Correction: Quantify unmet medical need across the disease landscape – A large language model-based methodology

Authors

David C. Fajgenbaum (ORCID: https://orcid.org/0000-0002-7367-8184)
Nicholas Ronald Fragola (ORCID: https://orcid.org/0000-0002-3049-6145)
Lee Lancashire (ORCID: https://orcid.org/0000-0003-4835-4847)
Matthew M. Goddeeris (ORCID: https://orcid.org/0000-0001-6346-7220)
Charlie Hempstead
Elliott W. Sharp
Charlotte Blewitt (ORCID: https://orcid.org/0009-0002-3170-7570)

Publication Details

Journal: PLoS Medicine
Published: 2026-04-10
DOI: https://doi.org/10.1371/journal.pmed.1005048
Primary Topic: Machine Learning in Healthcare
Type: article
Field-Weighted Citation Impact: 0.00

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article

Correction: Quantify unmet medical need across the disease landscape – A large language model-based methodology

David C. Fajgenbaum, Nicholas Ronald Fragola, Lee Lancashire, Matthew M. Goddeeris et al.

Apr 10, 2026

PLoS Medicine

Machine Learning in Healthcare

article

Correction: Quantify unmet medical need across the disease landscape – A large language model-based methodology

David C. Fajgenbaum, Nicholas Ronald Fragola, Lee Lancashire, Matthew M. Goddeeris, Charlie Hempstead, Elliott W. Sharp, Charlotte Blewitt

April 10, 2026article en

Abstract

No abstract available for this paper.

PLoS MedicineVol. 23(4)

Quality Education

Openalex Percentile: Top 26%

Machine Learning in Healthcare

DOI Open Access PDF OpenAlex

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Correction: Quantify unmet medical need across the disease landscape – A large language model-based methodology — David C. Fajgenbaum, Nicholas Ronald Fragola, et al. · PLoS Medicine (2026) | TGRS Research Map | TGRS