Reflection on the Integration of Artificial Intelligence in Anaesthesiology: Beyond Algorithmic Performance

We read with great interest the review by Dost et al.¹, “Artificial Intelligence in Anaesthesiology: Current Applications, Challenges, and Future Directions.” The authors provide a comprehensive and forward-looking synthesis of how artificial intelligence is reshaping the perioperative continuum, from preoperative assessment to intensive care, education, and research. In particular, their discussion of decision-support systems and predictive analytics raises important questions about how algorithmic outputs should be interpreted and integrated into routine anaesthetic care. Their conclusions are consistent with recent literature describing the rapid expansion of data-driven technologies in modern anaesthetic practice. 2 While the technical progress described is impressive, several unresolved issues warrant closer attention if innovation is to genuinely improve patient safety. A central concern remains the gap between algorithmic performance and true bedside utility. Many tools achieve excellent technical metrics under controlled conditions, yet their translation into meaningful patient-centred outcomes is far less certain, as highlighted by recent methodological evaluations. 3 This distinction, clearly acknowledged by Dost et al. 1 , formed the primary motivation for our correspondence. This tension is particularly evident in the discussion of the hypotension prediction index, which Dost et al. 1 cite as a prominent example of predictive analytics in perioperative medicine. Although this technology demonstrates strong discriminative ability, its real-world clinical interpretation remains complex. The validation study by Davies et al. 3 illustrates important methodological considerations in assessing such tools but does not report a definitive positive predictive value in the main text, nor does it demonstrate that false alerts resulted in clinically inappropriate fluid or vasopressor administration. Nonetheless, in theory, frequent alerts with limited immediate clinical relevance may contribute to alarm fatigue or increased cognitive load, a phenomenon well described in the broader literature