🤖 AI Summary
This study challenges the conventional paradigm in medical AI that equates “correctness” solely with performance metrics, focusing on the automated classification of plasma cells in bone marrow smears for multiple myeloma. It systematically examines core issues including label instability, model interpretability, clinically relevant evaluation, and human–AI responsibility allocation. Integrating medical image analysis, explainable AI, clinical assessment methodologies, and ethical frameworks for human–AI interaction, the work reconceptualizes “correctness” as a multidimensional construct encompassing data quality, interpretability, evaluation rigor, and accountability. The authors propose a theoretical framework tailored to dynamic clinical environments, uncover practical risks such as automation bias, and underscore the necessity of jointly considering technical performance and ethical practice to chart a responsible pathway for deploying medical AI systems.
📝 Abstract
This paper examines what it means for a medical AI system to be right by grounding the question in a specific clinical context: the automatic classification of plasma cells in digitized bone marrow smears for the diagnosis of multiple myeloma. Drawing on philosophy of science and research ethics, the paper argues that correctness in medical AI is not a singular property reducible to benchmark performance, but a multi-dimensional concept involving the availability of expertly labeled medical datasets, the explainability and interpretability of model outputs, the clinical meaningfulness of evaluation metrics, and the distribution of accountability in human-AI workflows. As such, the paper develops this argument through four interrelated themes: the instability of ground truth labels, the opacity of overconfident AI, the inadequacy of standard clinical metrics, and the risk of automation bias in time-pressured clinical settings.