Artificial intelligence in cancer research and precision medicine

Artificial intelligence (AI) has experienced explosive growth in oncology and related specialties in the past few years. The improved expertise in data capture, the increased ability in aggregation and analytic effort, along with decreasing costs of genome sequencing and related biologic “omics,” set the foundation and need for novel tools that can meaningfully process these data from multiple sources and types and provide value across biomedical discovery, diagnosis, prognosis, treatment, and prevention, in a multimodal fashion. However, while big data and AI tools have already revolutionized many fields, medicine has partially lagged due to its complexity and multidimensionality, leading to technical challenges in developing and validating solutions that generalize to diverse populations. Indeed, inner biases and miseducation of algorithms, in view of their implementation in daily clinical practice, are becoming relevant concerns. In fact, AI could mirror the unconscious thoughts, racism, and biases of the humans who generated these algorithms. So, a potential worsening of existing health disparities is possible, especially without a thoughtful, transparent, and inclusive approach that involves addressing bias in algorithm design and implementation along the cancer care continuum. In this chapter, a broad landscape of major applications of AI in cancer care is provided, with a focus on cancer research and precision medicine. Major challenges posed by the implementation of AI in the clinical setting will be discussed. Potentially feasible solutions for mitigating bias are provided, in the light of promoting cancer health equity.