Explainable-AI in Automated Medical Report Generation Using Chest X-ray Images
Bibliographic record
Abstract
The use of machine learning in healthcare has the potential to revolutionize virtually every aspect of the industry. However, the lack of transparency in AI applications may lead to the problem of trustworthiness and reliability of the information provided by these applications. Medical practitioners rely on such systems for clinical decision making, but without adequate explanations, diagnosis made by these systems cannot be completely trusted. Explainability in Artificial Intelligence (XAI) aims to improve our understanding of why a given output has been produced by an AI system. Automated medical report generation is one area that would benefit greatly from XAI. This survey provides an extensive literature review on XAI techniques used in medical image analysis and automated medical report generation. We present a systematic classification of XAI techniques used in this field, highlighting the most important features of each one that could be used by future research to select the most appropriate XAI technique to create understandable and reliable explanations for decisions made by AI systems. In addition to providing an overview of the state of the art in this area, we identify some of the most important issues that need to be addressed and on which research should be focused.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".