Significant advances have been made in the development of artificial intelligence (AI) systems in recent years. AI has been shown to be useful both in healthcare and in biomedicine due to its ability to process large amounts of data in a short time. Computer vision can be used to effectively diagnose cancer, e.g., by early disease screening, analyzing images of neoplasms, or even determining the stage of cancer. AI can be further used to schedule and personalize treatments, assist staff when making decisions regarding patient care, and even draw correlations between different diseases to avoid clinical complications. In addition to diagnostics and treatment, AI algorithms have successfully been applied in both the target and drug discovery processes and have proven to be far more time- and cost-effective than traditional methods. For example, large language models pretrained on biomedical data can be used to both extract large amounts of data from published research papers and to find correlations between different data sources that result in the identification of new therapeutic targets. Machine learning approaches have been implemented to predict drug–target interactions and for drug repurposing. Deep neural networks developed using PyTorch or TensorFlow together with deep transfer learning have been successfully applied to search for novel inhibitors of targets related to cancer and for cancer diagnosis. Despite these advances, cancer still represents a challenge for AI-supported in silico drug discovery. This is due to its remarkable ability to take over an organism’s regulatory mechanisms and evade the immune system to grow and metastasize. The mutational and morphological heterogeneity of cancer imposes additional requirements for AI in order to account for a variety of individual patient conditions and responses to treatment. Finally, DNA repair mechanisms in, e.g., cancer stem cells responsible for developing malignancies and drug resistance require efficient AI systems which join different treatment strategies into one common patient care plan.