Deterministic Math Solver Improves Clinical Calculations in LLMs
A new study shows that large language models can be made more reliable for clinical calculations by offloading arithmetic to a deterministic Python executor. The authors introduced a Program‑Solve interface where the model writes case‑specific code that a local executor runs, rather than performing the math itself.
Key points
- Program‑Solve interface writes Python for deterministic calculation, bypassing model arithmetic
- 32B Qwen model outperforms 7B by 7.05 points on 1,100 clinical cases
- 16 of 55 calculators flagged for guideline or coefficient concerns
Using the MedCalc‑Bench Verified dataset (1,100 cases, 55 calculators), the team evaluated two open‑weight Qwen models. The 7B variant achieved 75.31 % accuracy versus 72.02 % for direct arithmetic (+3.29 points), while the 32B model reached 90.53 % against 83.47 % (+7.05 points). A hand‑written library was exact on 440 supported cases but abstained on 40 % of the rest. The study also flagged 16 calculators with potential guideline or coefficient issues.
The results suggest that adding an executor helps larger models more than smaller ones, but it does not replace the need for verified formulas or reliable variable extraction.
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