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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2603.14158 |
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| _version_ | 1866915863326621696 |
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| author | Lopez, Ivan Everett, Selin S. Bunning, Bryan J. Liang, April S. Yao, Dong Han Vedak, Shivam C. Black, Kameron C. Ostmeier, Sophie Ma, Stephen P. Alsentzer, Emily Chen, Jonathan H. Chaudhari, Akshay S. Horvitz, Eric |
| author_facet | Lopez, Ivan Everett, Selin S. Bunning, Bryan J. Liang, April S. Yao, Dong Han Vedak, Shivam C. Black, Kameron C. Ostmeier, Sophie Ma, Stephen P. Alsentzer, Emily Chen, Jonathan H. Chaudhari, Akshay S. Horvitz, Eric |
| contents | Large language models (LLMs) are entering clinician workflows, yet evaluations rarely measure how clinician reasoning shapes model behavior during clinical interactions. We combined 61 New England Journal of Medicine Case Records with 92 real-world clinician-AI interactions to evaluate 21 reasoning LLM variants across 8 frontier models on differential diagnosis generation and next step recommendations under three conditions: reasoning alone, after expert clinician context, and after adversarial clinician context. LLM-clinician concordance increased substantially after clinician exposure, with simulations sharing >=3 differential diagnosis items rising from 65.8% to 93.5% and >=3 next step recommendations from 20.3% to 53.8%. Expert context significantly improved correct final diagnosis inclusion across all 21 models (mean +20.4 percentage points), reflecting both reasoning improvement and passive content echoing, while adversarial context caused significant diagnostic degradation in 14 models (mean -5.4 percentage points). Multi-turn disagreement probes revealed distinct model phenotypes ranging from highly conformist to dogmatic, with adversarial arguments remaining a persistent vulnerability even for otherwise resilient models. Inference-time scaling reduced harmful echoing of clinician-introduced recommendations across WHO-defined harm severity tiers (relative reductions: 62.7% mild, 57.9% moderate, 76.3% severe, 83.5% death-tier). In GPT-4o experiments, explicit clinician uncertainty signals improved diagnostic performance after adversarial context (final diagnosis inclusion 27% to 42%) and reduced alignment with incorrect arguments by 21%. These findings establish a foundation for evaluating clinician-AI collaboration, introducing interactive metrics and mitigation strategies essential for safety and robustness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14158 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Clinician input steers frontier AI models toward both accurate and harmful decisions Lopez, Ivan Everett, Selin S. Bunning, Bryan J. Liang, April S. Yao, Dong Han Vedak, Shivam C. Black, Kameron C. Ostmeier, Sophie Ma, Stephen P. Alsentzer, Emily Chen, Jonathan H. Chaudhari, Akshay S. Horvitz, Eric Human-Computer Interaction Machine Learning Large language models (LLMs) are entering clinician workflows, yet evaluations rarely measure how clinician reasoning shapes model behavior during clinical interactions. We combined 61 New England Journal of Medicine Case Records with 92 real-world clinician-AI interactions to evaluate 21 reasoning LLM variants across 8 frontier models on differential diagnosis generation and next step recommendations under three conditions: reasoning alone, after expert clinician context, and after adversarial clinician context. LLM-clinician concordance increased substantially after clinician exposure, with simulations sharing >=3 differential diagnosis items rising from 65.8% to 93.5% and >=3 next step recommendations from 20.3% to 53.8%. Expert context significantly improved correct final diagnosis inclusion across all 21 models (mean +20.4 percentage points), reflecting both reasoning improvement and passive content echoing, while adversarial context caused significant diagnostic degradation in 14 models (mean -5.4 percentage points). Multi-turn disagreement probes revealed distinct model phenotypes ranging from highly conformist to dogmatic, with adversarial arguments remaining a persistent vulnerability even for otherwise resilient models. Inference-time scaling reduced harmful echoing of clinician-introduced recommendations across WHO-defined harm severity tiers (relative reductions: 62.7% mild, 57.9% moderate, 76.3% severe, 83.5% death-tier). In GPT-4o experiments, explicit clinician uncertainty signals improved diagnostic performance after adversarial context (final diagnosis inclusion 27% to 42%) and reduced alignment with incorrect arguments by 21%. These findings establish a foundation for evaluating clinician-AI collaboration, introducing interactive metrics and mitigation strategies essential for safety and robustness. |
| title | Clinician input steers frontier AI models toward both accurate and harmful decisions |
| topic | Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2603.14158 |