Venkatesh Sivaraman, Joel Levin, Eric P. Mason, Victor Talisa, Suchi Saria, Jean Feng, Andrew J. King, Jeremy M. Kahn, Adam Perer · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.24313 · cited by 0 works (OpenAlex) · open-access PDF
La sepsis es una importante preocupación de salud pública, y muchos pacientes con sepsis mueren debido a la gestión hemodinámica ineficaz. La inteligencia artificial podría apoyar las decisiones hemodinámicas, especialmente en casos complejos donde la información conflictiva y las comorbididades conducen a la incertidumbre y a la variación de la práctica. Sin embargo, el valor real de estos sistemas depende no sólo de la precisión, sino de cómo influyen en la calidad de las decisiones clínicas. Primero evaluamos retrospectivamente las recomendaciones de un sistema de IA en 6.928 pacientes con sepsis, encontrando que mejoraron las decisiones de los proveedores originales en general, pero tenían un rendimiento significativamente peor en los casos complejos. Luego llevamos a cabo un experimento pre-registrado con 228 médicos de atención crítica estadounidenses
Machine translation (Meta M2M100); the English original follows.
Sepsis is a major public health concern, and many sepsis patients die due to ineffective hemodynamic management. Artificial intelligence could support hemodynamic decisions, particularly in complex cases where conflicting information and comorbidities drive uncertainty and practice variation. However, these systems' real-world value depends on not just accuracy, but how they influence the quality of clinician decisions. We first retrospectively evaluated an AI system's recommendations in 6,928 sepsis patients, finding that they improved upon the original providers' decisions overall but had significantly worse performance in complex cases. We then conducted a pre-registered experiment with 228 US critical care physicians to test their use of this imperfect advice in six complex treatment decisions, using one of three advice types designed to vary reasoning autonomy. Participants given simple model recommendations overcame poor advice, choosing optimal treatments in 56.3% of decisions compared to 31.8% if they had always followed the recommendations. They also made significantly better decisions than those receiving a less prescriptive advice type (42.9%). Physicians selectively adapted and improved weak advice, likely because it prompted them to reconsider their assessment. Our findings suggest that improving sepsis care decisions with AI may require accounting for these indirect influences resulting from human-AI collaborative reasoning.
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Topics: Advice (programming), Medicine, Sepsis, Intensive care medicine, Imperfect, Quality (philosophy)
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