Medical Fine-Tuning Reshapes, Not Reduces, Patient-Facing AI Risk: A Rubric-Based, LLM-Judge Evaluation Framework

Savan Doshi

cs.AIOct 17, 2026

Medical Fine-Tuning Reshapes, Not Reduces, Patient-Facing AI Risk: A Rubric-Based, LLM-Judge Evaluation Framework

Authors: Savan Doshi

Abstract

Patient-facing medical question answering with large language models (LLMs) is moving from research demonstrations to production deployments, yet evaluation remains anchored on factual correctness. Factuality is necessary but not sufficient: a response can be factually grounded and still issue a directive that, if acted on, would harm the patient. We introduce a risk-sensitive evaluation framework that scores responses by the presence and severity of six categories of risk-bearing language (treatment directives, contraindications, dosage expressions, urgency-triage cues, high-alert medication mentions, and overconfident assertions), anchored on published clinical harm taxonomies. Severity is classified by a three-judge LLM ensemble. We apply this framework to six 7-9B instruction-tuned models, three general (Mistral-7B, Phi-3.5-mini, Llama-3.1-8B) and three medically adapted (BioMistral-7B, OpenBioLLM-Llama3-8B, Med42-Llama3-8B), on 200 real patient questions from the K-QA benchmark. Two findings emerge. First, ensemble risk-bearing language is statistically independent of factual hallucination rate (pooled Spearman $ρ= -0.065$ , $n = 300$ , 95% CI $[-0.18, +0.05]$ ; distance correlation $= 0.10$ , independence test $p = 0.24$ ). Second, medical fine-tuning does not reduce aggregate risk but reshapes it: continued-pretraining adaptations (BioMistral, OpenBioLLM) consistently suppress high-alert medication mentions while amplifying overconfident assertions, whereas clinical instruction-tuning (Med42) suppresses dosing only, without amplifying overconfidence. Per-category Krippendorff $α$ is fair to good for the four linguistically anchored categories (0.53-0.71) and low for the two clinical-judgment-dependent categories (0.19, 0.31), indicating where automatic safety evaluation is and is not yet reliable. We release the rubric, judge prompts, and evaluation code.

Figures & tables

Category	Harm pathway	Key evidence
treatment_directive	Patient-initiated medication interruption	DOAC interruption: thromboembolic events rise from 1.0 to 21.6 per 100 patient-years ( Vene et al., 2016 ) ; statin discontinuation associated with near-doubling of all-cause mortality ( Peixoto et al., 2024 ) ; insulin self-administration errors cause severe hypoglycemia and DKA ( Grissinger, 2016 )
dosage_expression	Expression format drives error rate, not dose magnitude	84.4% caregiver dosing-error rate; teaspoon vs. mL units OR $=4.6$ ( Yin et al., 2016 ) ; metric-only instructions reduce error $>$ 50% ( van Ittersum & Wansink, 2016 ) ; AAP recommends metric-only patient-facing dosing ( American Academy of Pediatrics, 2015 )
urgency_triage	Over-reassurance delays time-critical care	Help-seeking delayed months–years for cancer after “all-clear” ( Renzi et al., 2015 ) ; routing acute MI through primary care nearly doubles time-to-hospital ( Rawles et al., 1993 ) ; symptom-checker apps miss $>$ 40% of emergencies ( Schmieding et al., 2022 )
high_alert_medication	Instruction structure drives harm, not drug misidentification	$\sim$ 100,000 insulin-related ED visits and 30,000 hospitalizations annually in the US ( Geller et al., 2014 ) ; capecitabine over-adherence without “stop-and-call” framing potentiates toxicity ( Bourmaud et al., 2015 ; Le Saux et al., 2018 )
overconfident_assertion	Confidence asserted independently of accuracy	Overconfidence is a leading contributor to diagnostic error ( Berner & Graber, 2008 ) ; LLMs assert wrong and right answers with nearly identical confidence ( Omar et al., 2025 ; Rosenbacke et al., 2025 )
contraindication	Closest evidence is clinician-side DDI alert fatigue	$>$ 90% DDI alert override rate with documented adverse-event consequences ( Phansalkar et al., 2013 ; Villa Zapata et al., 2022 ) . Patient-facing evidence is a research gap this framework helps surface.

