Explainable Versus Interpretable AI in Healthcare: How to Achieve Understanding
Year: 2025
Type: article
Abstract: The increasing adoption of deep learning methods has intensified the demand for explanations regarding how AI systems generate their results. This necessity originated primarily in the domain of image processing and has expanded to encompass the complexities of large language models (LLMs), particularly in medical contexts. For example, when LLM-based chatbots provide medical advice, the challenge lies in articulating the rationale behind their recommendations, especially when specific features may not be identifiable. This paper explores the distinction between explanation, interpretation, and understanding within AI-driven decision support systems. By adopting Daniel Dennett's intentional stance, we propose a methodology for analyzing how AI explanations can facilitate deeper user engagement and comprehension. Furthermore, we examine the implications of this methodology for the development and regulation of medical chatbots.
Source: BFH: ARBOR
DOI: 10.24451/arbor.12690
Authors: Moser, Denis Sumin, Sariyar, Murat
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7127945731
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