Integrating explainability and bias detection in binary medical image classification: a systematic review
Bibliographic record
Abstract
This systematic review explores how recent medical imaging studies combine explainability and bias detection in binary classification models, with a focus on promoting fairness and transparency in clinical AI. Following PRISMA guidelines, we analysed 34 studies (peer-reviewed publications and eligible preprints) published between 2020 and 2025 across radiology, dermatology, and cross-domain applications, and we appraised risk of bias using the PROBAST tool. Radiology dominates the field, largely due to the availability of public datasets and established fairness metrics such as AUC disparities and True Positive Rate differences (bias detection/auditing). Most studies use post-hoc explainability tools like GradCAM to highlight influential image regions, SHAP to assign feature contribution scores, and LIME to explain model behavior through input perturbations. Several hybrid methods have shown promise, including adversarial debiasing (training models to reduce subgroup performance gaps), concept activation (linking decisions to human-understandable concepts), and prototype learning (using representative examples to guide classification). In dermatology, researchers focus on reducing skin tone bias through Fitzpatrick type stratification and tools like GEBI, a method for visualizing model sensitivity, and counterfactual explanations that reveal how small input changes could alter predictions. Cross-domain studies emphasize generalizability, employing multimodal inputs and causal modeling to handle dataset and context shifts. Overall, this review highlights the increasing sophistication of methods that integrate interpretability and fairness-an essential step toward ethical and robust AI deployment in healthcare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".