55 citations · 112 across the 7 of their papers we have counts for
7 papers
RAR: Setting Knowledge Tripwires for Retrieval Augmented Rejection
Tommaso Mario Buonocore, Enea Parimbelli
Content moderation for large language models (LLMs) remains a significant challenge, requiring flexible and adaptable solutions that can quickly respond to emerging threats. This p…
Igea: a Decoder-Only Language Model for Biomedical Text Generation in Italian
Tommaso Mario Buonocore, Simone Rancati, Enea Parimbelli
The development of domain-specific language models has significantly advanced natural language processing applications in various specialized fields, particularly in biomedicine. H…
Reshaping Free-Text Radiology Notes Into Structured Reports With Generative Transformers
Laura Bergomi, Tommaso M. Buonocore, Paolo Antonazzo +5
BACKGROUND: Radiology reports are typically written in a free-text format, making clinical information difficult to extract and use. Recently the adoption of structured reporting (…
Evaluation of Predictive Reliability to Foster Trust in Artificial Intelligence. A case study in Multiple Sclerosis
Lorenzo Peracchio, Giovanna Nicora, Enea Parimbelli +5
Applying Artificial Intelligence (AI) and Machine Learning (ML) in critical contexts, such as medicine, requires the implementation of safety measures to reduce risks of harm in ca…
Advancing Italian Biomedical Information Extraction with Transformers-based Models: Methodological Insights and Multicenter Practical Application
Claudio Crema, Tommaso Mario Buonocore, Silvia Fostinelli +10
The introduction of computerized medical records in hospitals has reduced burdensome activities like manual writing and information fetching. However, the data contained in medical…
Localising In-Domain Adaptation of Transformer-Based Biomedical Language Models
Tommaso Mario Buonocore, Claudio Crema, Alberto Redolfi +2
In the era of digital healthcare, the huge volumes of textual information generated every day in hospitals constitute an essential but underused asset that could be exploited with…
Tree-based local explanations of machine learning model predictions, AraucanaXAI
Enea Parimbelli, Giovanna Nicora, Szymon Wilk +2
Increasingly complex learning methods such as boosting, bagging and deep learning have made ML models more accurate, but harder to understand and interpret. A tradeoff between perf…