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Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Combi, C; Amico, B; Bellazzi, R; Holzinger, A; Moore, JH; Zitnik, M; Holmes, JH.
A manifesto on explainability for artificial intelligence in medicine.
Artif Intell Med. 2022; 133: 102423
Doi: 10.1016/j.artmed.2022.102423
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- Co-authors Med Uni Graz
- Holzinger Andreas
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- Abstract:
- The rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, output to users. This concern is especially legitimate in biomedical contexts, where patient safety is of paramount importance. This position paper brings together seven researchers working in the field with different roles and perspectives, to explore in depth the concept of explainable AI, or XAI, offering a functional definition and conceptual framework or model that can be used when considering XAI. This is followed by a series of desiderata for attaining explainability in AI, each of which touches upon a key domain in biomedicine.
- Find related publications in this database (using NLM MeSH Indexing)
- Humans - administration & dosage
- Artificial Intelligence - administration & dosage
- Medicine - administration & dosage
- Find related publications in this database (Keywords)
- Artificial intelligence
- Explainability
- Explainable artificial intelligence
- Interpretability
- Interpretable artificial intelligence