Abstract
The use of artificial intelligence is rapidly increasing in medicine to support clinical
decision making mostly through diagnostic and prediction models. Such models derive
from huge databases (big data) including a large variety of health-related individual patient data (input) and the corresponding diagnosis and/or outcome (labels). Various types of algorithms (e.g. neural networks) based on powerful computational
ability (machine), allow to detect the relationship between input and labels (learning). More complex algorithms, like recurrent neural network can learn from previous
as well as actual input (deep learning) and are used for more complex tasks like imaging analysis and personalized (bespoke) medicine. The prompt availability of big data makes that artificial intelligence
can provide rapid answers to questions that would require years of traditional clinical
research. It may therefore be a key tool to overcome several major gaps in the model
of advanced chronic liver disease, mostly transition from mild to clinically significant
portal hypertension, the impact of acute decompensation and the role of further decompensation
and treatment efficiency. However, several limitations of artificial intelligence
should be overcome before its application in clinical practice. Assessment of the
risk of bias, understandability of the black boxes developing the models and models’ validation are the most important areas deserving
clarification for artificial intelligence to be widely accepted from physicians and
patients.
Keywords
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Article info
Publication history
Published online: December 30, 2022
Accepted:
December 8,
2022
Received:
November 3,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2022 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.
