MME PASCALE GUITERA

Rhumatologue

RPPS 10001520815

📊 Reconnaissance scientifique : 40/100📝 177 articles publiés🇫🇷 Publications académiques françaises (4)

✨ Génération du profil synthétique IA en cours…

Activité de recherche & publications

Indicateurs publics agrégés sur 250 M+ d'œuvres scientifiques (OpenAlex, PubMed). Traduits ici en langage patient.

Influence scientifique

40 articles ont été cités au moins 40fois par d'autres chercheurs — preuve que ses travaux sont repris par la communauté médicale.

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Top publications · les plus citées

Comparison of humans versus mobile phone-powered artificial intelligence for the diagnosis and management of pigmented skin cancer in secondary care: a multicentre, prospective, diagnostic, clinical trial

The Lancet. Digital health · 2023

📚 41 citations🎯 RCR 5.47Top 7% NIH🩺 Clinique🔓 Open Access📄 PDF gratuit ↗

A multimodal vision foundation model for clinical dermatology

Nature medicine · 2025

📚 36 citations🎯 RCR 14.59🔓 Open Access📄 PDF gratuit ↗

Lire l'abstract Crossref ↓

Abstract Diagnosing and treating skin diseases require advanced visual skills across domains and the ability to synthesize information from multiple imaging modalities. While current deep learning models excel at specific tasks such as skin cancer diagnosis from dermoscopic images, they struggle to meet the complex, multimodal requirements of clinical practice. Here we introduce PanDerm, a multimodal dermatology foundation model pretrained through self-supervised learning on over 2 million real-world skin disease images from 11 clinical institutions across 4 imaging modalities. We evaluated PanDerm on 28 diverse benchmarks, including skin cancer screening, risk stratification, differential diagnosis of common and rare skin conditions, lesion segmentation, longitudinal monitoring, and metastasis prediction and prognosis. PanDerm achieved state-of-the-art performance across all evaluated tasks, often outperforming existing models when using only 10% of labeled data. We conducted three reader studies to assess PanDerm’s potential clinical utility. PanDerm outperformed clinicians by 10.2% in early-stage melanoma detection through longitudinal analysis, improved clinicians’ skin cancer diagnostic accuracy by 11% on dermoscopy images and enhanced nondermatologist healthcare providers’ differential diagnosis by 16.5% across 128 skin conditions on clinical photographs. These results show PanDerm’s potential to improve patient care across diverse clinical scenarios and serve as a model for developing multimodal foundation models in other medical specialties, potentially accelerating the integration of artificial intelligence support in healthcare.

Position statement of the EADV Artificial Intelligence (AI) Task Force on AI-assisted smartphone apps and web-based services for skin disease

Journal of the European Academy of Dermatology and Venereology : JEADV · 2024

📚 28 citations🎯 RCR 8.88Top 3% NIH🔓 Open Access📄 PDF gratuit ↗

Lire l'abstract Crossref ↓

AbstractBackgroundAs the use of smartphones continues to surge globally, mobile applications (apps) have become a powerful tool for healthcare engagement. Prominent among these are dermatology apps powered by Artificial Intelligence (AI), which provide immediate diagnostic guidance and educational resources for skin diseases, including skin cancer.ObjectiveThis article, authored by the EADV AI Task Force, seeks to offer insights and recommendations for the present and future deployment of AI‐assisted smartphone applications (apps) and web‐based services for skin diseases with emphasis on skin cancer detection.MethodsAn initial position statement was drafted on a comprehensive literature review, which was subsequently refined through two rounds of digital discussions and meticulous feedback by the EADV AI Task Force, ensuring its accuracy, clarity and relevance.ResultsEight key considerations were identified, including risks associated with inaccuracy and improper user education, a decline in professional skills, the influence of non‐medical commercial interests, data security, direct and indirect costs, regulatory approval and the necessity of multidisciplinary implementation. Following these considerations, three main recommendations were formulated: (1) to ensure user trust, app developers should prioritize transparency in data quality, accuracy, intended use, privacy and costs; (2) Apps and web‐based services should ensure a uniform user experience for diverse groups of patients; (3) European authorities should adopt a rigorous and consistent regulatory framework for dermatology apps to ensure their safety and accuracy for users.ConclusionsThe utilisation of AI‐assisted smartphone apps and web‐based services in diagnosing and treating skin diseases has the potential to greatly benefit patients in their dermatology journeys. By prioritising innovation, fostering collaboration and implementing effective regulations, we can ensure the successful integration of these apps into clinical practice.

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Top publications · les plus citées

Publications scientifiques (27) — classées par pathologie

Transversal7

IA en rhumatologie5

Essai clinique4

Recommandations4

Épidémiologie & registres2

Revue générale2

Vraie vie / RWE2

Génétique1

Revue / méta-analyse1

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