As cognitive computing healthcare decisional support tools can read patient characteristics from microelectronic health records and then present treatment selections based on these characteristics, medical literature, and real-clinical evidence (RCE), they may be useful for both seasoned and inexperienced oncologists. The extent to which augmented intelligence systems conform to established medical practices and how they could influence surgical decision-making is still poorly understood. Experts in the field of Breast Oncology agreed with the “recommended” choice made by cognitive computing in 79.6% of cases, and with the “for consideration” choice made in 10.2% of cases, for a total agreement rate of 87.9%. The 8% of the situations yielded 59% of the replies that did not agree. According to the Cota observational database, “recommended” was given to 70.2% of matched controls, “for consideration” to 13.5%, and “not recommended” to 20.4%. Rookie oncologists choose 75.5% of recommended/for consideration medicines without Watson for Oncology (WFO)/Cota RCE and 95.3% with it. As a result of not using Watson for Oncology/Cota RCE, novices were 39% more likely to select an unrecommended option than experts. Treatment decisions made by Breast Oncology novices were shown to be significantly enhanced when Watson for Oncology was used in conjunction with Cota RCE. Offer a concise description of “Watson for Oncology” as an AI-driven system that provides treatment recommendations based on up-to-date scientific and clinical information. The fact that almost 20% of people with similar sickness characteristics in a real-world database were given non-recommended options demonstrates the necessity for decision help.