AI Agents for Clinical Data Assessment: Enhancing Decision-Making with Human-AI Collaboration

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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