{
  "abstract": "Background\n\nThe gap between junior and senior physicians in anesthesia is exacerbated by the reliance on experience. Large language models like DeepSeek-R1 offer a potential solution as \"cognitive scaffolding.\"Methods\n\nA simulation-based, multi-arm controlled study with randomized junior groups and a non-randomized senior expert comparator was conducted. Forty-eight (48) anesthesiologists, including 16 senior experts and 32 junior residents, were recruited and randomly assigned (using a computer-generated randomization sequence) to two groups: Junior-alone and Junior + AI. Participants evaluated 20 standard clinical scenarios across four clinical domains: airway management, hemodynamic regulation, anesthetic pharmacology, and crisis management. The AI-assisted group followed a triple-step method: independent initial decision, subsequent AI consultation, and decision revision. Their final performance was compared against sixteen (16) senior expert anesthesiologists who served as the gold standard.Results\n\nDeepSeek-R1 successfully improved performance in all four domains for junior doctors. Compared to the Junior-alone group, the Junior + AI group achieved significantly higher scores in accuracy (86.00 ± 16.61 vs 61.67 ± 19.45, P < 0.0001) and comprehensiveness (84.33 ± 17.18 vs 53.67 ± 23.27, P < 0.0001). However, further analysis revealed that while significantly improved, both accuracy and comprehensiveness remained lower than the senior expert scores (differences not statistically significant in mixed-effects analysis: P = 0.349 for Accuracy; P = 0.335 for Comprehensiveness). Performance in safety showed no significant difference to expert standards (P = 0.644). The AI-assisted group required slightly less total time (33.3 vs 33.8 min), showing no significant delay. 65% of AI suggestions were adopted overall.Conclusion\n\nDeepSeek-R1 shows promise as \"cognitive scaffolding\", supporting knowledge externalization for junior anesthesiologists. It substantially improves decision-making, though a numerical gap with experts persists in certain dimensions. The high risk of automation bias in critical scenarios requires targeted training on critical evaluation of AI outputs.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Anesthesiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.",
    "Department of Anesthesiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. wangzhipeng@gdph.org.cn."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Huiju Yang"
    },
    {
      "affs": [
        0
      ],
      "name": "Guangyan Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Zhipeng Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6aznt61vcyb6e.bib",
  "cited_by": 0,
  "doi": "10.1186/s12871-026-04173-4",
  "fulltext_url": "https://europepmc.org/articles/PMC13749728",
  "id": "6aznt61vcyb6e",
  "issns": [
    "1471-2253"
  ],
  "landing_url": "https://europepmc.org/article/MED/43009790",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7202395931",
  "pdf_url": "https://europepmc.org/articles/PMC13749728?pdf=render",
  "pmcid": "PMC13749728",
  "pmid": "43009790",
  "published": "2026-08-14",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/6aznt61vcyb6e.ris",
  "sources": [
    {
      "first_seen": "2026-11-12T15:22:43Z",
      "native_id": "MED:43009790",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/43009790"
    },
    {
      "first_seen": "2026-11-14T12:22:27Z",
      "native_id": "PMC13749728",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s12871-026-04173-4"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Bridging the experience gap: reasoning-oriented LLMs as cognitive scaffolds for junior anesthesiologists-a multi-arm controlled study.",
  "title_text": "Bridging the experience gap: reasoning-oriented LLMs as cognitive scaffolds for junior anesthesiologists-a multi-arm controlled study.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-14T12:22:27Z",
  "url": "https://research.draconitic.com/paper/6aznt61vcyb6e",
  "venue": "BMC anesthesiology",
  "versions": []
}