Crossing the Trust Gap in Medical AI: Building an Abductive Bridge for xAI

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18460%2F24%3A50021645" target="_blank" >RIV/62690094:18460/24:50021645 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s13347-024-00790-4" target="_blank" >https://link.springer.com/article/10.1007/s13347-024-00790-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s13347-024-00790-4" target="_blank" >10.1007/s13347-024-00790-4</a>

Result language

angličtina

Original language name

Crossing the Trust Gap in Medical AI: Building an Abductive Bridge for xAI

Original language description

In this paper, we argue that one way to approach what is known in the literature as the “Trust Gap” in Medical AI is to focus on explanations from an Explainable AI (xAI) perspective. Against the current framework on xAI – which does not offer a real solution – we argue for a pragmatist turn, one that focuses on understanding how we provide explanations in Traditional Medicine (TM), composed by human agents only. Following this, explanations have two specific relevant components: they are usually (i) social and (ii) abductive. Explanations, in this sense, ought to provide understanding by answering contrastive why-questions: “Why had P happened instead of Q?” (Miller in AI 267:1–38, 2019) (Sect. 1). In order to test the relevancy of this concept of explanation in medical xAI, we offer several reasons to argue that abductions are crucial for medical reasoning and provide a crucial tool to deal with trust gaps between human agents (Sect. 2). If abductions are relevant in TM, we can test the capability of Artificial Intelligence systems on this merit. Therefore, we provide an analysis of the capacity for social and abductive reasoning of different AI technologies. Accordingly, we posit that Large Language Models (LLMs) and transformer architectures exhibit a noteworthy potential for effective engagement in abductive reasoning. By leveraging the potential abductive capabilities of LLMs and transformers, we anticipate a paradigm shift in the integration of explanations within AI systems. This, in turn, has the potential to enhance the trustworthiness of AI-driven medical decisions, bridging the Trust Gap that has been a prominent challenge in the field of Medical AI (Sect. 3). This development holds the potential to not only improve the interpretability of AI-generated medical insights but also to guarantee that trust among practitioners, patients, and stakeholders in the healthcare domain is still present.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

60301 - Philosophy, History and Philosophy of science and technology

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Philosophy and Technology

ISSN

2210-5433

e-ISSN

2210-5441

Volume of the periodical

37

Issue of the periodical within the volume

3

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

25

Pages from-to

"Article number: 105"

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85201531768