An open-source clinical case dataset for medical image classification and multimodal AI applications

Autores
Nievas Offidani, Mauro; Roffet, Facundo Alejandro; González Galtier, María Carolina; Massiris Fernandez, Miguel Angel; Delrieux, Claudio Augusto
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
High-quality, openly accessible clinical datasets remain a significant bottleneck in advancing both research and clinical applications within medical artificial intelligence. Case reports, often rich in multimodal clinical data, represent an underutilized resource for developing medical AI applications. We present an enhanced version of MultiCaRe, a dataset derived from open-access case reports on PubMed Central. This new version addresses the limitations identified in the previous release and incorporates newly added clinical cases and images (totaling 93,816 and 130,791, respectively), along with a refined hierarchical taxonomy featuring over 140 categories. Image labels have been meticulously curated using a combination of manual and machine learning-based label generation and validation, ensuring a higher quality for image classification tasks and the fine-tuning of multimodal models. To facilitate its use, we also provide a Python package for dataset manipulation, pretrained models for medical image classification, and two dedicated websites. The updated MultiCaRe dataset expands the resources available for multimodal AI research in medicine. Its scale, quality, and accessibility make it a valuable tool for developing medical AI systems, as well as for educational purposes in clinical and computational fields.
Fil: Nievas Offidani, Mauro. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina
Fil: Roffet, Facundo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Laboratorio de Ciencias de Las Imágenes ; Departamento de Ingenieria Electrica y de Computadoras ; Universidad Nacional del Sur;
Fil: González Galtier, María Carolina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina
Fil: Massiris Fernandez, Miguel Angel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Laboratorio de Ciencias de Las Imágenes ; Departamento de Ingenieria Electrica y de Computadoras ; Universidad Nacional del Sur;
Fil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Laboratorio de Ciencias de Las Imágenes ; Departamento de Ingenieria Electrica y de Computadoras ; Universidad Nacional del Sur;
Materia
artificial intelligence
data curation
dataset
healthcare
image classification
medical imaging
medicine
multimodality
image captioning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/287372
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/287372
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling An open-source clinical case dataset for medical image classification and multimodal AI applicationsNievas Offidani, MauroRoffet, Facundo AlejandroGonzález Galtier, María CarolinaMassiris Fernandez, Miguel AngelDelrieux, Claudio Augustoartificial intelligencedata curationdatasethealthcareimage classificationmedical imagingmedicinemultimodalityimage captioninghttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2High-quality, openly accessible clinical datasets remain a significant bottleneck in advancing both research and clinical applications within medical artificial intelligence. Case reports, often rich in multimodal clinical data, represent an underutilized resource for developing medical AI applications. We present an enhanced version of MultiCaRe, a dataset derived from open-access case reports on PubMed Central. This new version addresses the limitations identified in the previous release and incorporates newly added clinical cases and images (totaling 93,816 and 130,791, respectively), along with a refined hierarchical taxonomy featuring over 140 categories. Image labels have been meticulously curated using a combination of manual and machine learning-based label generation and validation, ensuring a higher quality for image classification tasks and the fine-tuning of multimodal models. To facilitate its use, we also provide a Python package for dataset manipulation, pretrained models for medical image classification, and two dedicated websites. The updated MultiCaRe dataset expands the resources available for multimodal AI research in medicine. Its scale, quality, and accessibility make it a valuable tool for developing medical AI systems, as well as for educational purposes in clinical and computational fields.Fil: Nievas Offidani, Mauro. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Roffet, Facundo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Laboratorio de Ciencias de Las Imágenes ; Departamento de Ingenieria Electrica y de Computadoras ; Universidad Nacional del Sur;Fil: González Galtier, María Carolina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Massiris Fernandez, Miguel Angel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Laboratorio de Ciencias de Las Imágenes ; Departamento de Ingenieria Electrica y de Computadoras ; Universidad Nacional del Sur;Fil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Laboratorio de Ciencias de Las Imágenes ; Departamento de Ingenieria Electrica y de Computadoras ; Universidad Nacional del Sur;Multidisciplinary Digital Publishing Institute2025-07-31info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/287372Nievas Offidani, Mauro; Roffet, Facundo Alejandro; González Galtier, María Carolina; Massiris Fernandez, Miguel Angel; Delrieux, Claudio Augusto; An open-source clinical case dataset for medical image classification and multimodal AI applications; Multidisciplinary Digital Publishing Institute; Data; 10; 8; 31-7-2025; 1-212306-5729CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2306-5729/10/8/123info:eu-repo/semantics/altIdentifier/doi/10.3390/data10080123info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-08-25T14:30:34Zoai:ri.conicet.gov.ar:11336/287372instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-08-25 14:30:34.608CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An open-source clinical case dataset for medical image classification and multimodal AI applications
title An open-source clinical case dataset for medical image classification and multimodal AI applications
