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Authors' reply: Re: Koga et al. Retrieval‐augmented generation versus document‐grounded generation: a key distinction in large language models

The Journal of Pathology Clinical Research(2025)10.1002/2056-4538.70013Source: DataRank Database

Authors' reply: Re: Koga et al. Retrieval‐augmented generation versus document‐grounded generation: a key distinction in large language models is a research paper published in The Journal of Pathology Clinical Research (2025). On theSindex it has a DataRank of 0. It has been cited 1 time.

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (22 grants)OA: goldTopics (10)IDs (PubMed, PMC, OpenAlex)SDGs (1)

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Authors (3)

Isabella C. Wiest ORCID,Jakob Nikolas Kather ORCID,Katherine Hewitt ORCID

Live enrichment

PubMed:39835649 PMC:PMC11747990 OpenAlex:https://openalex.org/W4406676073

HORIZON EUROPE European Innovation Council

Grant: GENIAL 101096312

HORIZON EUROPE European Innovation Council

Grant: ODELIA 101057091

Deutsche Krebshilfe

Grant: DECADE 70115166

Clinical Center

Grant: EPICO R01 CA263318

Leeds Biomedical Research Centre

Grant: NIHR NIHR203331

Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz

Grant: CAMINO 01EO2101

Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz

Grant: Come2Data 16DKZ2044A

Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz

Grant: DEEP‐HCC 031L0315A

Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz

Grant: PEARL 01KD2104C

Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz

Grant: SWAG 01KD2215A

Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz

Grant: TANGERINE 01KT2302 through ERA‐NET Transcan

Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz

Grant: TRANSFORM LIVER 031L0312A

National Institute for Health Research (NIHR)

Grant: NIHR203331

German Federal Joint Committee/Gemeinsamer Bundesausschuss

Grant: TransplantKI 01VSF21048

European Research Council

Grant: NADIR 101114631

Deutscher Akademischer Austauschdienst

Grant: SECAI 57616814

European Research Council

Grant: 101114631

New directions for deep learning in cancer research through concept explainability and virtual experimentation.

Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz

Grant: DEEP-HCC 031L0315A

Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz

Grant: TANGERINE 01KT2302 through ERA-NET Transcan

European Commission

Grant: 101057091

Open Consortium for Decentralized Medical Artificial Intelligence

National Institutes of Health

Grant: 5R01CA263318-02

Integration of epidemiology, pathology, immunology and outcomes in colorectal cancer

European Commission

Grant: 101096312

Understanding Gene ENvironment Interaction in ALcohol-related hepatocellular carcinoma

goldLicense: cc-by

journalhttps://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/2056-4538.70013

publisherhttps://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/2056-4538.70013

publisherhttps://pathsocjournals.onlinelibrary.wiley.com/doi/pdf/10.1002/2056-4538.70013

journalhttps://doi.org/10.1002/2056-4538.70013

repositoryhttps://pubmed.ncbi.nlm.nih.gov/39835649

Fields of Study

Topic ModelingNatural Language Processing TechniquesMachine Learning in Healthcare

Keywords

Key (lock)Information retrievalNatural language processingComputer sciencePathologyRB1-214Letter to the Editor

Sustainable Development Goals

SDG 0: Quality Education