Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

Principles of Disease Surveillance

Principles of Disease Surveillance

Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...

Leaky Scanning

Leaky Scanning

During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA. Marilyn Kozak discovered that the sequence RCCAUGG (where R...

Statistical Methods for Analyzing Epidemiological Data

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Real-World Associations of KidneyIntelX Risk Stratification With Guideline-Directed Therapy, Kidney Outcomes, and Metabolic Trajectories in Early Diabetic Kidney Disease.

Diabetes, obesity & metabolism·2026

Same author

Large language models integrated into brain-computer interfaces for communication and control: a systematic review.

Biomedical physics & engineering express·2026

Same author

MIAAIM: Multi-omics image integration with dimensional reduction for tissue state mapping.

PLoS computational biology·2026

Same author

Sociodemographic bias in large language model clinical trial screening.

Journal of the American Medical Informatics Association : JAMIA·2026

Same author

<i>Ad-verse Effects:</i> Pharmaceutical Advertising Shifts Drug Recommendations by Consumer-Facing AI.

medRxiv : the preprint server for health sciences·2026

Same author

Genomic analyses implicate hormonal and metabolic dysregulation in polycystic ovary syndrome.

Nature genetics·2026

Same journal

Somatosensory cortex shapes perceptual decision bias via the superior colliculus.

Research square·2026

Same journal

Combinatorial Targeting of Avapritinib-Driven MAP Kinase Activation in High-Grade Glioma.

Research square·2026

Same journal

Supporting Implementation of the National Standards for Cancer Survivorship Care: Development of the Cancer Survivorship Maturity Model (CSMM).

Research square·2026

Same journal

Operationalizing a walking exercise prescription based on 6-minute walk test results.

Research square·2026

Same journal

Age but not sex modifies lymphoid immune responses in murine sepsis.

Research square·2026

Same journal

Indirect effect, through aspects of neighborhood affluence and racial/ethnic composition, of receiving a Section 8 voucher on the prevalence of psychiatric disorders among boys and girls in the Moving to Opportunity study.

Research square·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 28, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Large Language Models in Infectious Diseases: A Systemic Review.

Alon Gorenshtein¹, Eyal Klang², Jacob J Smith³

¹Department of Neurology, Harvard Medical School, Boston, MA.

Research Square

|February 27, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) show promise in infectious disease diagnosis but exhibit significant safety concerns, including errors and fabricated content, necessitating expert oversight for clinical use.

Keywords:

Antimicrobial stewardship Bias and fairness Clinical decision support Hallucinations (AI)Infectious diseases Large language models Patient safety Retrieval-augmented generation

More Related Videos

A Robust Pneumonia Model in Immunocompetent Rodents to Evaluate Antibacterial Efficacy against S. pneumoniae, H. influenzae, K. pneumoniae, P. aeruginosa or A. baumannii

A Robust Pneumonia Model in Immunocompetent Rodents to Evaluate Antibacterial Efficacy against S. pneumoniae, H. influenzae, K. pneumoniae, P. aeruginosa or A. baumannii

Published on: January 2, 2017

Use of the Invertebrate Galleria mellonella as an Infection Model to Study the Mycobacterium tuberculosis Complex

Use of the Invertebrate Galleria mellonella as an Infection Model to Study the Mycobacterium tuberculosis Complex

Published on: June 30, 2019

Related Experiment Videos

Last Updated: Feb 28, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

A Robust Pneumonia Model in Immunocompetent Rodents to Evaluate Antibacterial Efficacy against S. pneumoniae, H. influenzae, K. pneumoniae, P. aeruginosa or A. baumannii

A Robust Pneumonia Model in Immunocompetent Rodents to Evaluate Antibacterial Efficacy against S. pneumoniae, H. influenzae, K. pneumoniae, P. aeruginosa or A. baumannii

Published on: January 2, 2017

Use of the Invertebrate Galleria mellonella as an Infection Model to Study the Mycobacterium tuberculosis Complex

Use of the Invertebrate Galleria mellonella as an Infection Model to Study the Mycobacterium tuberculosis Complex

Published on: June 30, 2019

Area of Science:

Medical Informatics
Artificial Intelligence in Medicine
Infectious Disease Management

Background:

Clinical reasoning in infectious diseases requires validated evidence.
Large language models (LLMs) are increasingly used in healthcare for diagnosis and antimicrobial stewardship.
The safety and reliability of LLMs in infectious disease decision-making remain unestablished.

Purpose of the Study:

To systematically review studies evaluating LLMs for infectious disease decision-making.
To assess the performance, safety, and reliability of various LLM systems in this domain.

Main Methods:

A systematic review of studies using GPT, Claude, Gemini, and retrieval-augmented systems.
Searches conducted in PubMed, CENTRAL, Scopus, and Web of Science (Jan 2018-Sep 2025).
Risk of bias assessed using QUADAS-AI.

Main Results:

Thirty-one studies were included; most were cross-sectional and vignette-based.
90% of studies reported safety issues, including incomplete responses and fabricated content.
LLMs achieved 80-100% diagnostic sensitivity for structured infections but had ~50% agreement in antimicrobial stewardship.

Conclusions:

LLMs demonstrate potential in specific diagnostic tasks but are currently unreliable for autonomous clinical application.
High error rates and inconsistent reasoning necessitate expert oversight and validation before deployment.
Retrieval-augmented systems show improved specificity and reduced hallucinations compared to standard LLMs.