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Our research focuses on the intersection of artificial intelligence, biotechnology, and the pharmaceutical industry.
Through peer-reviewed publications, we explore critical topics such as AI safety, explainable AI (xAI), regulatory compliance, and the impact of AI on drug discovery and clinical decision-making.
This section presents our scientific contributions, offering in-depth analyses, methodologies, and case studies that drive innovation and ensure the responsible development of AI in life sciences.
2025
2025
AI for Oncology
AI for Oncology
Semantic Explainability in Oncological Decision Support: Enhancing Clinical Trust Through Transparent AI Systems.
Vincenzo Gioia, Remco Jan Geukes Foppen, Alberto Traverso
Vincenzo Gioia, Remco Jan Geukes Foppen, Alberto Traverso
MILAN, ITALY, 8-9 MAY 2025
https://www.aiforoncology.itThe availability of diverse treatment options in precision oncology has dramatically increased the complexity of therapeutic decision-making.
https://www.aiforoncology.itThe availability of diverse treatment options in precision oncology has dramatically increased the complexity of therapeutic decision-making.
AI for Oncology
AI for Oncology
Regulatory Framework for Language Models in Oncology.
Remco Jan Geukes Foppen, Vincenzo Gioia, Alberto Traverso.
Remco Jan Geukes Foppen, Vincenzo Gioia, Alberto Traverso.
MILAN, ITALY, 8-9 MAY 2025
https://www.aiforoncology.itThe integration of Large and Small Language Models (LLMs/SLMs) as support for oncology decision-making faces evolving regulatory landscapes.
https://www.aiforoncology.itThe integration of Large and Small Language Models (LLMs/SLMs) as support for oncology decision-making faces evolving regulatory landscapes.
2024
2024
Journal of Diabetes, Science and Technology · 26 December 2024
PMCID: PMC11672366 PMID: 39726262AI in diabetes management offers advanced solutions but presents safety and compliance challenges. This article explores xAI’s role in ensuring transparency, security, and trust, analyzing technical and regulatory aspects. It proposes risk mitigation strategies and GRC guidelines to develop reliable, compliant AI solutions that enhance patient engagement and improve clinical outcomes.
PMCID: PMC11672366 PMID: 39726262AI in diabetes management offers advanced solutions but presents safety and compliance challenges. This article explores xAI’s role in ensuring transparency, security, and trust, analyzing technical and regulatory aspects. It proposes risk mitigation strategies and GRC guidelines to develop reliable, compliant AI solutions that enhance patient engagement and improve clinical outcomes.
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