Too much evidence, too little time: From text to actionable recommendations through multi-objective evidence reasoning
Quick Answer
This paper shows that The SCEPTER framework transforms complex clinical case descriptions into actionable recommendations by integrating PubMed retrieval, semantic ranking, and multi-objective reasoning, achieving a compression ratio of 192:1 while maintaining high evidence diversity.
Quick Take
Evaluated on 150 case studies, it reduced the search space from 576 to 53 papers, resulting in 7 Pareto-optimal claims and 3 final recommendations.
Key Points
- SCEPTER combines multiple AI techniques for clinical decision support.
- Framework reduced search space from 576 to 53 papers on average.
- Generated 7 Pareto-optimal claims and 3 actionable recommendations.
- Maintained high evidence diversity with an entropy of 0.901.
- Ablation study showed improved utility over conventional ranking methods.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Evidence-based clinical decision making requires specialists to identify, evaluate and synthesize relevant scientific literature. However, PubMed searches for complex clinical cases often return hundreds of publications that cannot be reviewed manually under time constraints. This study proposes SCEPTER (Single-Case Evidence-driven PubMed-To-rEcommendation Reasoner), a framework for transforming clinical case descriptions into evidence-based recommendations. SCEPTER combines PubMed retrieval, PubMedBERT semantic ranking, large language model (LLM)-based claim extraction, evidence-level weighting, contradiction detection, consensus analysis and multi-objective Pareto claim selection. The framework generates structured evidence syntheses and grounded actionable recommendations. A Paper Q&A module further enables interactive exploration of selected publications. The proposed framework introduces multi-objective reasoning model that integrates literature support, contradiction analysis and interactive literature interrogation into a unified clinical decision-support pipeline. Evaluation on 150 case studies demonstrated that the framework reduced an average search space of 576 papers to 53 retained papers, 7 Pareto-optimal claims and 3 final recommendations, corresponding to an overall compression ratio of 192:1. Despite this reduction, the retained evidence maintained high diversity (entropy=0.901). The ablation study showed that Pareto-based selection increased evidence diversity and recommendation utility compared with conventional ranking approaches.
| Subjects: | Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2607.22574 [cs.AI] |
| (or arXiv:2607.22574v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.22574 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Simona-Vasilica Oprea [view email]
[v1]
Fri, 5 Jun 2026 03:53:28 UTC (1,087 KB)
— Originally published at arxiv.org
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