DeepER-Med: Advancing Deep Evidence-Based Research in Medicine Through Agentic AI
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2026
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| _version_ | 1866910138144653312 |
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| author | Wang, Zhizheng Wei, Chih-Hsuan Chan, Joey Leaman, Robert Day, Chi-Ping Wu, Chuan Knepper, Mark A Farias, Antolin Serrano Rincon-Torroella, Jordina Slika, Hasan Tyler, Betty Nguyen, Ryan Huu-Tuan Indurkar, Asmita Hébert, Mélanie Tian, Shubo He, Lauren Naffakh, Noor Aseem, Aseem Wan, Nicholas Chew, Emily Y Keenan, Tiarnan D L Lu, Zhiyong |
| author_facet | Wang, Zhizheng Wei, Chih-Hsuan Chan, Joey Leaman, Robert Day, Chi-Ping Wu, Chuan Knepper, Mark A Farias, Antolin Serrano Rincon-Torroella, Jordina Slika, Hasan Tyler, Betty Nguyen, Ryan Huu-Tuan Indurkar, Asmita Hébert, Mélanie Tian, Shubo He, Lauren Naffakh, Noor Aseem, Aseem Wan, Nicholas Chew, Emily Y Keenan, Tiarnan D L Lu, Zhiyong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_15456 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DeepER-Med: Advancing Deep Evidence-Based Research in Medicine Through Agentic AI Wang, Zhizheng Wei, Chih-Hsuan Chan, Joey Leaman, Robert Day, Chi-Ping Wu, Chuan Knepper, Mark A Farias, Antolin Serrano Rincon-Torroella, Jordina Slika, Hasan Tyler, Betty Nguyen, Ryan Huu-Tuan Indurkar, Asmita Hébert, Mélanie Tian, Shubo He, Lauren Naffakh, Noor Aseem, Aseem Wan, Nicholas Chew, Emily Y Keenan, Tiarnan D L Lu, Zhiyong Artificial Intelligence 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. |
| title | DeepER-Med: Advancing Deep Evidence-Based Research in Medicine Through Agentic AI |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.15456 |