**6. Postoperative applications**

The goals of the postoperative phase of care include predicting prognosis, identifying potential postoperative complications, and optimizing variables for enhanced aftercare and recovery. A study by Arvind et al. demonstrated that ANN and LR are superior to the American Society of Anesthesiologist (ASA) class in predicting the incidence of the cardiac, wound, VTE, and mortality in patients undergoing anterior cervical discectomy and fusion (ACDF) [146]. Similarly, Kim et al. found ANN and LR to be more accurate than ASA classification for predicting the same complications in posterior lumbar fusion [147]. AI has also allowed for greater distinction between disease progression versus tumor necrosis from radiation therapy in gliomas [144, 148].

Follow up in the postoperative phase can be simplified using telemedicine with smart phone apps, video conferencing or simple phone communication. A prospective trial by Reider-Demer et al. found that telemedicine postoperative follow up for patients who underwent elective intracranial neurosurgery was a safe and effective alternative to in-office visits [149]. What's more, the patients preferred the convenience of telemedicine visits.

It has been estimated that doctors spend up to 50% of their time on documentation, and nurses 20% [150]. Moreover, the initiation of the twenty first Century Cures Act has created a great need for methods to quickly produce summaries and communications that are easily understood [151]. Once further refined, LLMs could be invaluable tools to help fill this gap by generating rudimentary plain language medical information that can be modified by clinicians. They can also be used to generate authorization letters and various other types of documentation based on keywords. This would drastically reduce the amount of time spent on documentation and allow physicians as well as other medical providers to devote more of their time to patient care.
