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arxiv: 2604.23854 · v1 · submitted 2026-04-26 · cs.AI

Does Machine Unlearning Preserve Clinical Safety? A Risk Analysis for Medical Image Classification

Andreza M. C. Falcao , Filipe R. Cordeiro This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-05-08 05:54 UTCgrok-4.3open to challenge →

classification cs.AI
keywords machine unlearningclinical riskmedical image classificationfalse negativesdata removalDermaMNISTPathMNISTdeep learning safety
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The pith

Standard unlearning methods can raise false-negative rates in medical image classification, increasing clinical risk, while a modified SalUn-CRA variant keeps risk comparable to full retraining.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether machine unlearning, which removes selected training examples from a deployed model, preserves safety when the task is binary medical image classification. Common techniques such as fine-tuning, random labeling, and SalUn lower overall accuracy and raise the rate of missed malignant cases, which carries asymmetric harm in diagnosis. The authors introduce SalUn-CRA, which substitutes entropy-based forgetting for malignant samples in the forget set instead of random relabeling. On DermaMNIST and PathMNIST at 20 % and 50 % removal rates, SalUn-CRA produces Global Risk scores with asymmetric costs that match or beat those of full retraining while still achieving the requested forgetting. The work concludes that clinical risk metrics must be part of unlearning evaluation in medical systems rather than relying on utility or privacy metrics alone.

Core claim

Standard unlearning strategies (Fine-Tuning, Random Labeling, and SalUn) may reduce test utility while increasing false-negative rates, thereby amplifying clinical risk. SalUn-CRA, which replaces random relabeling with entropy-based forgetting for malignant samples, achieves lower or comparable clinical risk to full retraining while preserving unlearning effectiveness on DermaMNIST and PathMNIST under 20 % and 50 % data removal.

What carries the argument

SalUn-CRA, a variant of SalUn that replaces random relabeling with entropy-based forgetting targeted at malignant samples in the forget set, combined with Global Risk metrics that apply asymmetric costs to false negatives versus false positives.

If this is right

  • Unlearning without clinical-risk awareness can increase the probability of missed diagnoses even when overall accuracy appears acceptable.
  • Replacing random relabeling with entropy-based forgetting for malignant samples prevents the model from forming harmful benign associations.
  • Global Risk with asymmetric costs provides a more appropriate validation signal than standard accuracy or privacy metrics for medical unlearning.
  • SalUn-CRA matches or undercuts the clinical risk of full retraining at both 20 % and 50 % removal rates on the tested dermatology and pathology datasets.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same risk-aware forgetting strategy could be tested on multi-class or segmentation tasks common in radiology.
  • Regulatory frameworks for medical AI data deletion may need to require explicit clinical-risk audits rather than only membership-inference or accuracy checks.
  • If entropy-based forgetting generalizes, it could reduce the computational cost of safe unlearning by avoiding full retraining in regulated environments.

Load-bearing premise

That Global Risk metrics with asymmetric costs computed on DermaMNIST and PathMNIST accurately represent real-world clinical safety trade-offs and that entropy-based forgetting introduces no new undetected risks.

What would settle it

A clinical deployment on new patient images where SalUn-CRA produces a higher false-negative rate for confirmed malignant cases than a model retrained from scratch on the remaining data.

Figures

Figures reproduced from arXiv: 2604.23854 by Andreza M. C. Falcao, Filipe R. Cordeiro.

Figure 1
Figure 1. Figure 1: Clinical risk comparison across unlearning methods with forget rates view at source ↗
Figure 2
Figure 2. Figure 2: Trade-off between unlearning effectiveness (GAP) and Global Risk. view at source ↗
read the original abstract

The application of Deep Learning in medical diagnosis must balance patient safety with compliance with data protection regulations. Machine Unlearning enables the selective removal of training data from deployed models. However, most methods are validated primarily through efficiency and privacy-oriented metrics, with limited attention to clinically asymmetric error costs. In this work, we investigate how unlearning affects clinical risk in binary medical image classification. We show that standard unlearning strategies (Fine-Tuning, Random Labeling, and SalUn) may reduce test utility while increasing false-negative rates, thereby amplifying clinical risk. To mitigate this, we propose SalUn-CRA (Clinical Risk-Aware), a variant of SalUn that replaces random relabeling with entropy-based forgetting for malignant samples in the forget set, preventing the model from learning harmful benign associations. We evaluate on DermaMNIST and PathMNIST medical image datasets under 20% and 50% data removal. Using Global Risk metrics with asymmetric costs, SalUn-CRA achieves lower or comparable clinical risk to full retraining while preserving unlearning effectiveness. These results suggest that clinical risk should be an integral component of unlearning validation in medical systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper claims that standard machine unlearning methods (Fine-Tuning, Random Labeling, SalUn) applied to binary medical image classifiers on DermaMNIST and PathMNIST can reduce test utility while increasing false-negative rates, thereby raising clinical risk when evaluated under asymmetric error costs. It proposes SalUn-CRA, a variant that substitutes entropy-based forgetting for random relabeling of malignant samples in the forget set, and reports that this variant achieves lower or comparable Global Risk scores to full retraining while retaining unlearning effectiveness at 20% and 50% data removal rates.

