## 5 Prediction with AI
### Surgery Prediction  
This problem has indeed been addressed by [PERSON] et al. [25] with the use of an EfficientNet-b4 that was used to identify if a patient was a candidate for surgery and if the type of surgery was either a total knee arthroplasty (TKA) or a medial unicompartmental knee arthroplasty (UKA). The total data set consisted, for each of the 2767 patients, in three radiographs (before data augmentation): the weight-bearing antero-posterior, the lateral and the patellar views. Relying only on these three views, commonly ordered by primary care providers, the machine performed the task with an 87.8% accuracy. Antromedial osteoarthritis is the main primary indication for UKA [25], and thus an even more significant outcome is that the result was obtained without the need of first identifying if the patient suffered from this pathology (hence without relying on a valgus stress view, which would have required a well-versed staff in order to be obtained). One limitation of the study, however, is that the information on which the model was trained was deeply based on the decision of the surgeons who performed the operations in the data-set, which are therefore taken as the \"ideal choice\"; it is, however, important to highlight that the follow-up data of the patients used to populate the training set indicated extremely few post-operative complications, therefore the choices made were considered as acceptable to represent the ideal result. Another TKA related problem is the access disparity, caused by the fact that knee surgeons caring for patients who would benefit from TKA are incentivized to \"cherry pick\" healthier patients and \"lemon drop\" those with increased comorbidities and case complexity that would result in higher costs. That is why [PERSON] et al. [26] trained an artificial neural network to analyze 15 major attributes related to the patient in order to predict the length of stay, charges and costs, and patient disposition. The 15 attributes were age, gender, ethnicity, race, type of admission, whether the admission was from the emergency department, All Patient Refined (ARP) risk of mortality, APR severity of illness, number of associated chronic conditions and diagnoses, comorbidity status, whether the admission was on a weekend, hospital type, income quartile of the patient and whether the patient was transferred from an outside hospital. The outputs, instead, were divided as usual in only two per type. For the length of stay and charges and costs, the model had to specify if the predicted value was higher or lower than the 50 th percentile (to enhance generalizability) and, for the patient disposition, if the patient would have been treated at home after the operation or not. After that, a risk-based patient-specific payment model was developed assessing the financial probability of over-cost related to APR risk of mortality. The APR score is part of the All Patient Refined-Diagnosis Related Group (APR-DRG)methodology that was developed by 3M to allow analysis of outcomes across large cohorts for a given diagnostic group [27]. This study actually included external validation, since training data were taken from the National Inpatient Sample database with information coming from multiple hospitals while the test data came from the Orthopedic Minimal Data Set Episode of Care (OME), which contained information from 11 different hospitals. Due to the lack of inpatient charges in the second database, external validation was possible only for the first two outputs. After the training, length of stay prediction had an internal accuracy of 75.3% and AUC of 74.8%; after external validation, instead, these values improved with an accuracy of 80% and AUC 83.2%. Similar results were obtained for the inpatient discharges while discharge disposition achieved the lowest results, meaning that other patient-level variables needed to be included.