Academic Journal
A BEACON for Novel Disease Threats: Leveraging Artificial Intelligence for Informal Event-Based Outbreak Surveillance.
| Τίτλος: | A BEACON for Novel Disease Threats: Leveraging Artificial Intelligence for Informal Event-Based Outbreak Surveillance. |
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| Συγγραφείς: | Bhadelia N; Center on Emerging Infectious Diseases, Boston University, Boston, Massachusetts, USA.; Section of Infectious Diseases, Boston University Avedisian and Chobanian School of Medicine, Boston, Massachusetts, USA., Paschalidis IC; Boston University College of Engineering, Boston, Massachusetts, USA.; Hariri Institute for Computing, Boston University, Boston, Massachusetts, USA., Brownstein JS; Innovation and Digital Health Accelerator, Boston Children's Hospital, Boston, Massachusetts, USA.; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA., Lassman B; Center on Emerging Infectious Diseases, Boston University, Boston, Massachusetts, USA.; Section of Infectious Diseases, Boston University Avedisian and Chobanian School of Medicine, Boston, Massachusetts, USA. |
| Πηγή: | The Journal of infectious diseases [J Infect Dis] 2026 Feb 18; Vol. 233 (2), pp. e287-e289. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Oxford University Press Country of Publication: United States NLM ID: 0413675 Publication Model: Print Cited Medium: Internet ISSN: 1537-6613 (Electronic) Linking ISSN: 00221899 NLM ISO Abbreviation: J Infect Dis Subsets: MEDLINE |
| Imprint Name(s): | Publication: Jan. 2011- : Oxford : Oxford University Press Original Publication: 1904-2010 : Chicago, IL : University of Chicago Press |
| Ιατρικοί όροι (MeSH): | Disease Outbreaks*/prevention & control , Artificial Intelligence* , Epidemiological Monitoring*, Humans |
| Περίληψη: | Event-based surveillance systems have proven critical for early detection of disease outbreaks by analyzing informal data sources, and the integration of artificial intelligence (AI) offers new opportunities to enhance these capabilities at scale. We describe the Biothreats Emergence, Analysis and Communications Network (BEACON), a novel platform that combines a domain-adapted large language model with expert human oversight to provide rapid, contextualized outbreak reporting across the globe, while highlighting both the transformative potential and current limitations of AI-assisted disease surveillance. (© The Author(s) 2025. Published by Oxford University Press on behalf of Infectious Diseases Society of America.) |
| Competing Interests: | Potential conflicts of interest. All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed. |
| References: | Transfusion. 1982 Sep-Oct;22(5):384-7. (PMID: 7123634) BMC Public Health. 2016 Dec 8;16(1):1238. (PMID: 27931204) N Engl J Med. 2023 Apr 27;388(17):1597-1607. (PMID: 37099342) BMC Proc. 2025 Sep 15;19(Suppl 22):26. (PMID: 40947485) Nat Comput Sci. 2025 Jun;5(6):467-480. (PMID: 40481184) |
| Grant Information: | BEACON; United States GATES Gates Foundation; Epidemic Preparedness Innovations; National Science Foundation; Tianqiao and Chrissy Chen Institute; Pax Sapiens; Boston University |
| Contributed Indexing: | Keywords: artificial intelligence; disease surveillance; large language models; outbreaks |
| Entry Date(s): | Date Created: 20251230 Date Completed: 20260218 Latest Revision: 20260813 |
| Update Code: | 20260813 |
| PubMed Central ID: | PMC13017941 |
| DOI: | 10.1093/infdis/jiaf642 |
| PMID: | 41467697 |
| Βάση Δεδομένων: | MEDLINE |
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