Toward trustworthy AI with integrative explainable AI frameworks DOI
Bettina Finzel

it - Information Technology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 30, 2025

Abstract As artificial intelligence (AI) increasingly permeates high-stakes domains such as healthcare, transportation, and law enforcement, ensuring its trustworthiness has become a critical challenge. This article proposes an integrative Explainable AI (XAI) framework to address the challenges of interpretability, explainability, interactivity, robustness. By combining XAI methods, incorporating human-AI interaction using suitable evaluation techniques, implementation this serves holistic approach. The discusses framework’s contribution trustworthy gives outlook on open related interdisciplinary collaboration, generalization evaluation.

Язык: Английский

Special issue European Journal of Physiology: Artificial intelligence in the field of physiology and medicine DOI Creative Commons
Anika Westphal, Ralf Mrowka

Pflügers Archiv - European Journal of Physiology, Год журнала: 2025, Номер unknown

Опубликована: Март 11, 2025

Abstract This special issue presents a collection of reviews on the recent advancements and applications artificial intelligence (AI) in medicine physiology. The topics covered include digital histopathology, generative AI, explainable AI (XAI), ethical considerations development implementation. highlight potential to transform medical diagnostics, personalized medicine, clinical decision making, while also addressing challenges such as data quality, interpretability, trustworthiness. contributions demonstrate growing importance physiological research need for multi-level ethics approaches development, benefits applications. Overall, this showcases some pioneering aspects physiology, covering technical, applicative, viewpoints, underlines remarkable impact these fields.

Язык: Английский

Процитировано

0

Toward trustworthy AI with integrative explainable AI frameworks DOI
Bettina Finzel

it - Information Technology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 30, 2025

Abstract As artificial intelligence (AI) increasingly permeates high-stakes domains such as healthcare, transportation, and law enforcement, ensuring its trustworthiness has become a critical challenge. This article proposes an integrative Explainable AI (XAI) framework to address the challenges of interpretability, explainability, interactivity, robustness. By combining XAI methods, incorporating human-AI interaction using suitable evaluation techniques, implementation this serves holistic approach. The discusses framework’s contribution trustworthy gives outlook on open related interdisciplinary collaboration, generalization evaluation.

Язык: Английский

Процитировано

0