Examining the Influence of Explainable Artificial Intelligence on Healthcare Diagnosis and Decision Making
Bibliographic record
Abstract
Artificial Intelligence (AI) has made significant strides in revolutionizing healthcare, offering unparalleled opportunities for improved diagnostics and decision-making. The use of AI in healthcare sector is becoming more popular in today’s era. In recent times, eXplainable AI (XAI) has shown significant improvement in medical diagnosis. Doctors are also validating patient test reports via XAI predictions. However, these intelligent systems pose challenges with regards to the underlying understanding and interpretations of AI models. In other words, it is essential to investigate the reasons when these models make certain predictions. The proposed work aims to improve the interpretability of various AI models by employing 2 techniques, Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). The study uses the Random Forest Classifier on the Breast Cancer Wisconsin (Diagnostic) dataset. The findings of this study are expected not only to advance AI technologies in healthcare but also build trust in doctors and other healthcare experts.
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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 | 0.013 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".