MedPerturb: Robust Medical Decision Models

• MedPerturb is a comprehensive framework that integrates testbeds, datasets, and methodologies for analyzing medical decision-making under controlled perturbations.
• It employs quantitative metrics such as Average Treatment Rate, Mutual Information, and Fleiss’ κ to compare LLM and clinician responses to systematic input variations.
• The framework extends to diverse applications including radiotherapy optimization and mechanistic spectral perturbation analyses for disease progression insights.

MedPerturb is a suite of frameworks, datasets, and methodologies unified by the systematic study of medical decision-making and modeling under controlled perturbations. The term encompasses clinically-relevant testbeds for evaluating robustness in LLMs, mechanistic approaches for explaining pathological transitions in biological systems, perturbation-based optimization in radiotherapy planning, and rigorous evaluation paradigms for model fairness and reliability under input variation (Gourabathina et al., 20 Jun 2025, Mayfield et al., 1 May 2026, Bonacker et al., 2019, Yang et al., 1 May 2026). Across its various instantiations, MedPerturb centers on quantifying, interpreting, and leveraging model and system responses to targeted, non-content, or mechanistically-defined perturbations.

1. Dataset Construction, Axes, and Organization

MedPerturb, as a dataset for LLM robustness evaluation, comprises 800 clinical contexts drawn from OncQA (GPT-4-generated cancer vignettes), r/AskaDocs (real Reddit questions with expert replies), and standardized medical testing formats (USMLE, dermatology). Each context is subjected to systematic, non-content perturbations along three independent axes (Gourabathina et al., 20 Jun 2025):

• Gender modification: Gender-swap (all gender markers exchanged), Gender-removal (all gender markers deleted), generated via deterministic Llama-3-8B prompts.
• Style variation: “Uncertain” hedges (insertion of tentative language), “Colorful/colloquial” embellishment, both using Llama-3-8B with controlled prompts.
• Format change: Machine-generated multi-turn doctor-patient conversations (via interacting GPT-4 agents), and third-person LLM-generated summaries.

Each base vignette and its variants are presented as prompts for three forced-choice triage questions: MANAGE (self-care at home), VISIT (clinic/ED), and RESOURCE (labs/referrals), giving 2,400 vignette/question pairs. Outputs include 28,800 LLM responses from four models across three seeds, and 7,200 human annotations (36 U.S. medical students, with stratified assignment to variants). Cases with strongly gendered pathologies are filtered to avoid content confounds.

2. Evaluation Methodology and Statistical Frameworks

Evaluation comprises discrimination between model and human response profiles to controlled input perturbations, using a comprehensive suite of metrics:

• Average Treatment Rate (ATR): Mean fraction of “yes” responses per question/variant and cohort (humans: three clinicians; LLMs: 4 models × 3 seeds).
• Mutual Information (MI): Consistency of triage responses across perturbation pairs for a given annotator. Formally,

$$\mathrm{MI}_q = \sum_{a,b \in \{0,1\}} P(t^{\mathrm{base}}_q = a, t^{\mathrm{pert}}_q = b) \log \frac{P(t^{\mathrm{base}}_q = a, t^{\mathrm{pert}}_q = b)}{P(t^{\mathrm{base}}_q = a)P(t^{\mathrm{pert}}_q = b)}$$

• Percent Change (PC): Proportion of annotators who change their recommendation between baseline and perturbed prompts.
• Fleiss’ κ: Multi-rater kappa statistic for inter-annotator agreement per variant, applied to both LLM and clinician cohorts.
• Statistical Protocols: Paired t-tests for ATR differences, Mann–Whitney U for MI, and Bonferroni corrections for multiple comparisons are standard. Inter-cohort comparison leverages Wilcoxon signed-rank and McNemar’s tests (Gourabathina et al., 20 Jun 2025).

A critical methodological advance is the use of “token-matched” paraphrase baselines for counterfactual prompting: the number of tokens altered in the targeted edit (e.g., gender-swap) is matched by a meaning-preserving GPT-5.2-generated paraphrase, thus controlling for general model brittleness to surface-form change (Yang et al., 1 May 2026).

3. Key Empirical Results: Human–Model Divergence and Sensitivity

MedPerturb reveals several robust trends in LLM versus human clinical reasoning under input variability (Gourabathina et al., 20 Jun 2025):

• LLMs are systematically more sensitive than clinicians to superficial gender and style cues. Under gender-swap or colorful style, Mutual Information drops by 10–30 points in LLM responses (p<0.01), while clinician MI remains ≳0.90.
• LLMs are overcautious: Under baseline, clinicians recommend self-management ~37 percentage points more often than LLMs, while LLMs are more likely to select additional resources.
• Format alterations (multi-turn, summary) disproportionately affect clinicians: Clinicians show large shifts in MANAGE and RESOURCE responses under summaries or multi-turn formats (>30% and –20% for summaries, respectively), while LLMs are almost invariant.
• Inter-rater agreement (κ) drops among clinicians under summary and dialog formats but increases slightly for LLMs, highlighting internal model consistency but reduced alignment with human practice.
• Illusory demographic bias: Initial findings of LLM gender sensitivity are explained by general prompt sensitivity: flip rates for gender-swap (14.9%) are matched by token-matched paraphrases (14.1%), indicating effects are not attributable to gender per se (Yang et al., 1 May 2026). Only certain stylistic perturbations in smaller models reach statistical significance after controlling for the general response distribution.

