# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 7 The Role of AI in Cancer Genomics
### Addressing Challenges
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Device/Software Name** & **Developer** & **Version/Manufacturer 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 PreFound AI® & iCAD & Version 3.0, iCAD Inc., & 2021 & \
\begin{tabular}{c} Breast cancer detection \\ in 3D mammography \\ \end{tabular} &
\begin{tabular}{c} AI tool to improve \\ detection rates and \\ reduce false positives in \\ digital breast \\ tomosynthesis. \\ \end{tabular} & FDA 510(k) K203822 (12 March 2021) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._equitable. This requires collaboration between clinicians, data scientists, and policy makers to establish guidelines and best practices for bias mitigation in AI. The use of AI in cancer genomics also raises important ethical considerations, particularly those related to data privacy and patient consent [112]. As AI systems rely heavily on large datasets, including genomic and clinical data, protecting the privacy and security of patient information is paramount. Misuse or breaches of sensitive data could undermine public trust in these technologies, hindering their widespread adoption. To safeguard patient privacy, it is essential to implement robust data protection protocols, such as encryption and secure data storage, to ensure that patient data are not accessed or shared without proper authorization. Data encryption involves transforming data into a coded format to prevent unauthorized access. Common types include symmetric encryption (using a single key for both encryption and decryption) and asymmetric encryption (using a pair of public and private keys) [115]. Given the advent of quantum computing, the field is also exploring post-quantum encryption methods, which are cryptographic algorithms designed to be secure against attacks by quantum computers, ensuring long-term data protection in the face of evolving technological threats [116]. Additionally, clear and transparent informed consent procedures should be established so that patients are fully aware of how their data will be used in AI-driven research and decision making processes. One promising approach is Federated Learning, which enables multiple institutions to train AI models on decentralized genomic datasets without transferring sensitive patient data. In Federated Learning, instead of centralizing all patient data for training, the AI model is distributed to local data silos (e.g., individual hospitals or research centers). Each local model is trained on its respective sensitive dataset, and only aggregated model updates (e.g., changes in model parameters or weights), not the raw patient data, are sent back to a central server. These updates are then combined to improve a global model, which is subsequently shared back with the local participants for further refinement [117]. This iterative process allows for collaborative model training across diverse datasets while strictly preserving patient privacy and data locality. This method enhances collaboration across research centers while maintaining patient privacy. Recent studies have demonstrated how Federated Learning can improve predictive modeling in genomics by aggregating diverse datasets securely [118]. Furthermore, ethical concerns related to algorithmic fairness and the potential for discriminatory outcomes in AI-driven decisions must be addressed. Ensuring that AI models are designed to respect human dignity and autonomy is critical. This involves ensuring that AI systems do not disseminate harmful stereotypes or lead to unfair decisions based on non-medical factors, such as a patient's socio-economic status or gender.