grant

Accelerating Risk-of-Bias (RoB) Assessment in Environmental Health Studies Using Large Language Models

Organization PICO PORTAL, INC.Location SAINT PETERSBURG, UNITED STATESPosted 2 Jun 2025Deadline 31 May 2027
NIHUS FederalResearch GrantFY2025AI AugmentedAI algorithmAI assistedAI drivenAI enhancedAI integratedAI language modelsAI poweredAI systemAI technologyAccelerationAddressAgreementAir PollutionAlgorithmsArtificial IntelligenceArtificial Intelligence enhancedAugmented by AIAugmented by the AIAugmented with AIAugmented with the AICalibrationChatGPTChemical ExposureComputer ReasoningConsumptionCountryDataData SetData SystemsDecision MakingDevelopmentEffectivenessEnsureEnvironmental ExposureEnvironmental FactorEnvironmental HealthEnvironmental Health ScienceEnvironmental ProtectionEnvironmental Risk FactorEvaluationFoundationsFunding AgencyFunding SourceGPT-2GPT-3GPT-4GenerationsGoalsGrantHealthHealth CareHealth Care ProfessionalHealth PolicyHealth ProfessionalHumanIT SystemsInformation SystemsInformation Technology SystemsIntelligenceInterventionInvestmentsJudgmentLeftLiteratureLlamaMachine IntelligenceMeasuresMethodologyModelingModern ManNIEHSNamesNational Institute of Environmental Health SciencesObservation researchObservation studyObservational StudyObservational researchOntologyOutcomePFASPerformancePhasePoisonPoliciesPolicy MakerPoly-fluoroalkyl substancesPopulationPrivate SectorProcessPublic HealthPublishingResearchResearch ResourcesResourcesReview LiteratureRiskRisk AssessmentSBIRSmall Business Innovation ResearchSmall Business Innovation Research GrantSocial SciencesSpeedTechnologyTestingTextTimeToxic ChemicalToxic SubstanceTrainingTransformer language modelTranslationsWater Pollutionartificial intelligence algorithmartificial intelligence assistedartificial intelligence augmentedartificial intelligence drivenartificial intelligence integratedartificial intelligence language modelsartificial intelligence poweredartificial intelligence technologyclimate changeclimatic changescostdevelopmentalenhanced with AIenhanced with Artificial Intelligenceenvironmental riskevidence baseglobal climate changehealth assessmenthealth care policyimprovedinnovateinnovationinnovativeinternet portallarge language modellarge scale language modelmassive scale language modelsnamenamednamingon-line portalonline portalperfluorinated alkyl substancesperfluoroalkyl substancesperfluoroalkylated substancespolyfluorinated alkyl substancespolyfluoroalkyl substancesreal world applicationsystematic reviewtech developmenttechnology developmenttooltoxic compoundtranslationweb portalweb toolweb-based portalweb-based tool
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Full Description

Grant No: 1R43ES037589-01
PI Name : Agai, Eitan

Project Summary

PICO Portal, Inc. proposes the development and rigorous evaluation of advanced AI-driven tools to automate, increase efficiency, and reduce the costs of conducting Risk-of-Bias (RoB) assessments in environmental health systematic reviews (SRs). SRs are the "Gold Standard" for synthesizing evidence to inform decision-making in environmental health, healthcare, health policy, and social science. However, these reviews are resource-intensive, requiring significant investments of time, expertise, and financial resources, particularly for tasks such as RoB assessments and data extraction.

This project aims to utilize the capabilities of large language models (LLMs), such as Meta AI's LLaMA, OpenAI’s GPT, and Anthropic’s Claude, to address these challenges. Specifically, we will focus on developing, fine-tuning, and validating LLM-based workflows to accurately identify and extract critical text passages from studies related to per- and polyfluoroalkyl substances (PFAS).

We aim to refine the model through iterative testing and optimization until its performance closely mirrors that of human experts. Achieving a high level of agreement between AI-extracted passages and those identified by trained professionals is crucial to ensuring that the model is not only accurate but also reliable and effective in real-world applications.

The successful implementation of this project will increase efficiency and reduce costs for conducting SRs in environmental health. Furthermore, this innovation could extend beyond environmental health to other fields such as healthcare, health policy, and social science. It could also benefit any review process, including scoping the literature, identifying research gaps for funding agencies, and conducting competitive intelligence for the private sector.

Grant Number: 1R43ES037589-01
NIH Institute/Center: NIH

Principal Investigator: Eitan Agai

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Accelerating Risk-of-Bias (RoB) Assessment in Environmental Health Studies Using Large Language Models — PICO PORTAL, IN | Dev Procure