These results suggest that apparent model biases may be largely artifacts of surface-form perturbability rather than true attribute-specific sensitivity.

4. Metric Classes, Power Analysis, and Best-Practice Recommendations

MedPerturb-driven evaluation leverages three families of metrics:

• Aggregate metrics: Flip rate, mutual information, and φ coefficient; population-level and unsigned.
• Per-sample distributional metrics: Jensen–Shannon divergence (JSD), Kullback–Leibler divergence (KL); capture instance-wise response shifts.
• Regression metrics: Signed estimate of effect direction and magnitude (e.g., log-odds shift $\beta_{\mathrm{pert}}$ per intervention arm).

Simulation studies and empirical analysis find that per-sample metrics (KL, JSD) are dramatically more powerful for moderate effects, especially under class imbalance or when flips in opposite directions cancel. Regression uniquely characterizes directionality and magnitude. Aggregate metrics can fail to register even substantial systematic shifts, particularly for imbalanced tasks.

To isolate targeted effects, MedPerturb prescribes always including a magnitude-matched paraphrase baseline, reporting per-sample metrics, and using signed regression when hypothesis directionality is tested (Yang et al., 1 May 2026). No claims of demographic or style bias should be made absent significance over the paraphrase “null,” and multiple-comparison adjustment is essential.

5. Implications, Limitations, and Future Directions

MedPerturb exposes critical vulnerabilities in standard LLM robustness testing:

• Prompt brittleness is widespread: Simple surface-form perturbations (even those unrelated to content or protected attributes) routinely elicit response shifts as large as those attributed to gender or style.
• LLM–clinician misalignment: LLMs exhibit high internal consistency but poor qualitative agreement with humans under real-world format variability—posing challenges for integration into clinical workflows.
• Risks in human-in-the-loop systems: Clinician susceptibility to LLM-generated reformatting (especially summaries) may inadvertently depress resource allocation, underscoring a subtle feedback risk if LLMs are used as pre-processors.

Key limitations of the current MedPerturb instantiation include U.S.-only, student-level annotation pools; LLM-generated perturbations (potential for artifact introduction); coverage limited to non-content axes (gender, style, format); English language only; and four model backends. Future research is expected to expand the perturbation axes (race, age, paraphrase), language coverage, and clinical specialties; incorporate more real-world EHR contexts; and systematically benchmark alignment methods (e.g., RLHF) and advanced fairness metrics (Gourabathina et al., 20 Jun 2025).

6. Extensions Beyond Clinical LLMs: Spectral Perturbation, Optimization, and Model Explanation

MedPerturb, as a conceptual and technical motif, also underpins:

• Disease as Hamiltonian perturbation: Disease progression framed as an additive spectral perturbation $\Delta H$ of a healthy biomarker covariance matrix $H_0$; perturbations are characterized via first-order shifts in eigenvalues and rotations in eigenvectors, giving interpretable biomarker-level mechanistic insight. Prognostic predictions are formed via spectral projections, yielding optimality guarantees under Gaussian assumptions (Mayfield et al., 1 May 2026).
• Optimization under bounded perturbation: In the context of intensity-modulated radiation therapy (IMRT), acceleration of projection methods via k-step heavy-ball and surrogate-constraint perturbations results in 2–4× reduction in computational effort and improved objective values, all with bounded-perturbation-resilient convergence guarantees (Bonacker et al., 2019).
• Perturbation-based explanation in medical AI: Exact computation of network-level Jacobians provides an objective, local measure of model sensitivity to input variation, forming the foundation for perturbation-based explanations independent of sampling or learning-path dependencies (Abe et al., 19 Feb 2025).
• Design principles for molecular perturbation models: Embedding perturbations in knowledge graphs and modeling out-of-distribution generalization via graph neural networks and latent shift modules (see TxPert), with implications for predicting complex transcriptomic responses in genomics and cell biology (Wenkel et al., 20 May 2025).

A plausible implication is that “MedPerturb” now denotes not a singular software toolkit but a convergent methodology across causal/fairness evaluation, mechanistic clinical omics, optimization, and deep model explainability under controlled perturbation regimes.