Integrating AI into the Clinical Workflows Across the Cancer Care Continuum: Opportunities and Challenges

Abstract

Cancer cases are projected to hit 35 million worldwide by 2050, posing a significant burden on health care systems. The cancer care continuum has evolved to precision medicine practices, provisioning personalized treatments based on multimodal and multiomics data. Contextual analysis of such diverse, voluminous, spatiotemporal patient data is beyond human cognitive capacity. Artificial Intelligence (AI) technologies are reshaping the data mining paradigm in healthcare by delivering novel data-led insights in real time. AI-based methods for cancer risk predictions, diagnosis, prognosis, and therapeutics are developed, validated, and approved, indicating readiness for integration in clinical workflows. Additional validation of AI models using real-world data representing diverse populations is recommended to address clinical, technical, regulatory, ethical, and legal challenges, along with trust issues. Integrating AI tools into cancer care workflows to augment clinical decision-making, without compromising clinical autonomy and patient safety, is essential to address the increasing demand for cancer care by 2050.

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