Artificial, but is it intelligent?

Abstract

This editorial was not written by a chatbot, but it could have been.1 The expansion of abilities in artificial intelligence and machine learning (AI/ML) has led to a dramatic uptake in a variety of disciplines, with particular excitement in medical diagnosis and prognosis. Aside from its increasingly common use in the detection of large vessel occlusion for rapid stroke triage,2 recent applications of AI/ML in neurointervention have included patient selection3 and prediction of functional outcomes in mechanical thrombectomy,4–6 detection of catheter complications or undesirable embolization during endovascular intervention,7–9 and identification of patients with procedurally challenging arterial anatomy,10 among many others, employing AI/ML applications across large language models and computer vision. The state of the science of AI/ML in clinical outcome prediction in particular was recently summarized in the pages of this journal.11 A meta-analysis of 60 studies that used AI/ML to predict postoperative outcomes or complication after cerebrovascular or neuroendovascular surgery for stroke, aneurysm, or cerebral vascular malformation found relatively favorable performance compared with standard clinical prediction scales (area under the receiver operator characteristics curve (AUROC) >0.85 in most cases). Typically, such performance would be considered acceptable for clinical use. However, only 16.7% of such studies included external validation, and many had a high risk of bias. Given the rapid evolution of AI/ML in neurointervention, it is tempting for the clinician to lean more and more on this technology for diagnosis, prognosis, and clinical decision-making. However, we identify areas of concern that must be addressed in …