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Published on: June 13, 2025
Integrating Anomaly Detection and LLM-Based Explanation Generation in Clinical Data Dashboards.
Hamidreza Maharlou1, Paula Fierek2, Hannah Benedictine Maier3
1Peter L. Reichertz Institute, Hannover Medical School (MHH), Germany.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
This study enhances clinical dashboards by combining anomaly detection with large language models (LLMs) for clear explanations. GPT-5 excels at generating patient-specific anomaly insights, improving data analysis trustworthiness.
Area of Science:
- Clinical Informatics
- Artificial Intelligence in Medicine
- Data Science
Background:
- Clinical study dashboards require interpretable anomaly detection.
- Automated anomaly explanations are needed for clinical stakeholders.
Purpose of the Study:
- To integrate unsupervised anomaly detection with LLM-based explanations for clinical dashboards.
- To evaluate LLMs for generating patient-specific anomaly explanations.
Main Methods:
- Employed Isolation Forest for anomaly detection in the P4D cohort study data.
- Utilized t-distributed Stochastic Neighbor Embedding (t-SNE) for outlier assessment.
- Evaluated GPT-5, GPT-4, Gemini 2.5 Flash, and Llama 3 for explanation generation.
Main Results:
- GPT-5 generated the most clinically useful and consistent natural language explanations.
- Llama 3 demonstrated limitations in contextual precision for anomaly explanations.
- The integrated approach improved the identification and understanding of data inconsistencies.
Conclusions:
- LLM-enhanced anomaly detection significantly improves clinical dashboard interpretability.
- GPT-5 shows strong potential for transparently explaining clinical data anomalies.
- This integration enhances the usability and trustworthiness of automated clinical data analysis.