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@PHDTHESIS{Kempt:959574,
author = {Kempt, Hendrik},
othercontributors = {Nagel, Saskia K. and Nyholm, Sven},
title = {{E}thical investigations of {AI} in medical diagnostics},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2023-05683},
pages = {1 Online-Ressource},
year = {2023},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2023},
abstract = {Since the advent of machine learning as an artificial
intelligence paradigm, opportunities for automating clinical
processes and decision-making have emerged that were
previously considered unattainable. This dissertation aims
to develop ethical analyses of the issues surrounding the
use of "medical AI" for various diagnostic purposes. To
motivate and contextualize the articles, a brief overview of
the developments of artificial intelligence in medical
diagnostics and the related ethical debate is first
presented. For this endeavor, three different levels are
presented against which diagnostic AI can be analyzed in
terms of its intent and function. The first level concerns
the role of AI as a decision support system. In this role,
AI is primarily understood according to the assistance of
physicians and patients in the decision-making process. The
requirements for AI in this role are thus to compensate for
human limitations. At the same time, such assistance can not
only support decisions, but also make new decisions possible
in the first place. The second level assumes that AI can
replace human decisions, i.e. those of both physicians and
patients. In this view, AI applications for diagnostic
purposes become successful when they exceed the benchmarks
of human decision-making. As a third level, the influence of
AI on medical practice is examined. Here, the role of AI as
an assistant or replacement is less important than the
consequences that the use of such AI may have for other
medical processes. This concerns, for example, norms of the
doctor-patient relationship. This level allows for an
analysis of the impact of AI on medical norms that goes
beyond the analysis of the technology. The first paper
discusses several of these conceptual and normative issues
and proposes a solution to a specific use case of AI for the
provision of second medical opinions. To this end, a
previously unavailable taxonomy of types of clinical second
opinions is first undertaken. With an overview of the
concepts of responsibility, explainability, and peer
disagreements, this paper points to a pragmatic yet
normative solution that allows AI-based decision support
systems to take on as many tasks in diagnostic processes as
possible: In the case of a second opinion of an AI
confirming the initial diagnosis, this can be considered
sufficient evidence for a diagnosis; should such a second
opinion deviate from the initial diagnosis of the treating
physician, this must be considered as an indicator that a
further, human assessment of that case is necessary. This
norm is referred to here as the "rule of disagreement". The
second paper builds on these findings and addresses the
question of how decision support systems can be disagreed
with in clinical contexts. Physicians who come to different
diagnostic conclusions than an AI they use as such a
decision support system face a double challenge: on the one
hand, the AI's diagnostic proposal is evidence that needs to
be taken into account; on the other hand, the decision for a
diagnosis is in the hands of physicians, and a rejection of
the evidence produced by the AI requires further,
alternative justifications that are not readily available.
To this end, we first distinguish the forms of conflict
between physicians and technology: malfunctions, mistakes,
and disagreements. Starting from the notion of Meaningful
Human Control (MHC) as a normative standard for assessing
the ethical permissibility of cooperative systems in
clinical processes, the notion of meaningful disagreement is
introduced and elaborated. In the third part, the notion of
explainability of AI-generated diagnoses is normatively
examined. To this end, the various goods that explainability
of AI diagnoses may entail are first described. Through the
conceptual distinction between "absolute" and "relative"
explainability, a normative distinction is then also
introduced to examine the standards against which the
explanations of an AI diagnosis are measured. Insofar as
such explanations are understood in comparison to human
standards ("relative") of diagnoses rather than absolute,
higher claims for AI diagnoses cannot be held. Last, it is
shown that the goods of explainability presented at the
beginning can be replaced without loss by recourse to
appropriate risk assessment through certification
("certifiability") and reference to the physician's
expertise in translating the AI-based diagnosis to the
patient’s understanding ("interpretability"). The fourth
paper continues the distinctions made in the third regarding
the relative and absolute explainability of AI diagnoses and
transfers them to the context of global standardizations of
AI systems and therefore additionally distinguishes between
local and global standards of explainability. High standards
of explainable diagnoses in countries with high medical
standards create a risk that AIs developed in these
countries may not be approved globally due to insufficient
standards of explainability; to avoid regions with a lack of
experts in explaining diagnoses being unable to use these
AIs, it is suggested that the decision to set standards
should be left to the communities themselves. The fifth part
examines the extent to which AI diagnostic tools should be
used as placebos. For this purpose, the debate about
placebos in clinical contexts is first examined and a
distinction is made between seven different forms of placebo
effect eliciting tools. An analysis of doctor-patient
relationships as an intimate relationship of trust and the
resulting "normative entanglement" establishes the
permissibility of placebos. The next step is to examine the
skill of AI applications as placebos. Since an AI cannot
replace the normative entanglement of doctors and patients,
its admissibility is examined solely in the context of
trusting doctor-patient treatments. In the sixth part, the
question is answered whether an AI should make judgments of
medical interventions as "medically necessary". To this end,
we first introduce the distinction between medical necessity
as a judgment about the instrumental necessity of an
intervention and as a judgment about the socially
justifiable minimum of medical services ("social medical
necessity"). By focusing on the first distinction, questions
of judgment are raised by AIs. Since these judgments always
include judgments about the goals of an intervention, which
are socially and morally agreed upon, a skeptical position
of AI as a judge of interventions as medically necessary is
taken. This is followed by a discussion of both the findings
of these papers and the prospect of further research
questions in this context. The approach of assessing the
uses and influences of AI according to the three levels
allows for a problem-oriented, pragmatic view of the use of
AI, but also allows for the setting of clear boundaries and
the introduction of sharp ethical distinctions. Through
these distinctions and the theses connected to them, further
issues also emerge, such as the question of democratizing
standards of explicability, global AI regulations and
growing inequalities in healthcare, new attributions of
responsibility in physician-AI collaborations, and changes
in clinical work and care environments.},
cin = {711120},
ddc = {100},
cid = {$I:(DE-82)711120_20180704$},
pnm = {BMBF 01GP1910A - Verbundprojekt - Teilprojekt Umfragen,
Interviews (BMBF-01GP1910A)},
pid = {G:(BMBF)BMBF-01GP1910A},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2023-05683},
url = {https://publications.rwth-aachen.de/record/959574},
}
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