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Authors:
Lem Ngongalah
Robin Renwick
Keywords: Artificial intelligence; healthcare; explainable AI; trustworthiness; transparency
Abstract:
Artificial Intelligence (AI) has revolutionised healthcare, offering advanced diagnostics, personalised treatments, and enhanced patient outcomes. As AI increasingly integrates into healthcare systems, the need for Explainable AI (XAI) becomes paramount to ensure transparent and ethical decision-making. The lack of transparency and interpretability in AI systems poses significant challenges in healthcare, potentially undermining trust and hindering adoption. Understanding and addressing the complexities of XAI in healthcare is crucial for fostering trust among stakeholders, improving patient care, and adhering to ethical principles. Previous efforts have highlighted the importance of XAI but often lacked comprehensive approaches for implementation in diverse healthcare settings. This article explores the integration of XAI in healthcare, focusing on insights from three European Commission-funded projects under Horizon 2020/Horizon Europe. These projects prioritize transparency, accountability, and accessibility, showcasing the potential of XAI enhanced decision-making. In addition, the papers recognise the limitations of XAI, such as the absence of standardized approaches and the difficulty of balancing AI complexity with transparency, emphasizing the need for continuous refinement and adaptation to ensure successful XAI integration across varied healthcare settings.
Pages: 17 to 19
Copyright: Copyright (c) IARIA, 2024
Publication date: March 10, 2024
Published in: conference
ISBN: 978-1-68558-136-7
Location: Athens, Greece
Dates: from March 10, 2024 to March 14, 2024