J Neurosonol Neuroimag.  2025 Jun;17(1):1-10. 10.31728/jnn.2025.00171.

Rethinking Clinical AI Applications in Stroke - Pitfalls, Misconceptions, and Directions for Responsible Use

• Park S

• KMID: 2569346
• DOI: http://doi.org/10.31728/jnn.2025.00171

Abstract

Artificial intelligence (AI), particularly deep learning, continues to advance in medical image analysis and clinical prediction. In stroke care—where timely, accurate decisions are critical—AI is seen as a promising tool, with potential to detect complex imaging patterns and enhance clinical workflows. However, practical application in real-world settings remains limited, often due to structural issues in model design, evaluation, and insufficient integration of clinical context. This narrative review examines common pitfalls in developing and applying AI models in stroke care. High performance alone does not ensure clinical value; what matters is whether the predicted target (label) is clinically meaningful and well-defined. If the label is ambiguous or fails to reflect the underlying clinical condition, even highly accurate models may produce misleading or unhelpful outputs. We also discuss limitations of current explainability tools and emphasize that lack of interpretability hinders trust and adoption in high-stakes decisions. Rather than functioning as autonomous decision-makers, AI models are better positioned as coordinators or accelerators—supporting, not replacing, clinical judgment. For responsible integration into practice, developers must disclose key aspects of the model, including training data, label definitions, and performance conditions. Clinicians, in turn, should be prepared to interpret evaluation metrics in the context of real-world care. Ultimately, clinical AI should focus not merely on maximizing performance but on solving problems relevant to clinical practice, with transparency and explainability as essential prerequisites for adoption.

Keyword

artificial intelligence; deep learning; stroke; clinical decision-making; explainability