KATY — Knowledge At the Tip of Your fingers: Clinical Knowledge for Humanity

The KATY project aims to develop an AI-empowered personalised medicine system to assist medical professionals and researchers in diagnosing patients more accurately, making predictions about their future health, and recommending better treatments. KATY will tackle the challenge of translating AI-based suggestions into practical decision-making processes and treatment strategies that clinicians can understand and trust by combining high-performing black-box machine learning approaches with a comprehensive knowledge graph. The knowledge graph serves as input to AI methods (e.g. directly, through embeddings) as well as encodes the AI outcomes themselves to create a shared semantic space for data, scientifi c context and predictions capable of supporting explanation methods.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-06
DOI
https://doi.org/10.5281/zenodo.15609793
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
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article

KATY — Knowledge At the Tip of Your fingers: Clinical Knowledge for Humanity

Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Healthcare
article

KATY — Knowledge At the Tip of Your fingers: Clinical Knowledge for Humanity

article en

Abstract

The KATY project aims to develop an AI-empowered personalised medicine system to assist medical professionals and researchers in diagnosing patients more accurately, making predictions about their future health, and recommending better treatments. KATY will tackle the challenge of translating AI-based suggestions into practical decision-making processes and treatment strategies that clinicians can understand and trust by combining high-performing black-box machine learning approaches with a comprehensive knowledge graph. The knowledge graph serves as input to AI methods (e.g. directly, through embeddings) as well as encodes the AI outcomes themselves to create a shared semantic space for data, scientifi c context and predictions capable of supporting explanation methods.

Zenodo (CERN European Organization for Nuclear Research)
University of Lisbon (PT)
Peace, Justice and strong institutions
Openalex Percentile: Top 75%
Machine Learning in Healthcare
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