Academic Journal
A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis.
| Τίτλος: | A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis. |
|---|---|
| Συγγραφείς: | Spieser, Jackson, Balapour, Ali, Meller, Jarek, Patra, Krushna C., Shamsaei, Behrouz |
| Πηγή: | Methods & Protocols; Apr2026, Vol. 9 Issue 2, p33, 31p |
| Θεματικοί όροι: | Multiagent systems, Clinical decision making, Swarm intelligence, Data analysis, Biomedical signal processing, Patient selection, Language models |
| Περίληψη: | This review evaluates the emerging paradigm of multi-agent systems (MASs) for biomedical and clinical data analysis, focusing on their ability to overcome the reasoning and reliability limitations of standalone large language models (LLMs). We synthesize findings from recent architectural frameworks, specifically LangGraph, CrewAI, and the Model Context Protocol (MCP), to examine how specialized agent teams divide labor, utilize precision tools, and cross-verify outputs. We find that MAS architectures yield significant performance gains in various domains: recent implementations improved oncology decision-making accuracy from 30.3% to 87.2% and reached a peak of 93.2% accuracy on USMLE-style benchmarks through simulated clinical evolution. In clinical trial matching, multi-agent frameworks achieved 87.3% accuracy and enhanced clinician screening efficiency by 42.6% (p < 0.001). However, we also highlight critical operational challenges, including an unreliability tax of 15–50× higher token consumption compared to standalone models and the risk of cascading errors where initial hallucinations are amplified across the agent collective. We conclude that while MAS enables a shift toward collaborative intelligence in biomedicine, its clinical and research adoption requires the development of deterministic orchestration and rigorous cost-utility frameworks to ensure safety and expert-centered oversight. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Spieser%2C+Jackson%22">Spieser, Jackson</searchLink><br /><searchLink fieldCode="AR" term="%22Balapour%2C+Ali%22">Balapour, Ali</searchLink><br /><searchLink fieldCode="AR" term="%22Meller%2C+Jarek%22">Meller, Jarek</searchLink><br /><searchLink fieldCode="AR" term="%22Patra%2C+Krushna+C%2E%22">Patra, Krushna C.</searchLink><br /><searchLink fieldCode="AR" term="%22Shamsaei%2C+Behrouz%22">Shamsaei, Behrouz</searchLink> – Name: TitleSource Label: Source Group: Src Data: Methods & Protocols; Apr2026, Vol. 9 Issue 2, p33, 31p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Multiagent+systems%22">Multiagent systems</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+decision+making%22">Clinical decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Swarm+intelligence%22">Swarm intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Biomedical+signal+processing%22">Biomedical signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Patient+selection%22">Patient selection</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This review evaluates the emerging paradigm of multi-agent systems (MASs) for biomedical and clinical data analysis, focusing on their ability to overcome the reasoning and reliability limitations of standalone large language models (LLMs). We synthesize findings from recent architectural frameworks, specifically LangGraph, CrewAI, and the Model Context Protocol (MCP), to examine how specialized agent teams divide labor, utilize precision tools, and cross-verify outputs. We find that MAS architectures yield significant performance gains in various domains: recent implementations improved oncology decision-making accuracy from 30.3% to 87.2% and reached a peak of 93.2% accuracy on USMLE-style benchmarks through simulated clinical evolution. In clinical trial matching, multi-agent frameworks achieved 87.3% accuracy and enhanced clinician screening efficiency by 42.6% (p < 0.001). However, we also highlight critical operational challenges, including an unreliability tax of 15–50× higher token consumption compared to standalone models and the risk of cascading errors where initial hallucinations are amplified across the agent collective. We conclude that while MAS enables a shift toward collaborative intelligence in biomedicine, its clinical and research adoption requires the development of deterministic orchestration and rigorous cost-utility frameworks to ensure safety and expert-centered oversight. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Methods & Protocols is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/mps9020033 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 33 Subjects: – SubjectFull: Multiagent systems Type: general – SubjectFull: Clinical decision making Type: general – SubjectFull: Swarm intelligence Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Biomedical signal processing Type: general – SubjectFull: Patient selection Type: general – SubjectFull: Language models Type: general Titles: – TitleFull: A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Spieser, Jackson – PersonEntity: Name: NameFull: Balapour, Ali – PersonEntity: Name: NameFull: Meller, Jarek – PersonEntity: Name: NameFull: Patra, Krushna C. – PersonEntity: Name: NameFull: Shamsaei, Behrouz IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 24099279 Numbering: – Type: volume Value: 9 – Type: issue Value: 2 Titles: – TitleFull: Methods & Protocols Type: main |
| ResultId | 1 |