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Explainambiguity Think Tank is part of the Drug Target Review Advisory Board
Explainambiguity Think Tank is dedicated to addressing the complexities and ambiguities inherent in the integration of Artificial Intelligence (AI) within the pharmaceutical and healthcare sectors.
Our mission is to provide clarity, foster innovation, and ensure regulatory compliance by tackling the root causes of ambiguity in AI applications. By doing so, we aim to drive business success through enhanced transparency, trust, and efficiency.
Join us in our mission to clarify the ambiguous, ensuring a safer, more efficient, and innovative healthcare landscape. Together, we can transform challenges into opportunities for growth and improvement, driving business success through AI clarity.
Latest insights
Latest insights
Token-Level Attribution for Transparent Biomedical AI
Biomedical Engineering and Computational Biology · January 17, 2026https://doi.org/10.1177/11795972251407864 Explainability (xAI) is critical for fostering trust, ensuring safety, and supporting regulatory compliance in healthcare AI systems. Large Language Models (LLMs), with impressive capabilities, operate as “black boxes” with prohibitive computational demands and regulatory challenges. Small Language Models (SLMs) with open-source architectures present a pragmatic alternative, offering efficiency, potential interpretability, and alignment with data privacy frameworks. This study evaluates whether token-level attribution (TLA) methods can provide technical traceability in SLMs for clinical decision support.
Con gli algoritmi, trasparenza e fiducia sono il miglior farmaco
ICT Business · Dec 23, 2025L’intelligenza artificiale è una grande promessa nel campo della salute e delle biotecnologie, e in particolare le attività di studio e sviluppo di nuovi farmaci. Ma agli algoritmi si legano anche giustificate preoccupazioni ed esigenze di trasparenza e spiegabilità (explainability) ancor più forti rispetto ad altri campi di applicazione dell’AI. Ce ne parlano RJ Geukes Foppen e Vincenzo Gioia, fondatori di Explainambiguity, un think tank dedicato all’integrazione responsabile dell’AI nell’industria, nelle Life Sciences e in sanità.
Disease natural history and care quality assessment of hormone receptor positive / human epidermal growth factor receptor 2 negative metastatic breast cancer (HR+/HER2- mBC) – an international multicentric studySan Antonio Breast Cancer Symposium · Nov 24 2025Despite early detection and treatment, ~30% of early-stage HR+ HER2− breast cancer (BC) patients relapse. Rebiopsy at recurrence is recommended by international guidelines when feasible, as it informs optimal treatment selection.
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