Artificial intelligence is entering hand surgery through imaging, outcome prediction, and patient communication. Neural networks read scaphoid and distal radius radiographs. Machine learning models predict outcomes after carpal tunnel release. Large language models are being tested for patient communication and chart drafting. Adoption, however, has outpaced validation. Most hand surgery artificial intelligence tools are tested only on data resembling their training set, deployed in workflows that have not been audited, and rarely remeasured after release. The hand surgeon remains the clinical decision maker into whose workflow these tools are integrated, and is therefore accountable for the patient outcomes they shape, even when the model's internal workings remain opaque. We propose a four-part stewardship framework for the hand surgeon adopting these tools: external validation on the population the model will actually see; transparent disclosure of intended use and known failure modes; defined escalation paths when the model and surgeon disagree; and prospective audit after deployment. We set out the specific questions to ask a vendor, the reporting checklist items that support each question, and the performance metrics a surgeon needs to interpret before adopting a tool. This framework has implications for residency training, board certification, the editorial standards of hand surgery journals, and the regulatory pathways that bring such tools to the clinic.

First web space contracture is a common and potentially debilitating condition that impairs thumb positioning and compromises pinch and grasp. The complex, three-dimensional anatomy of the web space and the intricate biomechanics of the thumb contribute to substantial variability in both assessment methods and treatment strategies. At present, there is no universally accepted framework for defining, classifying, or measuring outcomes in first web space contracture. This review aims to synthesize current strategies for assessment and outcome evaluation, highlighting their limitations and areas of inconsistency. By clarifying existing methodologies and identifying gaps in the literature, this article aims to provide a foundation for more standardized, clinically meaningful evaluation and management of first web space contracture.

Our understanding of Kienbock disease has evolved significantly over the last decade with advances in the study of basic science and advanced imaging. Some patients are predisposed to the disease, and the evolution of the disease may be influenced by the patient's demographic. Both biomechanical and biological factors play a significant role. The morphology of the wrist and lunate influence loading mechanics through the central column. Both arterial and venous factors are important in the development of ischemia and the onset of a compartment syndrome of bone. Stability of the wrist plays a role. The disease affects the ligaments within and between the carpus and radius through synovitis or avulsion, and disruption of these can leading to collapse of the lunate and carpus. Better understanding of the disease pathogenesis will improve our ability to strategize surgical management.