可解释的人工智能的当前方法和整合生理学的未来前景
1Cognitive Systems, University of Bamberg, Weberei 5, 96047, Bamberg, Germany. bettina.finzel@uni-bamberg.de.
Pflugers Archiv : European journal of physiology
|February 24, 2025
概括
可解释的人工智能 (XAI) 通过使人工智能模型易于理解来增强生理学研究. 本综述涵盖了XAI的方法,挑战和在医学中可靠的人类-AI合作的未来前景.
科学领域:
- 生理学研究生理学研究
- 医疗信息学医学信息学
- 人工智能的人工智能是人工智能.
背景情况:
- 人工智能 (AI) 在生理学研究中越来越多地用于分析和预测.
- 可解释的人工智能 (XAI) 旨在使人工智能模型对人类决策者来说透明和易于理解.
- XAI方法包括固有的可解释模型和对不透明模型的后期解释.
研究的目的:
- 介绍与生理学相关的核心主题和当前XAI方法.
- 审查医学领域的XAI研究中已解决和未解决的挑战.
- 提供XAI在整合性生理学研究中的未来应用前景.
主要方法:
- 在生理学当前XAI方法的选择性概述.
- 用医学领域的实用例子进行插图.
- 讨论XAI研究中的挑战和未来前景.
主要成果:
- 在生理学研究中,XAI对于理解AI至关重要.
- 目前的XAI方法提供了解释复杂的人工智能模型的方法.
- 实践示例突出了应用XAI的成功和挑战.
结论:
- 在整合性生理学研究中,XAI对于开发可靠的人工智能至关重要.
- 未来的方向包括将生理专业知识整合到XAI开发中,以实现更好的人类-AI合作伙伴关系.
- XAI承诺将提高AI在医疗和生理应用中的实用性和好处.
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