AI Agents for Clinical Data Assessment: Enhancing Decision-Making with Human-AI Collaboration
Bibliographic record
Abstract
This paper presents a novel framework for medical data assessment that integrates automated AI analysis, SHAP-based interpretability, and human feedback to generate comprehensive medical reports. Our approach employs a logistic regression model evaluated on a heart disease dataset, demonstrating robust performance across training, validation, and test splits. The framework uses SHAP values to provide transparent, quantitative insights into the influence of each clinical parameter on the prediction outcome. By incorporating human feedback as the definitive ground truth, the system refines its outputs, thereby bridging the gap between automated analysis and evolving clinical expertise. This integration addresses common challenges in clinical data including missing entries, coding discrepancies, and heterogeneity across healthcare providers to ensure that the generated reports are both consistent and reliable. The resulting automated report not only reduces the documentation burden on healthcare professionals but also standardizes reporting workflows, ultimately enhancing diagnostic decision-making. Future work will focus on extending the multi-agent framework to encompass additional clinical tasks and on integrating reinforcement learning techniques to enable continuous model improvement based on real-time feedback.
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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 | 0.018 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".