AI Chatbots Show an Accuracy-Practicality Trade-off in Anesthesia Crisis Scenarios

When a patient goes into cardiac arrest while lying prone on an operating table, a junior anesthesiologist has seconds to identify the most likely cause and act on it. A new benchmark study from Chinese hospitals suggests that the large language models many young physicians now consult first may not reliably make that leap. The research, published in Anesthesiology and Perioperative Science, evaluated OpenAI-o1 and DeepSeek-R1 across 30 anesthesia crisis scenarios and found a striking split: the more accurate model produced vaguer guidance, while the less accurate one gave the detailed, stepwise instructions that junior doctors actually wanted to use.

The study, led by Zhuoxi Wu, Yuting Tan, Qin Chen and colleagues with senior author Hong Li, was designed around a problem the authors call the competency transition period. Junior physicians moving from supervised training to unsupervised practice must integrate fragmented knowledge, master complex decision-making and adapt to high-intensity workflows, and in anesthesiology errors can directly jeopardize patient safety. Residents commonly experience anxiety during their first phase of independent clinical work, and traditional support resources fall short of real-time demands while senior mentors are constrained by time and staffing. With DeepSeek’s rapid deployment in Chinese healthcare systems, the authors note, physicians are increasingly adopting a consult-the-model-first habit, despite the well-known risk of hallucinations, meaning plausible but factually incorrect outputs that inexperienced clinicians may be poorly equipped to catch.

To build the test, twelve senior anesthesiologists used a Delphi consensus process to create a repository of anesthesia crisis scenarios covering four modules: perioperative differential diagnosis, urgent decision-making, guideline updates, and ethics and communication. Sixty standardized items were refined through multiple rating rounds, with questions retained if their median importance score reached at least 3.5 on a Likert scale and agreement was tight, and thirty emergencies were finally selected and allocated across the modules in a 30:30:30:10 ratio. Chinese scenarios were forward- and back-translated by professional medical translators and reconciled by bilingual anesthesiologists to ensure cross-language comparability.

Three model-language combinations were tested: OpenAI-o1 with English input and output, DeepSeek-R1 in English, and DeepSeek-R1 in Chinese, abbreviated OA, DSE and DSC. Each scenario was delivered as a single prompt beginning with the fixed instruction to answer as an anesthesiologist, with no additional system instructions, prompt engineering or manual adjustment of generation parameters. Responses were collected over two days in March 2025 by an independent researcher, and every output was triple-verified against current guidelines, textbooks or expert-curated standard answers. Twenty experts and twenty junior physicians then evaluated the responses, with a month allowed for thorough assessment.

The evaluation was dual-axis. Experts rated medical accuracy on a 5-point Likert scale and classified any errors by type, and they scored clinical logicality using the SBAR framework, the Situation-Background-Assessment-Recommendation structure originally developed for clinical handoffs, with each of the four components rated from 0 to 2 for a total of 0 to 8. Junior physicians rated practicality on a 5-point scale across three subdimensions: step clarity, guideline applicability and learning assistance. Readability was measured with Flesch-Kincaid indices.

The headline result was an inverse relationship between the two primary domains. OpenAI-o1 showed superior accuracy, with the ordering OA greater than DSE greater than DSC supported by a posterior probability of 0.94 and a Bayes factor of 4.70, which the authors classify as strong evidence. DeepSeek-R1 in Chinese led on practicality, with the reverse ordering supported by a posterior probability of 0.82 and a Bayes factor of 3.67. The same pattern held in the subdimensions: DSC performed best on step clarity with a posterior probability of 0.75 and on guideline applicability with 0.82, while learning assistance favored OA and DSE over DSC with weaker support. The authors illustrate the trade-off with an anaphylaxis example: OpenAI-o1 generically recommended epinephrine without error, DeepSeek-R1 in Chinese suggested an incorrect initial intravenous dose of 0.3 to 0.5 milligrams when the appropriate IV starting dose is 0.01 to 0.1 milligrams, and DeepSeek-R1 in English correctly recommended intramuscular epinephrine at 0.3 to 0.5 milligrams but omitted the intraoperative IV dosing common in surgical settings.

