tabular}{c} **FDA Clearance** \\ **Reference** \\ \end{tabular}  \\ \hline \multirow{4}{*}{ \begin{tabular}{c} Paige Prostate \\ Detect \\ aPROMISE \\ (Lambeus) \\ \end{tabular}  } & \multirow{4}{*}{Paige.AI} & \multirow{4}{*}{ \begin{tabular}{c} Paige.AI, New York, NY, \\ USA \\ \end{tabular}  } & \multirow{4}{*}{2021} & Primary diagnosis of & \