# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 7 The Role of AI in Cancer Genomics
### Addressing Challenges  
While AI holds immense potential in cancer genomics, its integration into clinical practice comes with a range of challenges that must be addressed to ensure effective, ethical, and equitable use. These challenges primarily focus on transparency, bias, and ethical considerations, each of which plays a crucial role in maximizing the potential of AI-driven technologies while minimizing risks. One of the key challenges in AI implementation in oncology is ensuring that AI models are transparent and comprehensible. In clinical settings, particularly in oncology, decisions based on AI-generated insights must be easily understandable by healthcare providers to build trust and confidence. Black-box models, where the decision making process of the AI is not transparent, can be problematic because clinicians and patients may not understand how a particular conclusion was obtained. This lack of transparency can cause problems around the reliability of AI systems, especially when they are used in important decisions like cancer treatment. To address this issue, there is a growing need for explainable AI (XAI), which focuses on developing algorithms whose decisions can be easily interpreted and explained by humans [110]. Examples of XAI methods include LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), which provide local explanations for individual predictions, as well as saliency maps in deep learning, which highlight input features most relevant to a model's output [111]. By integrating interpretability into AI systems, clinicians can better understand why a particular treatment recommendation was made or how a diagnostic result was reached. Crucially, without proper XAI analyses and demonstrable interpretability, the widespread application of AI into routine clinical practice should be approached with extreme caution, as the lack of transparency can impede clinical trust, accountability, and the ability to ensure patient safety and equitable care. This helps in ensuring that AI becomes a useful tool for clinicians rather than a source of confusion or mistrust, thereby improving its clinical adoption. Moreover, explainable AI also supports the legal and ethical responsibility of healthcare providers when utilizing AI tools. By understanding AI decisions, clinicians can make informed choices about whether to accept or challenge an AI recommendation, fostering a more collaborative and transparent decision making process. Another significant challenge in the integration of AI in cancer genomics is bias in datasets [112]. AI algorithms are only as good as the data they are trained on, and if the training data are biased, the AI model can extend or even exacerbate existing healthcare disparities. For example, if genomic data or clinical records predominantly represent certain populations, such as individuals of European descent, AI models may be less accurate for people from other ethnic groups [113]. This can lead to disparities in diagnosis, treatment recommendations, and patient outcomes, as the AI system may fail to account for the genetic variations or healthcare needs of diverse populations. To mitigate this risk, it is crucial to diversify datasets used for training AI models [114]. This includes ensuring that datasets are representative of various ethnicities, age groups, and other demographic factors. In addition, AI systems should undergo rigorous testing and validation across different populations to ensure that their predictions are robust and