activity
20202026
most citedAloe: A Family of Fine-tuned Open Healthcare LLMs

7 citations · 9 across the 7 of their papers we have counts for

collaborators

10 papers

cs.CV2026

Aloe-Vision: Robust Vision-Language Models for Healthcare

Jaume Guasch-Martí, Enrique Lopez-Cuena, Martín Suárez-Fernández +3

Large Vision-Language Models (LVLMs) specialized in healthcare are emerging as a promising research direction due to their potential impact in clinical and biomedical applications.…

cs.CV2026

Language Models Can Explain Visual Features via Steering

Javier Ferrando, Enrique Lopez-Cuena, Pablo Agustin Martin-Torres +3

Sparse Autoencoders uncover thousands of features in vision models, yet explaining these features without requiring human intervention remains an open challenge. While previous wor…

cs.AI2026

Exploring SAIG Methods for an Objective Evaluation of XAI

Miquel Miró-Nicolau, Gabriel Moyà-Alcover, Anna Arias-Duart

The evaluation of eXplainable Artificial Intelligence (XAI) methods is a rapidly growing field, characterized by a wide variety of approaches. This diversity highlights the complex…

cs.AI2025

Bias by Design? How Data Practices Shape Fairness in AI Healthcare Systems

Anna Arias-Duart, Maria Eugenia Cardello, Atia Cortés

Artificial intelligence (AI) holds great promise for transforming healthcare. However, despite significant advances, the integration of AI solutions into real-world clinical practi…

cs.CL2025

The Aloe Family Recipe for Open and Specialized Healthcare LLMs

Dario Garcia-Gasulla, Jordi Bayarri-Planas, Ashwin Kumar Gururajan +10

Purpose: With advancements in Large Language Models (LLMs) for healthcare, the need arises for competitive open-source models to protect the public interest. This work contributes…

cs.CL2025

Efficient Safety Retrofitting Against Jailbreaking for LLMs

Dario Garcia-Gasulla, Adrian Tormos, Anna Arias-Duart +4

Direct Preference Optimization (DPO) is an efficient alignment technique that steers LLMs towards preferable outputs by training on preference data, bypassing the need for explicit…