Significance. If the empirical comparisons hold after supplying the missing metric definitions and implementation details, the work is significant because it shifts unlearning evaluation in medical AI from purely privacy- and utility-focused metrics toward clinically asymmetric risk. The introduction of an entropy-based mitigation and the explicit use of Global Risk provide a concrete example of how domain-specific safety considerations can be integrated into unlearning pipelines.

major comments (3)
  1. Abstract and Methods: The Global Risk metric with asymmetric costs is the sole basis for the headline claim that standard unlearning amplifies clinical risk and that SalUn-CRA matches retraining. No formula, explicit cost ratio (e.g., FN:FP weighting), derivation from clinical guidelines, or sensitivity analysis is supplied, rendering the directional results on false-negative increases unverifiable and potentially sensitive to the arbitrary choice of ratio.
  2. Methods: The entropy-based forgetting mechanism for malignant forget-set samples in SalUn-CRA is described only at high level without pseudocode, exact entropy threshold or relabeling rule, or analysis of side effects on model calibration or new undetected failure modes. This mechanism is load-bearing for the proposed superiority over Random Labeling.
  3. Experiments: No details are given on statistical significance testing, hyperparameter selection, exact implementation of the entropy mechanism or baselines, or the precise composition of the forget sets at 20% and 50% removal. These omissions prevent assessment of whether the reported risk reductions are robust or reproducible.
minor comments (2)
  1. Abstract: The phrase 'preserving unlearning effectiveness' should be accompanied by the concrete privacy or membership-inference metrics used to support it.
  2. The manuscript would benefit from an explicit limitations paragraph discussing how well the chosen datasets and binary classification setting generalize to real multi-class clinical workflows.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed review. We address each major comment below and will revise the manuscript to incorporate the requested clarifications and details.

read point-by-point responses

  1. Referee: [—] Abstract and Methods: The Global Risk metric with asymmetric costs is the sole basis for the headline claim that standard unlearning amplifies clinical risk and that SalUn-CRA matches retraining. No formula, explicit cost ratio (e.g., FN:FP weighting), derivation from clinical guidelines, or sensitivity analysis is supplied, rendering the directional results on false-negative increases unverifiable and potentially sensitive to the arbitrary choice of ratio.

    Authors: We agree that the Global Risk metric requires fuller specification to support the claims. In the revised manuscript we will add the explicit formula, the chosen asymmetric cost ratio (with justification drawn from clinical literature on the higher cost of missed malignancies), its derivation, and a sensitivity analysis across plausible ratios. These additions will make the reported directional effects on false-negative rates verifiable and allow readers to assess robustness. revision: yes

  2. Referee: [—] Methods: The entropy-based forgetting mechanism for malignant forget-set samples in SalUn-CRA is described only at high level without pseudocode, exact entropy threshold or relabeling rule, or analysis of side effects on model calibration or new undetected failure modes. This mechanism is load-bearing for the proposed superiority over Random Labeling.

    Authors: We acknowledge that the entropy-based forgetting component is currently described at too high a level. The revision will include pseudocode for the full procedure, the precise entropy threshold and relabeling rule applied to malignant samples, and an analysis of side effects on calibration and potential new failure modes. This will strengthen the justification for SalUn-CRA relative to standard random labeling. revision: yes

  3. Referee: [—] Experiments: No details are given on statistical significance testing, hyperparameter selection, exact implementation of the entropy mechanism or baselines, or the precise composition of the forget sets at 20% and 50% removal. These omissions prevent assessment of whether the reported risk reductions are robust or reproducible.

    Authors: We will expand the Experiments section and supplementary material with the missing information: the statistical tests employed (including p-value thresholds), the hyperparameter selection protocol, exact implementation details for the entropy mechanism and all baselines, and the precise composition of the forget sets (including how malignant samples were sampled at each removal rate). These additions will support reproducibility and allow evaluation of result robustness. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical evaluation against retraining baseline

full rationale

The paper is an empirical study that evaluates standard unlearning methods and proposes SalUn-CRA on DermaMNIST and PathMNIST under fixed removal percentages, reporting test utility, false-negative rates, and Global Risk scores relative to full retraining. No equations, fitted parameters, or derivations appear that reduce any reported risk number or superiority claim to a quantity defined inside the paper by construction. The Global Risk metric is applied as an external evaluation tool rather than being derived from the results themselves, and no self-citation chains or ansatzes are invoked to justify core claims. The work is therefore self-contained against the stated baselines.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Abstract provides no explicit free parameters or invented entities; the work rests on standard supervised learning assumptions and the unstated premise that the chosen risk metric and datasets capture clinical reality.

axioms (2)
  • domain assumption Global Risk metrics with asymmetric costs correctly quantify clinical safety
    Used as the primary evaluation criterion for all methods
  • ad hoc to paper Entropy-based relabeling of malignant forget-set samples prevents harmful benign associations without side effects
    Core mechanism of the proposed SalUn-CRA variant

pith-pipeline@v0.9.0 · 5507 in / 1284 out tokens · 41391 ms · 2026-05-08T05:54:26.365297+00:00 · methodology

discussion (0)

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Reference graph

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