Simon Williams

Marco Scarpa

Hani J. Marcus

Computational power has risen exponentially in recent decades, providing new opportunities in many fields, including that of surgical oncology. One branch of information technology permeating many aspects of healthcare is artificial intelligence (AI), and the incorporation of deep learning neural networks into clinical care. AI enables the computational analysis of vast amounts of data, and the learned ability to contextualize this information digitally through sophisticated algorithms, opening a number of opportunities in the clinical setting. This Research Topic aims to bring together the latest research in the field to demonstrate how these tools can be of benefit to surgical oncologists and patients alike. Oncological surgeons are tasked with making difficult clinical decisions, simultaneously weighing up patient data with expected outcomes, whilst keeping in mind the risks of subsequent morbidity and mortality. Incorporating deep learning algorithms in this field would enable mass clinical, biochemical, and radiological data analysis with numerous potential benefits. Current algorithms have been shown to be efficacious and efficient in making prognostic predictions and making post-surgical outcome assessments using data from various sources. This Research Topic invites manuscript submissions which demonstrate utilizations of AI as a tool to support surgical oncologists through their advanced data analysis capabilities. Manuscript submissions which showcase new opportunities for the incorporation of these systems into surgical practice are welcome, as well as how AI can support clinical decision making, and be used in the training of new oncological surgeons.

Hani J. Marcus

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