DeepER-Med: Advancing Deep Evidence-Based Research in Medicine Through Agentic AI

2026-04-16

AI Summary

Current medical AI systems lack verifiable, evidence-based evaluation criteria and benchmark datasets reflecting real-world clinical complexity, undermining the trustworthiness and transparency of their outputs. This work proposes DeepER, the first agent-based framework for deep evidence-based reasoning in medicine, which establishes an explicit, auditable evidence-generation pipeline through three core modules: research planning, multi-agent collaboration, and evidence synthesis. The study introduces a verifiable evidence-based workflow into medical AI for the first time, constructs DeepER-MedQA—a curated real-world benchmark—and integrates multi-hop retrieval, evidence-based reasoning, and expert evaluation mechanisms. In human evaluations, DeepER consistently outperforms leading AI systems, generating novel scientific insights; notably, its conclusions align with current clinical guidelines in seven out of eight evaluated cases.

Technology Category

• Multiagent Systems: Other Foundations of Multi Agent Systems
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Trustworthiness and transparency are essential for the clinical adoption of artificial intelligence (AI) in healthcare and biomedical research. Recent deep research systems aim to accelerate evidence-grounded scientific discovery by integrating AI agents with multi-hop information retrieval, reasoning, and synthesis. However, most existing systems lack explicit and inspectable criteria for evidence appraisal, creating a risk of compounding errors and making it difficult for researchers and clinicians to assess the reliability of their outputs. In parallel, current benchmarking approaches rarely evaluate performance on complex, real-world medical questions. Here, we introduce DeepER-Med, a Deep Evidence-based Research framework for Medicine with an agentic AI system. DeepER-Med frames deep medical research as an explicit and inspectable workflow of evidence-based generation, consisting of three modules: research planning, agentic collaboration, and evidence synthesis. To support realistic evaluation, we also present DeepER-MedQA, an evidence-grounded dataset comprising 100 expert-level research questions derived from authentic medical research scenarios and curated by a multidisciplinary panel of 11 biomedical experts. Expert manual evaluation demonstrates that DeepER-Med consistently outperforms widely used production-grade platforms across multiple criteria, including the generation of novel scientific insights. We further demonstrate the practical utility of DeepER-Med through eight real-world clinical cases. Human clinician assessment indicates that DeepER-Med's conclusions align with clinical recommendations in seven cases, highlighting its potential for medical research and decision support.

Problem

• evidence appraisal
• trustworthiness
• transparency
• medical AI
• benchmarking

Innovation

• Agentic AI
• Evidence-Based Medicine
• Deep Research Framework
• Trustworthy AI
• Medical Benchmarking