Open medical models passed Spain's residency exam—and still gave unsound reasons
Four open models answered 1,552 Spanish MIR questions. Three larger systems reached strong estimated ranks, yet 13–38% of their correct answers included at least one clinically unsound rejection of a distractor.
The short version
- What happened
- Researchers scored four open models of 8–27 billion parameters on 1,552 official MIR questions from eight examination years.
- Who it affects
- A trainee or clinician can receive the correct multiple-choice answer for the wrong medical reason. That is especially risky when the explanation—not the option letter—is what guides learning or care.
- What remains unproven
- The study is a preprint and has not been peer reviewed.
Keep in mind
The study is a preprint and has not been peer reviewed.
Only four open models, one Spanish prompt and direct answering were tested; larger or reasoning-focused systems may behave differently.
An LLM, not a physician, graded the model rationales; the authors identified judge errors on negatively worded options.
Rank mappings are estimates and the image questions may often be answerable from text alone.
Source analysis · 1 linked source. Primary analysed source date: 22 October 2026.
Read the full report · 3 min The evidence, methods, implications and limitations
A high rank concealed several different weaknesses
The team reconstructed 2,595 MIR questions and used 1,552 scorable questions from 2018–2025 for the main ranking and calibration tests. Qwen3-8B, Qwen3-14B, Gemma-3-27B and MedGemma-27B answered deterministically under the exam's reward and penalty rules. The larger models scored well enough for many specialty choices, but the rank estimates depend partly on interpolated 2023 and 2024 allocation data.[1]
Correct answers were not always clinically well reasoned
For 123 questions with physician commentary, the researchers asked an external Llama model to compare each model's reasoning with the clinical explanation. Among correctly answered questions, 13–38% included at least one distractor rejected for a wrong or unsupported reason. No physician validated that automated judge, so the exact rates remain provisional.[1]
The distinction that matters
Four checks behind one exam headline
The paper separates performance that a single accuracy score would merge.
Rank
Score 1,552 MIR questions
Strong estimated placement for three larger models
Reason
Compare rationales on 123 questions
13–38% of correct answers had an unsound distractor reason
Image
Show, remove or swap 171 clinical plates
No material accuracy benefit from the correct plate
Hospitals should test the task, not trust the label
Medical fine-tuning reduced average overconfidence but also reduced accuracy and did not improve how well confidence separated right from wrong answers. The two vision models changed some answers when images changed, yet real images produced no detectable accuracy gain over no image or the wrong image. Before local deployment, clinicians need independent, task-specific tests with patient cases, expert review and real deferral costs.[1]