ArticleBMC medical informatics and decision making2026
Agentic GPT-5.0 system outperforms standard large language models and human experts in critical care clinical decision-making: a simulation study.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAgentic artificial intelligence (AI) systems employing multi-model architectures with iterative reasoning may surpass standard single-model large language models (LLMs) in complex clinical decision-making. Comprehensive comparisons of agentic versus standard LLM deployment against human specialists in critical care remain limited.
objectiveThis simulation study compared the performance of an agentic system combining GPT-5.0 and Gemini 2.0 Flash against two standard LLMs (Gemini 2.0 Flash and GPT-4o) and human specialists in acid-base disorder interpretation and sepsis management using text-based clinical vignettes.
methodsForty-five clinical vignettes (20 acid-base, 25 sepsis) developed by an independent expert panel were evaluated by: (1) Gemini 2.0 Flash (standard single-turn); (2) GPT-4o (standard single-turn); (3) an agentic system combining GPT-5.0 and Gemini 2.0 Flash with multi-step reasoning and cross-verification; and (4) 20 board-certified physicians. Responses were anonymized and assessed by two blinded graders against pre-established gold standards using an explicit scoring rubric.
resultsFor acid-base disorders, the agentic system achieved 91.0% overall accuracy (95% CI 85.2-96.8%), significantly outperforming GPT-4o (78.0%, P = .002), Gemini (74.5%, P < .001), and human specialists (83.0%, P = .038). SSC hour-1 bundle compliance was 96.8% for the agentic system versus 82.4% for GPT-4o, 79.2% for Gemini, and 90.4% for humans (all P < .05). ROC analysis demonstrated superior discrimination for the agentic system (AUC = 0.932) compared to humans (0.856), GPT-4o (0.814), and Gemini (0.786). Subgroup findings in complex case categories are exploratory given small case numbers.
conclusionsIn this simulation study using text-based clinical vignettes, an agentic AI system combining GPT-5.0 and Gemini 2.0 Flash demonstrated significantly higher performance than standard LLM implementations and human medical specialists in structured tasks of acid-base interpretation and sepsis bundle compliance. These simulation-based findings suggest that agentic architectures may represent a promising direction for structured clinical decision support; prospective validation in real clinical environments with actual patient data is essential before implementation.
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