Table 1: Each category corresponds to a class of patient-facing communication for which published patient-safety literature has documented harm consequences independent of factual accuracy. Five of six categories are supported by multi-decade clinical-safety literature; contraindication is retained as a research gap.

General model	Medical counterpart	Adaptation pipeline
Mistral-7B-Instruct-v0.3	BioMistral-7B ( Labrak et al., 2024 )	Continued pretraining (PMC, PubMed)
Phi-3.5-mini (3.8B)	OpenBioLLM-Llama3-8B ( Pal, 2024 )	Continued pretraining + medical SFT
Llama-3.1-8B-Instruct	Med42-Llama3-8B ( Christophe et al., 2024 )	Clinical instruction-tuning (full-param FT)

Table 2: Six 7–9B models as three medical/general pairs. Mistral/BioMistral and Llama-3.1/Med42 share base architecture; Phi-3.5/OpenBioLLM is a scale-matched comparison (3.8B vs. 8B) with different base architectures.

Figure 1: Ensemble RSHS vs. NLI hallucination rate across six models on 50 K-QA prompts ( $n=300$ ). Pooled Spearman $\rho=-0.065$ , 95% bootstrap CI $[-0.18,+0.05]$ : the two evaluation axes are statistically independent.

Model	$n$	Spearman $\rho$	95% bootstrap CI
Mistral-7B	50	$-0.135$	$[-0.40,+0.15]$
Phi-3.5-mini	50	$+0.274$	$[-0.04,+0.55]$
Llama-3.1-8B	50	$-0.184$	$[-0.44,+0.08]$
BioMistral-7B	50	$-0.013$	$[-0.27,+0.26]$
OpenBioLLM-Llama3-8B	50	$-0.139$	$[-0.41,+0.16]$
Med42-Llama3-8B	50	$-0.109$	$[-0.39,+0.20]$

Table 3: Spearman correlation between ensemble RSHS and NLI hallucination rate, per model and pooled. CIs from 5000-resample percentile bootstrap. No individual model shows a significant correlation; the pooled CI tightly bounds

|\rho|\leq 0.18

Figure 2: Per-category presence rates with 95% bootstrap CIs (5000 resamples) for the six models, grouped by family pair. BioMistral and OpenBioLLM show reduced presence of high-alert medication mentions relative to their general counterparts; severity-level amplification of overconfident assertion is reported separately in Table 5 .

Model (type)	$n$	Mean RSHS (95% CI)
Mistral-7B (gen)	200	1.43 [1.31, 1.56]
Phi-3.5-mini (gen)	200	1.24 [1.11, 1.38]
Llama-3.1-8B (gen)	200	1.28 [1.14, 1.43]
BioMistral-7B (med)	200	1.41 [1.26, 1.55]
OpenBioLLM-Llama3-8B (med)	200	1.33 [1.19, 1.47]
Med42-Llama3-8B (med)	200	1.25 [1.11, 1.39]

Table 4: Mean ensemble RSHS per model. CIs from 5000-resample percentile bootstrap. All six models fall within a 0.19 band; medical and general groups overlap.

	Mistral $\to$ BioMistral		Phi-3.5 $\to$ OpenBioLLM		Llama-3.1 $\to$ Med42
Category	$\Delta$	$p$	$\Delta$	$p$	$\Delta$	$p$
treatment_directive	$-0.034$	0.49	$-0.029$	0.51	$+0.032$	0.63
contraindication	$-0.009$	0.50	$+0.005$	1.00	$-0.002$	0.91
dosage_expression	$-0.083$	0.07	$\mathit{-0.169}$	0.0003	$\mathit{-0.125}$	0.013
urgency_triage	$\mathit{-0.155}$	0.005	$+0.028$	0.52	$-0.020$	0.80
high_alert_medication	$\mathit{-0.154}$	$\mathbf{<10^{-4}}$	$\mathit{-0.086}$	0.0004	$+0.020$	0.98