The SBAR analysis exposed a deeper reasoning failure on complex tasks. In low-complexity scenarios such as differential diagnosis, all three models performed equivalently across accuracy, logicality and every SBAR component, with decisive Bayesian support for the equality hypothesis, indicating that foundational anesthesia reasoning was not significantly affected by input language. In high-complexity urgent decision-making, however, overall clinical logicality remained statistically equivalent across models, yet the Situation and Assessment components failed to reach the predefined support threshold of a posterior probability of at least 0.5, even with moderate to strong Bayes factors. Examination of raw responses showed that models frequently listed potential diagnoses without prioritizing them by clinical context, a pattern experts labeled Diagnostic Assessment Omissions. In the prone-position cardiac arrest example, the models correctly recognized the arrest but enumerated all reversible causes without flagging venous air embolism as the primary concern, a failure the authors interpret as an absence of a robust Situation-to-Assessment linkage.

The junior physician panel added a human dimension that complicates any purely accuracy-based verdict. Only 28.6 percent of junior physicians said OpenAI-o1 alleviated their anxiety about managing anesthesia crises, compared with 95.2 percent for DeepSeek-R1 in Chinese and 91.5 percent for the English version. Respondents emphasized a need for concrete operational guidance over theoretical repetition, and in format preferences flowcharts and tables ranked highest at 52.56 percent, followed by stepwise text at 26.46 percent. A readability paradox sharpened the picture: OpenAI-o1 produced the most complex text with a mean Flesch-Kincaid score of 17.24, DeepSeek-R1 in English scored 16.9, and the Chinese version scored 14.75, so the most accurate model wrote the hardest text while the least accurate wrote the simplest, and practicality rankings ran exactly opposite to readability. The authors distinguish syntactic readability from clinical actionability, noting that abstract outputs demand extra cognitive translation while operationally explicit outputs directly serve real-time decisions, even when some details are wrong.

Language and context also shaped performance in ways the authors caution cannot be attributed to model architecture alone, since OpenAI-o1 was tested only in English while DeepSeek-R1 was tested in both languages. DeepSeek-R1 in Chinese more often cited Chinese laws, regulations and culturally contextualized ethical communication, whereas the other combinations offered generalized recommendations such as noting that rules vary by state or country. The authors frame this as a localization advantage reflecting corpus composition rather than intrinsic superiority, and they warn that reliance on static training data risks outdated regulatory information. Expert feedback word clouds flagged Procedure Priority Errors, Drug Dosage Deficiencies and Diagnostic Assessment Omissions as the most frequent reasoning breaks, alongside Legal Basis Deficiency in cross-cultural medicolegal scenarios.

The team is careful about scope. The sample came from Southwest China, limiting generalizability; the evaluation measured short-term performance on simulated cases with subjective scoring and should not be read as evidence of real-world clinical effectiveness or patient-safety impact; the Bayes factor simulations assumed independent observations and did not model within-rater or within-scenario correlations, which may make the evidence estimates optimistic; and the models represent a snapshot that may change with updates. On that basis the authors propose a conceptual Precision-Dynamic-Closed-Loop ecosystem combining curated case libraries, dynamic expert validation and continuous error feedback, while stressing that it remains theoretical and requires prospective real-world validation. Their conclusion is blunt: what junior physicians perceive as most useful is not necessarily what is most reliable, and evaluating clinical AI on accuracy alone overlooks how such tools are actually used under pressure. Until specialty-specific evaluation frameworks and safeguards exist, the study suggests, the fast, detailed answers young anesthesiologists find most reassuring may also be the ones most in need of an expert’s second look.

Subject of Research: Benchmark evaluation of OpenAI-o1 and DeepSeek-R1 for supporting junior anesthesiologists in anesthesia crisis scenarios

Article Title: Benchmark evaluation of OpenAI-o1 vs DeepSeek-R1 for junior anesthesiologists in Chinese hospitals: hazards of fast large language models use in high-stakes specialties

Article References: Wu, Z., Tan, Y., Chen, Q., Ye, X., Zhang, Q., Bi, J., & Li, H. (2026). Benchmark evaluation of OpenAI-o1 vs DeepSeek-R1 for junior anesthesiologists in Chinese hospitals: hazards of fast large language models use in high-stakes specialties. Anesthesiology and Perioperative Science, 4(1), Article 40. https://doi.org/10.1007/s44254-026-00176-z

Image Credits: AI Generated

DOI: 10.1007/s44254-026-00176-z

Keywords: large language models, anesthesiology, OpenAI-o1, DeepSeek-R1, junior physicians, clinical decision support, SBAR framework, Bayesian analysis, medical accuracy, hallucinations, patient safety, China