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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
AI assistance in tumor multidisciplinary teamsESMO Real World Data and Digital Oncology
Volume 11, 100684 March 26, 2026
February 12, 2026
https://doi.org/10.1016/j.esmorw.2026.100684 Oncology MDTs face inefficiencies caused by the manual synthesis of heterogeneous data and the lack of standardized processes. AI, through NLP and LLMs, can integrate multimodal data, support personalized decision‑making, and create feedback loops, improving clinical accuracy, governance, research, and resource utilization.
Volume 11, 100684 March 26, 2026
February 12, 2026
https://doi.org/10.1016/j.esmorw.2026.100684 Oncology MDTs face inefficiencies caused by the manual synthesis of heterogeneous data and the lack of standardized processes. AI, through NLP and LLMs, can integrate multimodal data, support personalized decision‑making, and create feedback loops, improving clinical accuracy, governance, research, and resource utilization.
Beyond serendipity: rational design and AI’s expansion of the undruggable target landscape
Drug Target Review · February 6, 2026For decades, drugging the ‘undruggable’ was thought to require luck rather than logic. Today, AI is transforming serendipity into strategy by enabling rational, data-driven approaches to previously inaccessible targets.
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.
Rome, Italy, March 28, 2026
https://healthcareinnovationforum.itHealthcare is undergoing a profound transformation. Innovation, technology, and new organizational models are redefining how we design and sustain the medicine of the future—more personalized, integrated, and preventive. In this context, the Healthcare Innovation Forum stands as a shared space for dialogue and co‑design among institutions, professionals, and industry.
https://healthcareinnovationforum.itHealthcare is undergoing a profound transformation. Innovation, technology, and new organizational models are redefining how we design and sustain the medicine of the future—more personalized, integrated, and preventive. In this context, the Healthcare Innovation Forum stands as a shared space for dialogue and co‑design among institutions, professionals, and industry.
BIOTECHGATE DIGITAL PARTNERING
Feb 23-27, 2026Biotechgate Digital PartneringBiotechgate Digital Partnering is designed to support the business development & licensing of Pharma, Biotech, Medtech and Digital Health companies. Join five days of virtual partnering and establish new global connections thanks to pre-arranged one-on-one Zoom meetings in our business forum.
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