Table 5: Per-category paired Wilcoxon signed-rank tests (

\Delta

= medical minus general mean severity). Italic

\Delta

: significant suppression; bold

\Delta

: significant amplification.

n=200

per pair. Mistral and Phi pairs show the reshape pattern; Llama/Med42, trained via clinical instruction-tuning, suppresses dosing only. Bonferroni-Holm and Benjamini-Hochberg (FDR = 0.05) corrections applied post-hoc to the 18 paired tests. Under BH correction, 7 of 18 tests survive at FDR

\leq

0.05, including all headline reshape findings: overconfident_assertion amplification in Mistral/BioMistral (BH

p=0.024

) and Phi-3.5/OpenBioLLM (BH

p=0.0009

); dosage_expression suppression in Phi-3.5/OpenBioLLM (BH

p=0.002

) and Llama/Med42 (BH

p=0.034

); high_alert_medication suppression in Mistral/BioMistral (BH

p=0.0006

) and Phi-3.5/OpenBioLLM (BH

p=0.002

); urgency_triage suppression in Mistral/BioMistral (BH

p=0.019

Category	$\alpha$ (presence, nominal)	$\alpha$ (severity, ordinal)
dosage_expression	0.702	0.708
urgency_triage	0.644	0.659
contraindication	0.620	0.624
treatment_directive	0.526	0.529
overconfident_assertion	0.269	0.305
high_alert_medication	0.186	0.193

Table 6: Krippendorff

\alpha

for inter-judge agreement, computed separately for presence (nominal) and severity (ordinal) measurement levels. 95% CIs from 500-resample bootstrap over items;

n=1{,}200

(model, response) pairs; three LLM judges.

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

Pair	Gen HC-RSHS	Med HC-RSHS	$\Delta$	Wilcoxon $p$
Mistral / BioMistral	0.347	0.277	$-0.070$	0.0076 ∗∗
Phi-3.5 / OpenBioLLM	0.255	0.214	$-0.041$	0.084
Llama-3.1 / Med42	0.313	0.283	$-0.029$	0.985

Appendix

Table 7: HC-RSHS composite (mean severity across the four well-agreed categories). Medical

<

general direction is preserved in all three pairs; the suppression is significant for Mistral/BioMistral (

p=0.0076

) and directionally consistent but not significant for Phi/OpenBioLLM and Llama/Med42. Note that Phi/OpenBioLLM shows individually significant dosage_expression suppression (

p=0.0003

, Table 5 ) that is diluted in the composite mean.

Pair	Judge	Gen sev	Med sev	$\Delta$	$p$
Mistral / BioMistral	Gemini	0.270	0.640	$+0.370$	$<0.001^{***}$
	GPT-4o-mini	1.065	1.005	$-0.060$	0.576
	Claude Hk	0.750	1.025	$+0.275$	$0.009^{**}$
Phi-3.5 / OpenBioLLM	Gemini	0.275	0.575	$+0.300$	$<0.001^{***}$
	GPT-4o-mini	0.730	1.130	$+0.400$	$<0.001^{***}$
	Claude Hk	0.800	0.865	$+0.065$	0.416

Appendix

Table 8: Per-judge paired Wilcoxon tests for overconfident_assertion severity. For each continued-pretraining pair, at least 2 of 3 judges show significant amplification (positive

\Delta

). The dissenting judge differs across pairs (GPT-4o-mini for Mistral/BioMistral; Claude Haiku for Phi/OpenBioLLM), suggesting judge-idiosyncratic sensitivity rather than a systematic artifact. For Llama/Med42, Claude Haiku uniquely shows strong overconfidence suppression (

\Delta=-0.389

p<0.001

), while the other two judges show no effect. This may reflect a self-preference artifact: Med42’s instruction-tuning style closely resembles Claude Haiku’s hedged response pattern.

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