Should AI models be explainable to clinicians?

In the high-stakes realm of critical care, where daily decisions are crucial and clear communication is paramount, comprehending the rationale behind Artificial Intelligence (AI)-driven decisions appears essential. While AI has the potential to improve decision-making, its complexity can hinder comp...

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Published in:Critical care (London, England) England), 2024-09, Vol.28 (1), p.301, Article 301
Main Authors: Abgrall, Gwénolé, Holder, Andre L, Chelly Dagdia, Zaineb, Zeitouni, Karine, Monnet, Xavier
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial Intelligence - standards
Artificial Intelligence - trends
Clinical Decision-Making - methods
Computer Science
Critical Care - methods
Critical Care - standards
Debate
Decision-making
Humans
Life Sciences
Physicians - standards
Statistics
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creator Abgrall, Gwénolé
Holder, Andre L
Chelly Dagdia, Zaineb
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Monnet, Xavier
description In the high-stakes realm of critical care, where daily decisions are crucial and clear communication is paramount, comprehending the rationale behind Artificial Intelligence (AI)-driven decisions appears essential. While AI has the potential to improve decision-making, its complexity can hinder comprehension and adherence to its recommendations. "Explainable AI" (XAI) aims to bridge this gap, enhancing confidence among patients and doctors. It also helps to meet regulatory transparency requirements, offers actionable insights, and promotes fairness and safety. Yet, defining explainability and standardising assessments are ongoing challenges and balancing performance and explainability can be needed, even if XAI is a growing field.
doi_str_mv 10.1186/s13054-024-05005-y
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subjects Artificial intelligence
Artificial Intelligence - standards
Artificial Intelligence - trends
Clinical Decision-Making - methods
Computer Science
Critical Care - methods
Critical Care - standards
Debate
Decision-making
Humans
Life Sciences
Physicians - standards
Statistics
title Should AI models be explainable to clinicians?
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