Reply to Vargas, Peirano et al.

Personalized medicine, a term growing in popularity, is a pioneering approach to tailoring patient treatment based on multiple data sources; however, the current lack of diverse data sources limits the implantation of artificial intelligence (AI)-based precision medicine models.1 The models we created to predict the complexity of Mohs micrographic surgery (MMS) components2 are intended to optimize patient treatment. Although the models' features are objective, the results themselves were surgeon-dependent. Nonetheless, we hoped that the quantity of data would shield the models from their current single surgeon limitation. The external case validation provided by Vargas, Peirano et al. produced poor prediction results in both models. When publishing our AI models, we aspired to earn surgeon trust by making the models as interpretable as possible, both globally and locally.3 We used a decision tree method with standard clinical features to support global interpretation. To support local interpretation, we provided a web-based app4 to help surgeons understand three factors: how each decision was made, what each model's certainty was for each decision, and most importantly, how each feature contributed to the model's decision. In Vargas, Peirano et al., which presented demographic and tumor data of the test population studied, we saw that the population demographic did not vary significantly from the core models; however, several features did differ in the Chilean patient cohort. The Chilean cohort presented with tumors that were more scarred (32% vs. 8.2%), were flatter (51% vs. 14%), and required twice as much complicated wound reconstruction (35% vs. 16%), respectively, than the Shoham et al. cohort. It would be of great interest to gain the following insights from the external validation from Vargas, Peirano et al.: in which cases the model did perform well and how the tumor features mentioned above influenced the models' performance. Another factor to consider is that the vast majority of the models' experience was acquired in an outpatient setting. While this is in line with most MMS surgeries, it may affect the models' prediction power for complex reconstruction procedures, most likely performed in hospital settings. The suggestion to include the tumor subtype was discussed thoroughly in our publication. We believe that the benefit of a model without a subtype is that a feature's benefits outweigh its flaws. Regarding the inclusion of tumor photographs, this would have required a much larger data set, in the order of tens of thousands of photographs; moreover, the computing power needed would make the models less accessible. In conclusion, we thank Vargas, Peirano et al. for their external validation. We agree that to obtain genuinely robust models, we need to increase the number of surgeons and centres participating and providing model-training data. We are picking up the gauntlet and have added a “Contribute” section to our web app: https://www.mms-prediction.com/contribute,4 in which surgeons can share their experiences and results, thus helping train the models that will serve the MMS surgeon community. We hope that Vargas, Peirano, their colleagues and others will participate in this endeavour. We would like to thank our patients, as we continue to strive for delivering better treatment. All authors contributed equally to this work. The authors have no conflict of interest to declare.