Breast Cancer Diagnosis With Explainable Artificial Intelligence (XAI): Uncovering Strengths and Biases
Bibliographic record
Abstract
Breast cancer is one of the most common malignancies afflicting women globally, necessitating the use of cutting-edge AI approaches in diagnostic procedures to improve patient outcomes drastically. However, one key difficulty with the most recent AI models is a lack of transparency, making it difficult for medical practitioners to implement these technologies to increase diagnostic accuracy. Many explainable AI (XAI) solutions have been developed to overcome this issue. This survey focuses on applying XAI approaches in breast cancer detection and diagnosis, particularly emphasizing their role in increasing model transparency and clinical decision-making. The article also provides insights into the inherent biases in the most recent machine learning models towards specific XAI approaches, such as the compatibility of Convolutional Neural Networks (CNNs) with visual explanation methods and tree-based models with feature significance evaluations. Finally, the article covers the obstacles to using XAI technologies in clinical practice and the significance of defining standard measures for assessing their performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".