Beyond binary diagnosis: Key questions on AI accuracy, real-world applicability, and safety in clinical decision support.

This comment relates to Kücking et al.'s (2026) study on the bidirectional effects of artificial intelligence recommendations and healthcare provider related factors on the accuracy of wound impregnation diagnosis. While acknowledging the valuable contributions of this research, including distinguishing between correct/incorrect artificial intelligence outputs, rigorous simulation design, and emphasis on clinical safety, we have raised key questions to enhance the interpretation of results and real-world translation. The main focuses include the moderating role of artificial intelligence system accuracy in automation bias, external effectiveness in real clinical environments, potential mechanisms for gender differences in diagnostic performance, the impact of visual cue design on decision-making, and the potential of explainable artificial intelligence (XAI) in risk mitigation. This review aims to promote further research and facilitate the safe and effective integration of artificial intelligence based clinical decision support systems (CDSS) into clinical practice.