Artificial intelligence for clinical oncology

Abstract

Clinical oncology is experiencing rapid growth in data that are collected to enhance cancer care. With recent advances in the field of artificial intelligence (AI), there is now a computational basis to integrate and synthesize this growing body of multi-dimensional data, deduce patterns, and predict outcomes to improve shared patient and clinician decision making. While there is high potential, significant challenges remain. In this perspective, we propose a pathway of clinical cancer care touchpoints for narrow-task AI applications and review a selection of applications. We describe the challenges faced in the clinical translation of AI and propose solutions. We also suggest paths forward in weaving AI into individualized patient care, with an emphasis on clinical validity, utility, and usability. By illuminating these issues in the context of current AI applications for clinical oncology, we hope to help advance meaningful investigations that will ultimately translate to real-world clinical use.

Keywords: artificial intelligence; care pathway; clinical oncology; clinical translation; precision medicine.

Figure 1:

Narrow task-specific AI applications addressing a specific touchpoint along the patient pathway, and utilizing a specific data type and AI method.

Figure 2:

An example cancer patient pathway converges with an ever-increasing data stream. Potential AI applications and exemplary clinical users at each touchpoint are also illustrated.

Figure 3:

Bridging the AI translational gap between initial model development and routine clinical cancer care by emphasizing and demonstrating three essential concepts: clinical validity, utility, and usability.