spellingShingle An open-source clinical case dataset for medical image classification and multimodal AI applications
Nievas Offidani, Mauro
artificial intelligence
data curation
dataset
healthcare
image classification
medical imaging
medicine
multimodality
image captioning
title_short An open-source clinical case dataset for medical image classification and multimodal AI applications
title_full An open-source clinical case dataset for medical image classification and multimodal AI applications
title_fullStr An open-source clinical case dataset for medical image classification and multimodal AI applications
title_full_unstemmed An open-source clinical case dataset for medical image classification and multimodal AI applications
title_sort An open-source clinical case dataset for medical image classification and multimodal AI applications
dc.creator.none.fl_str_mv Nievas Offidani, Mauro
Roffet, Facundo Alejandro
González Galtier, María Carolina
Massiris Fernandez, Miguel Angel
Delrieux, Claudio Augusto
author Nievas Offidani, Mauro
author_facet Nievas Offidani, Mauro
Roffet, Facundo Alejandro
González Galtier, María Carolina
Massiris Fernandez, Miguel Angel
Delrieux, Claudio Augusto
author_role author
author2 Roffet, Facundo Alejandro
González Galtier, María Carolina
Massiris Fernandez, Miguel Angel
Delrieux, Claudio Augusto
author2_role author
author
author
author
dc.subject.none.fl_str_mv artificial intelligence
data curation
dataset
healthcare
image classification
medical imaging
medicine
multimodality
image captioning
topic artificial intelligence
data curation
dataset
healthcare
image classification
medical imaging
medicine
multimodality
image captioning
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv High-quality, openly accessible clinical datasets remain a significant bottleneck in advancing both research and clinical applications within medical artificial intelligence. Case reports, often rich in multimodal clinical data, represent an underutilized resource for developing medical AI applications. We present an enhanced version of MultiCaRe, a dataset derived from open-access case reports on PubMed Central. This new version addresses the limitations identified in the previous release and incorporates newly added clinical cases and images (totaling 93,816 and 130,791, respectively), along with a refined hierarchical taxonomy featuring over 140 categories. Image labels have been meticulously curated using a combination of manual and machine learning-based label generation and validation, ensuring a higher quality for image classification tasks and the fine-tuning of multimodal models. To facilitate its use, we also provide a Python package for dataset manipulation, pretrained models for medical image classification, and two dedicated websites. The updated MultiCaRe dataset expands the resources available for multimodal AI research in medicine. Its scale, quality, and accessibility make it a valuable tool for developing medical AI systems, as well as for educational purposes in clinical and computational fields.
Fil: Nievas Offidani, Mauro. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina
Fil: Roffet, Facundo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Laboratorio de Ciencias de Las Imágenes ; Departamento de Ingenieria Electrica y de Computadoras ; Universidad Nacional del Sur;
Fil: González Galtier, María Carolina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina
Fil: Massiris Fernandez, Miguel Angel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Laboratorio de Ciencias de Las Imágenes ; Departamento de Ingenieria Electrica y de Computadoras ; Universidad Nacional del Sur;
Fil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Laboratorio de Ciencias de Las Imágenes ; Departamento de Ingenieria Electrica y de Computadoras ; Universidad Nacional del Sur;
description High-quality, openly accessible clinical datasets remain a significant bottleneck in advancing both research and clinical applications within medical artificial intelligence. Case reports, often rich in multimodal clinical data, represent an underutilized resource for developing medical AI applications. We present an enhanced version of MultiCaRe, a dataset derived from open-access case reports on PubMed Central. This new version addresses the limitations identified in the previous release and incorporates newly added clinical cases and images (totaling 93,816 and 130,791, respectively), along with a refined hierarchical taxonomy featuring over 140 categories. Image labels have been meticulously curated using a combination of manual and machine learning-based label generation and validation, ensuring a higher quality for image classification tasks and the fine-tuning of multimodal models. To facilitate its use, we also provide a Python package for dataset manipulation, pretrained models for medical image classification, and two dedicated websites. The updated MultiCaRe dataset expands the resources available for multimodal AI research in medicine. Its scale, quality, and accessibility make it a valuable tool for developing medical AI systems, as well as for educational purposes in clinical and computational fields.
publishDate 2025
dc.date.none.fl_str_mv 2025-07-31
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/287372
Nievas Offidani, Mauro; Roffet, Facundo Alejandro; González Galtier, María Carolina; Massiris Fernandez, Miguel Angel; Delrieux, Claudio Augusto; An open-source clinical case dataset for medical image classification and multimodal AI applications; Multidisciplinary Digital Publishing Institute; Data; 10; 8; 31-7-2025; 1-21
2306-5729
CONICET Digital
CONICET
url http://hdl.handle.net/11336/287372
identifier_str_mv Nievas Offidani, Mauro; Roffet, Facundo Alejandro; González Galtier, María Carolina; Massiris Fernandez, Miguel Angel; Delrieux, Claudio Augusto; An open-source clinical case dataset for medical image classification and multimodal AI applications; Multidisciplinary Digital Publishing Institute; Data; 10; 8; 31-7-2025; 1-21
2306-5729
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2306-5729/10/8/123
info:eu-repo/semantics/altIdentifier/doi/10.3390/data10080123
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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